upernet_transnext_base_512x512_160k_ade20k_ss.py

_base_ = [
    '_base_/models/upernet_transnext.py',
    '_base_/datasets/ade20k.py',
    '_base_/default_runtime.py',
    '_base_/schedules/schedule_160k.py'
]

crop_size = (512, 512)
# optimizer
model = dict(
    backbone=dict(
        pretrained=None,
        type='transnext_base',
        pretrain_size=224,
        img_size=512,
        is_extrapolation=False,
    ),
    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
optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.05,
                 paramwise_cfg=dict(custom_keys={'query_embedding': dict(decay_mult=0.),
                                                 'relative_pos_bias_local': dict(decay_mult=0.),
                                                 'cpb': dict(decay_mult=0.),
                                                 'temperature': 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)