text: _base_ = [
      '../_base_/models/upernet_convnext.py', '../_base_/datasets/ade20k.py',
      '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
  ]
  crop_size = (512, 512)
  checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-small_3rdparty_32xb128-noema_in1k_20220301-303e75e3.pth'  # noqa
  model = dict(
      backbone=dict(
          type='mmcls.ConvNeXt',
          arch='small',
          out_indices=[0, 1, 2, 3],
          drop_path_rate=0.3,
          layer_scale_init_value=1.0,
          gap_before_final_norm=False,
          init_cfg=dict(
              type='Pretrained', checkpoint=checkpoint_file,
              prefix='backbone.')),
      decode_head=dict(
          in_channels=[96, 192, 384, 768],
          num_classes=150,
      ),
      auxiliary_head=dict(in_channels=384, num_classes=150),
      test_cfg=dict(mode='slide', crop_size=crop_size, stride=(341, 341)),
  )
  
  optimizer = dict(
      constructor='LearningRateDecayOptimizerConstructor',
      _delete_=True,
      type='AdamW',
      lr=0.0001,
      betas=(0.9, 0.999),
      weight_decay=0.05,
      paramwise_cfg={
          'decay_rate': 0.9,
          'decay_type': 'stage_wise',
          'num_layers': 12
      })
  
  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)
  # fp16 settings
  optimizer_config = dict(type='Fp16OptimizerHook', loss_scale='dynamic')
  # fp16 placeholder
  fp16 = dict()
  