text: _base_ = [
      '../_base_/models/swin_unet_gtv2.py', '../_base_/datasets/ade20k.py',
      '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
  ]
  model = dict(
      backbone=dict(
          embed_dim=96,
          depths=[2, 2, 6, 2],
          num_heads=[3, 6, 12, 24],
          window_size=7,
          ape=False,
          drop_path_rate=0.3,
          patch_norm=True,
          use_checkpoint=False,
          num_classes=150, 
          gt_num=1
      ),
      decode_head=dict(
          embed_dim=96,
          depths=[2, 2, 6, 2],
          num_heads=[3, 6, 12, 24],
          window_size=7,
          ape=False,
          drop_path_rate=0.3,
          patch_norm=True,
          use_checkpoint=False,
          num_classes=150, 
          gt_num=1
      )
      #,
      # auxiliary_head=dict(
      #     in_channels=512,
      #     num_classes=150
      # )
      )
  
  # AdamW optimizer, no weight decay for position embedding & layer norm in backbone
  optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01,
                   paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
                                                   'relative_position_bias_table': dict(decay_mult=0.),
                                                   'norm': dict(decay_mult=0.)}))
  
  lr_config = dict(_delete_=True, policy='poly',
                   warmup='linear',
                   warmup_iters=1500,
                   warmup_ratio=1e-6,
                   power=1.0, min_lr=0.0, by_epoch=False)
  
  # By default, models are trained on 8 GPUs with 2 images per GPU
  data=dict(samples_per_gpu=2)
  
  
  
  