text: _base_ = ['./mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py']
  pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth'  # noqa
  
  depths = [2, 2, 18, 2]
  model = dict(
      backbone=dict(
          pretrain_img_size=384,
          embed_dims=128,
          depths=depths,
          num_heads=[4, 8, 16, 32],
          window_size=12,
          init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
      panoptic_head=dict(in_channels=[128, 256, 512, 1024]))
  
  # set all layers in backbone to lr_mult=0.1
  # set all norm layers, position_embeding,
  # query_embeding, level_embeding to decay_multi=0.0
  backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0)
  backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0)
  embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
  custom_keys = {
      'backbone': dict(lr_mult=0.1, decay_mult=1.0),
      'backbone.patch_embed.norm': backbone_norm_multi,
      'backbone.norm': backbone_norm_multi,
      'absolute_pos_embed': backbone_embed_multi,
      'relative_position_bias_table': backbone_embed_multi,
      'query_embed': embed_multi,
      'query_feat': embed_multi,
      'level_embed': embed_multi
  }
  custom_keys.update({
      f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi
      for stage_id, num_blocks in enumerate(depths)
      for block_id in range(num_blocks)
  })
  custom_keys.update({
      f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi
      for stage_id in range(len(depths) - 1)
  })
  # optimizer
  optimizer = dict(
      paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0))
  