text: # model settings
  norm_cfg = dict(type='SyncBN', requires_grad=True)
  model = dict(
      type='EncoderDecoder',
      pretrained=None,
      backbone=dict(
          type='SwinUNetV2UNetTransformer',
          pretrain_img_size=512,
          embed_dim=96,
          depths=[2, 2, 6, 2],
          num_heads=[3, 6, 12, 24],
          num_classes=30,
          window_size=7,
          mlp_ratio=4.,
          qkv_bias=True,
          qk_scale=None,
          drop_rate=0.,
          attn_drop_rate=0.,
          drop_path_rate=0.3,
          ape=False,
          patch_norm=True,
          # out_indices=(0, 1, 2, 3),
          use_checkpoint=False),
      decode_head=dict(
          type='SwinUNetDV2',
          pretrain_img_size=512,
          embed_dim=96,
          depths=[2, 2, 6, 2],
          num_heads=[3, 6, 12, 24],
          in_channels=384,
          channels=512,
          num_classes=30,
          window_size=7,
          mlp_ratio=4.,
          qkv_bias=True,
          qk_scale=None,
          drop_rate=0.,
          attn_drop_rate=0.,
          drop_path_rate=0.3,
          ape=False,
          patch_norm=True,
          # out_indices=(0, 1, 2, 3),
          use_checkpoint=False),
      # auxiliary_head=dict(
      #     type='FCNHead',
      #     in_channels=384,
      #     in_index=2,
      #     channels=256,
      #     num_convs=1,
      #     concat_input=False,
      #     dropout_ratio=0.1,
      #     num_classes=30,
      #     norm_cfg=norm_cfg,
      #     align_corners=False,
      #     loss_decode=dict(
      #         type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
      # model training and testing settings
      train_cfg=dict(),
      test_cfg=dict(mode='whole'))
  