class_docstr: Computes one-to-one matching between predictions and ground truth.
  
  This class computes an assignment between the targets and the predictions
  based on the costs. The costs are weighted sum of some components.
  For DETR the costs are weighted sum of classification cost, regression L1
  cost and regression iou cost. The targets don't include the no_object, so
  generally there are more predictions than targets. After the one-to-one
  matching, the un-matched are treated as backgrounds. Thus each query
  prediction will be assigned with `0` or a positive integer indicating the
  ground truth index:
  
  - 0: negative sample, no assigned gt
  - positive integer: positive sample, index (1-based) of assigned gt
  
  Args:
      match_costs (:obj:`ConfigDict` or dict or             List[Union[:obj:`ConfigDict`, dict]]): Match cost configs.
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docstr: Computes one-to-one matching based on the weighted costs.
  
  This method assign each query prediction to a ground truth or
  background. The `assigned_gt_inds` with -1 means don't care,
  0 means negative sample, and positive number is the index (1-based)
  of assigned gt.
  The assignment is done in the following steps, the order matters.
  
  1. assign every prediction to -1
  2. compute the weighted costs
  3. do Hungarian matching on CPU based on the costs
  4. assign all to 0 (background) first, then for each matched pair
     between predictions and gts, treat this prediction as foreground
     and assign the corresponding gt index (plus 1) to it.
  
  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. It may includes ``masks``, with shape
          (n, h, w) or (n, l).
      gt_instances (:obj:`InstanceData`): Ground truth of instance
          annotations. It usually includes ``bboxes``, with shape (k, 4),
          ``labels``, with shape (k, ) and ``masks``, with shape
          (k, h, w) or (k, l).
      img_meta (dict): Image information.
  
  Returns:
      :obj:`AssignResult`: The assigned result.
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code: def assign(
      self,
      pred_instances: InstanceData,
      gt_instances: InstanceData,
      img_meta: Optional[dict] = None,
      **kwargs,
  ) -> AssignResult:
      """Computes one-to-one matching based on the weighted costs.
  
      This method assign each query prediction to a ground truth or
      background. The `assigned_gt_inds` with -1 means don't care,
      0 means negative sample, and positive number is the index (1-based)
      of assigned gt.
      The assignment is done in the following steps, the order matters.
  
      1. assign every prediction to -1
      2. compute the weighted costs
      3. do Hungarian matching on CPU based on the costs
      4. assign all to 0 (background) first, then for each matched pair
         between predictions and gts, treat this prediction as foreground
         and assign the corresponding gt index (plus 1) to it.
  
      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. It may includes ``masks``, with shape
              (n, h, w) or (n, l).
          gt_instances (:obj:`InstanceData`): Ground truth of instance
              annotations. It usually includes ``bboxes``, with shape (k, 4),
              ``labels``, with shape (k, ) and ``masks``, with shape
              (k, h, w) or (k, l).
          img_meta (dict): Image information.
  
      Returns:
          :obj:`AssignResult`: The assigned result.
      """
      assert isinstance(gt_instances.labels, Tensor)
      num_gts, num_preds = len(gt_instances), len(pred_instances)
      gt_labels = gt_instances.labels
      device = gt_labels.device
  
      # 1. assign -1 by default
      assigned_gt_inds = torch.full(
          (num_preds,), -1, dtype=torch.long, device=device
      )
      assigned_labels = torch.full(
          (num_preds,), -1, dtype=torch.long, device=device
      )
  
      if num_gts == 0 or num_preds == 0:
          # No ground truth or boxes, return empty assignment
          if num_gts == 0:
              # No ground truth, assign all to background
              assigned_gt_inds[:] = 0
          return AssignResult(
              num_gts=num_gts,
              gt_inds=assigned_gt_inds,
              max_overlaps=None,
              labels=assigned_labels,
          )
  
      # 2. compute weighted cost
      cost_list = []
      for match_cost in self.match_costs:
          cost = match_cost(
              pred_instances=pred_instances,
              gt_instances=gt_instances,
              img_meta=img_meta,
          )
          cost_list.append(cost)
      cost = torch.stack(cost_list).sum(dim=0)
  
      # 3. do Hungarian matching on CPU using linear_sum_assignment
      cost = cost.detach().cpu()
      if linear_sum_assignment is None:
          raise ImportError(
              'Please run "pip install scipy" '
              "to install scipy first."
          )
  
      matched_row_inds, matched_col_inds = linear_sum_assignment(cost)
      matched_row_inds = torch.from_numpy(matched_row_inds).to(device)
      matched_col_inds = torch.from_numpy(matched_col_inds).to(device)
  
      # 4. assign backgrounds and foregrounds
      # assign all indices to backgrounds first
      assigned_gt_inds[:] = 0
      # assign foregrounds based on matching results
      assigned_gt_inds[matched_row_inds] = matched_col_inds + 1
      assigned_labels[matched_row_inds] = gt_labels[matched_col_inds]
      return AssignResult(
          num_gts=num_gts,
          gt_inds=assigned_gt_inds,
          max_overlaps=None,
          labels=assigned_labels,
      )
  
file_path: mmdetection/mmdet/models/task_modules/assigners/hungarian_assigner.py
imports: from typing import List, Optional, Union
  import torch
  from mmengine import ConfigDict
  from mmengine.structures import InstanceData
  from scipy.optimize import linear_sum_assignment
  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 HungarianAssigner obj's underlying member method code with example usage: obj.assign(pred_instances, gt_instances, img_meta) and returns: AssignResult, AssignResult