text: _base_ = [
      '../_base_/models/setr_naive_pup.py',
      '../_base_/datasets/cityscapes_768x768_multi_scale.py', '../_base_/default_runtime.py',
      '../_base_/schedules/schedule_40k.py'
  ]
  
  norm_cfg = dict(type='SyncBN', requires_grad=True)
  model = dict(
      backbone=dict(img_size=768,align_corners=False, pos_embed_interp=True,drop_rate=0.,
                    model_name='deit_base_distilled_path16_384', embed_dim=768, depth=12, num_heads=12),
      decode_head=dict(img_size=768,align_corners=False,num_conv=2,upsampling_method='bilinear',
                       embed_dim=768, in_index=11),
      auxiliary_head=[dict(
      type='VisionTransformerUpHead',
      in_channels=1024,
      channels=512,
      in_index=2,
      img_size=768,
      embed_dim=768,
      num_classes=19,
      norm_cfg=norm_cfg,
      num_conv=2,
      upsampling_method='bilinear',
      align_corners=False,
      loss_decode=dict(
          type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
      dict(
      type='VisionTransformerUpHead',
      in_channels=1024,
      channels=512,
      in_index=5,
      img_size=768,
      embed_dim=768,
      num_classes=19,
      norm_cfg=norm_cfg,
      num_conv=2,
      upsampling_method='bilinear',
      align_corners=False,
      loss_decode=dict(
          type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
      dict(
      type='VisionTransformerUpHead',
      in_channels=1024,
      channels=512,
      in_index=8,
      img_size=768,
      embed_dim=768,
      num_classes=19,
      norm_cfg=norm_cfg,
      num_conv=2,
      upsampling_method='bilinear',
      align_corners=False,
      loss_decode=dict(
          type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
      ])
  
  optimizer = dict(lr=0.01, weight_decay=0.0,
  paramwise_cfg = dict(custom_keys={'head': dict(lr_mult=10.)})
  )
  
  crop_size = (768, 768)
  test_cfg = dict(mode='slide', crop_size=crop_size, stride=(512, 512))
  find_unused_parameters = True
  data = dict(samples_per_gpu=1)
  