text: _base_ = [
      '../_base_/models/setr_naive_pup.py',
      '../_base_/datasets/pascal_context.py', '../_base_/default_runtime.py',
      '../_base_/schedules/schedule_80k.py'
  ]
  norm_cfg = dict(type='BN', requires_grad=True)
  model = dict(
      backbone=dict(img_size=256, align_corners=False,
                    pos_embed_interp=True, drop_rate=0., norm_cfg=norm_cfg),
      decode_head=dict(img_size=256, align_corners=False, num_conv=4, upsampling_method='bilinear',
                       num_upsampe_layer=4, norm_cfg=norm_cfg, num_classes=3),
      auxiliary_head=[dict(
          type='VisionTransformerUpHead',
          in_channels=1024,
          channels=512,
          in_index=9,
          img_size=256,
          embed_dim=1024,
          num_classes=3,
          norm_cfg=norm_cfg,
          num_conv=2,
          upsampling_method='bilinear',
          num_upsampe_layer=2,
          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=14,
          img_size=256,
          embed_dim=1024,
          num_classes=3,
          norm_cfg=norm_cfg,
          num_conv=2,
          upsampling_method='bilinear',
          num_upsampe_layer=2,
          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=19,
          img_size=256,
          embed_dim=1024,
          num_classes=3,
          norm_cfg=norm_cfg,
          num_conv=2,
          upsampling_method='bilinear',
          num_upsampe_layer=2,
          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=23,
          img_size=256,
          embed_dim=1024,
          num_classes=3,
          norm_cfg=norm_cfg,
          num_conv=2,
          upsampling_method='bilinear',
          num_upsampe_layer=2,
          align_corners=False,
          loss_decode=dict(
              type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4))
      ])
  
  optimizer = dict(lr=0.001, weight_decay=0.0,
                   paramwise_cfg=dict(custom_keys={'head': dict(lr_mult=10.)})
                   )
  
  crop_size = (288, 288)
  test_cfg = dict(mode='slide', crop_size=crop_size, stride=(288, 288))
  find_unused_parameters = True
  data = dict(samples_per_gpu=2)
  