text: _base_ = [
      '../../configs/_base_/models/upernet_uniformer.py', 
      '../../configs/_base_/datasets/ade20k.py',
      '../../configs/_base_/default_runtime.py', 
      '../../configs/_base_/schedules/schedule_160k.py'
  ]
  model = dict(
      backbone=dict(
          type='UniFormer',
          embed_dim=[64, 128, 320, 512],
          layers=[5, 8, 20, 7],
          head_dim=64,
          drop_path_rate=0.4,
          windows=True,
          hybrid=False,
          window_size=32,
      ),
      decode_head=dict(
          in_channels=[64, 128, 320, 512],
          num_classes=150
      ),
      auxiliary_head=dict(
          in_channels=320,
          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)
                   
  data=dict(samples_per_gpu=2)