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# optimizer
optim_wrapper = dict(optimizer=dict(type='Lamb', lr=0.005, weight_decay=0.02))
# learning policy
param_scheduler = [
# warm up learning rate scheduler
dict(
type='LinearLR',
start_factor=0.0001,
by_epoch=True,
begin=0,
end=5,
# update by iter
convert_to_iter_based=True),
# main learning rate scheduler
dict(
type='CosineAnnealingLR',
T_max=95,
eta_min=1.0e-6,
by_epoch=True,
begin=5,
end=100)
]
# train, val, test setting
train_cfg = dict(by_epoch=True, max_epochs=100, val_interval=1)
val_cfg = dict()
test_cfg = dict()
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# based on the actual training batch size.
auto_scale_lr = dict(base_batch_size=2048)
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