class_docstr: Computes matching between predictions and ground truth.
  
  Args:
      center_radius (float): Ground truth center size
          to judge whether a prior is in center. Defaults to 2.5.
      candidate_topk (int): The candidate top-k which used to
          get top-k ious to calculate dynamic-k. Defaults to 10.
      iou_weight (float): The scale factor for regression
          iou cost. Defaults to 3.0.
      cls_weight (float): The scale factor for classification
          cost. Defaults to 1.0.
      iou_calculator (ConfigType): Config of overlaps Calculator.
          Defaults to dict(type='BboxOverlaps2D').
class_docstr_tok: ["Computes","matching","between","predictions","and","ground","truth",".","Args",":","center_radius","(","float",")",":","Ground","truth","center","size","to","judge","whether","a","prior","is","in","center",".","Defaults","to","2.5.","candidate_topk","(","int",")",":","The","candidate","top-k","which","used","to","get","top-k","ious","to","calculate","dynamic-k.","Defaults","to","10.","iou_weight","(","float",")",":","The","scale","factor","for","regression","iou","cost",".","Defaults","to","3.0.","cls_weight","(","float",")",":","The","scale","factor","for","classification","cost",".","Defaults","to","1.0.","iou_calculator","(","ConfigType",")",":","Config","of","overlaps","Calculator",".","Defaults","to","dict","(","type='BboxOverlaps2D","'",")","."]
code: def __init__(
      self,
      center_radius: float = 2.5,
      candidate_topk: int = 10,
      iou_weight: float = 3.0,
      cls_weight: float = 1.0,
      iou_calculator: ConfigType = dict(type="BboxOverlaps2D"),
  ):
      self.center_radius = center_radius
      self.candidate_topk = candidate_topk
      self.iou_weight = iou_weight
      self.cls_weight = cls_weight
      self.iou_calculator = TASK_UTILS.build(iou_calculator)
  
file_path: mmdetection/mmdet/models/task_modules/assigners/sim_ota_assigner.py
imports: from typing import Optional, Tuple
  import torch
  import torch.nn.functional
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import TASK_UTILS
  from mmdet.utils import ConfigType
  from .assign_result import AssignResult
  from .base_assigner import BaseAssigner
label_desc: Use image node_id 1 to create a new SimOTAAssigner object from inherited base classes: BaseAssigner with example: obj = SimOTAAssigner(center_radius, candidate_topk, iou_weight, cls_weight, iou_calculator)