text: _base_ = [
      '../_base_/models/mrmd.py',
      '../_base_/datasets/cityscapes_768x768.py', '../_base_/default_runtime.py',
      '../_base_/schedules/schedule_40k.py'
  ]
  norm_cfg = dict(type='SyncBN', requires_grad=True)
  
  model = dict(
      auxiliary_head=[dict(
          type='VisionTransformerUpHead',
          in_channels=1024,
          channels=512,
          in_index=9,
          img_size=768,
          embed_dim=2048,
          num_classes=19,
          norm_cfg=norm_cfg,
          num_conv=2,
          upsampling_method='bilinear',
          align_corners=False,
          conv3x3_conv1x1=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=768,
          embed_dim=2048,
          num_classes=19,
          norm_cfg=norm_cfg,
          num_conv=2,
          upsampling_method='bilinear',
          align_corners=False,
          conv3x3_conv1x1=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=768,
          embed_dim=2048,
          num_classes=19,
          norm_cfg=norm_cfg,
          num_conv=2,
          upsampling_method='bilinear',
          align_corners=False,
          conv3x3_conv1x1=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.)})
                   )
  img_norm_cfg = dict(
      mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
  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)
  
  
  