class_docstr: BoxInst mask head used in https://arxiv.org/abs/2012.02310.
  
  This head outputs the mask for BoxInst.
  
  Args:
      pairwise_size (dict): The size of neighborhood for each pixel.
          Defaults to 3.
      pairwise_dilation (int): The dilation of neighborhood for each pixel.
          Defaults to 2.
      warmup_iters (int): Warmup iterations for pair-wise loss.
          Defaults to 10000.
class_docstr_tok: ["BoxInst","mask","head","used","in","https",":","\/\/arxiv.org\/abs\/2012.02310",".","This","head","outputs","the","mask","for","BoxInst",".","Args",":","pairwise_size","(","dict",")",":","The","size","of","neighborhood","for","each","pixel",".","Defaults","to","3.","pairwise_dilation","(","int",")",":","The","dilation","of","neighborhood","for","each","pixel",".","Defaults","to","2.","warmup_iters","(","int",")",":","Warmup","iterations","for","pair-wise","loss",".","Defaults","to","10000","."]
code: def __init__(
      self,
      *arg,
      pairwise_size: int = 3,
      pairwise_dilation: int = 2,
      warmup_iters: int = 10000,
      **kwargs,
  ) -> None:
      self.pairwise_size = pairwise_size
      self.pairwise_dilation = pairwise_dilation
      self.warmup_iters = warmup_iters
      super().__init__(*arg, **kwargs)
  
file_path: mmdetection/mmdet/models/dense_heads/boxinst_head.py
imports: from typing import List
  import torch
  import torch.nn.functional
  from mmengine import MessageHub
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.utils import InstanceList
  from ..utils.misc import unfold_wo_center
  from .condinst_head import CondInstBboxHead, CondInstMaskHead
label_desc: Use image node_id 1 to create a new BoxInstMaskHead object from inherited base classes: CondInstMaskHead with example: obj = BoxInstMaskHead()