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# optimizer
# In ClassyVision, the lr is set to 0.003 for bs4096.
# In this implementation(bs2048), lr = 0.003 / 4096 * (32bs * 64gpus) = 0.0015
optim_wrapper = dict(
optimizer=dict(type='AdamW', lr=0.0015, weight_decay=0.3),
# specific to vit pretrain
paramwise_cfg=dict(custom_keys={
'.cls_token': dict(decay_mult=0.0),
'.pos_embed': dict(decay_mult=0.0)
}),
)
# learning policy
warmup_epochs = 15 # about 10000 iterations for ImageNet-1k
param_scheduler = [
# warm up learning rate scheduler
dict(
type='LinearLR',
start_factor=1e-3,
by_epoch=True,
end=warmup_epochs,
# update by iter
convert_to_iter_based=True),
# main learning rate scheduler
dict(
type='CosineAnnealingLR',
eta_min=1e-5,
by_epoch=True,
begin=warmup_epochs)
]
# train, val, test setting
train_cfg = dict(by_epoch=True, max_epochs=300, 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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