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_base_ = [
'../_base_/models/upernet_beit.py', '../_base_/datasets/ade20k_640x640.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
crop_size = (640, 640)
data_preprocessor = dict(size=crop_size)
model = dict(
data_preprocessor=data_preprocessor,
pretrained='pretrain/beit_base_patch16_224_pt22k_ft22k.pth',
test_cfg=dict(mode='slide', crop_size=(640, 640), stride=(426, 426)))
optim_wrapper = dict(
_delete_=True,
type='OptimWrapper',
optimizer=dict(
type='AdamW', lr=3e-5, betas=(0.9, 0.999), weight_decay=0.05),
constructor='LayerDecayOptimizerConstructor',
paramwise_cfg=dict(num_layers=12, layer_decay_rate=0.9))
param_scheduler = [
dict(
type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
dict(
type='PolyLR',
power=1.0,
begin=1500,
end=160000,
eta_min=0.0,
by_epoch=False,
)
]
# By default, models are trained on 8 GPUs with 2 images per GPU
train_dataloader = dict(batch_size=2)
val_dataloader = dict(batch_size=1)
test_dataloader = val_dataloader
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