_base_ = [
    '../_base_/models/dpt_vit-b16.py', '../_base_/datasets/ade20k.py',
    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
crop_size = (512, 512)
data_preprocessor = dict(size=crop_size)
model = dict(data_preprocessor=data_preprocessor)
# AdamW optimizer, no weight decay for position embedding & layer norm
# in backbone

optim_wrapper = dict(
    _delete_=True,
    type='OptimWrapper',
    optimizer=dict(
        type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01),
    # 自定义参数
    paramwise_cfg=dict(
        custom_keys={
            'pos_embed': dict(decay_mult=0.), # 位置嵌入（positional embeddings）。decay_mult=0. 意味着对这些嵌入不应用权重衰减
            'cls_token': dict(decay_mult=0.), # 是在某些模型（如 Transformer 或 BERT）中，用于分类任务的特定 token
            'norm': dict(decay_mult=0.) # 对归一化层的参数也禁用了权重衰减
        }))

param_scheduler = [
    dict(
        type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
    dict(
        type='PolyLR',
        eta_min=0.0,
        power=1.0,
        begin=1500,
        end=160000,
        by_epoch=False,
    )
]

# By default, models are trained on 8 GPUs with 2 images per GPU
train_dataloader = dict(batch_size=2, num_workers=2)
val_dataloader = dict(batch_size=1, num_workers=4)
test_dataloader = val_dataloader
