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_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=[3, 4, 8, 3],
head_dim=64,
drop_path_rate=0.25,
windows=False,
hybrid=False
),
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) |