class_docstr: MaskFormer fusion head which postprocesses results for panoptic
  segmentation, instance segmentation and semantic segmentation.
class_docstr_tok: ["MaskFormer","fusion","head","which","postprocesses","results","for","panoptic","segmentation",",","instance","segmentation","and","semantic","segmentation","."]
code: def __init__(
      self,
      num_things_classes: int = 80,
      num_stuff_classes: int = 53,
      test_cfg: OptConfigType = None,
      loss_panoptic: OptConfigType = None,
      init_cfg: OptMultiConfig = None,
      **kwargs,
  ):
      super().__init__(
          num_things_classes=num_things_classes,
          num_stuff_classes=num_stuff_classes,
          test_cfg=test_cfg,
          loss_panoptic=loss_panoptic,
          init_cfg=init_cfg,
          **kwargs,
      )
  
file_path: mmdetection/mmdet/models/seg_heads/panoptic_fusion_heads/maskformer_fusion_head.py
imports: from typing import List
  import torch
  import torch.nn.functional
  from mmengine.structures import InstanceData, PixelData
  from torch import Tensor
  from mmdet.evaluation.functional import INSTANCE_OFFSET
  from mmdet.registry import MODELS
  from mmdet.structures import SampleList
  from mmdet.structures.mask import mask2bbox
  from mmdet.utils import OptConfigType, OptMultiConfig
  from .base_panoptic_fusion_head import BasePanopticFusionHead
label_desc: Use image node_id 1 to create a new MaskFormerFusionHead object from inherited base classes: BasePanopticFusionHead with example: obj = MaskFormerFusionHead(num_things_classes, num_stuff_classes, test_cfg, loss_panoptic, init_cfg)