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mae_vit-base-p16_8xb512-amp-coslr-400e_in1k.py
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mae_vit-base-p16_8xb512-amp-coslr-400e_in1k.py
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_base_ = [
'../_base_/models/mae_vit-base-p16.py',
'../_base_/datasets/imagenet_bs512_mae.py',
'../_base_/default_runtime.py',
]
# optimizer wrapper
optim_wrapper = dict(
type='AmpOptimWrapper',
loss_scale='dynamic',
optimizer=dict(
type='AdamW',
lr=1.5e-4 * 4096 / 256,
betas=(0.9, 0.95),
weight_decay=0.05),
paramwise_cfg=dict(
custom_keys={
'ln': dict(decay_mult=0.0),
'bias': dict(decay_mult=0.0),
'pos_embed': dict(decay_mult=0.),
'mask_token': dict(decay_mult=0.),
'cls_token': dict(decay_mult=0.)
}))
# learning rate scheduler
param_scheduler = [
dict(
type='LinearLR',
start_factor=1e-4,
by_epoch=True,
begin=0,
end=40,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=360,
by_epoch=True,
begin=40,
end=400,
convert_to_iter_based=True)
]
# runtime settings
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=400)
default_hooks = dict(
# only keeps the latest 3 checkpoints
checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
randomness = dict(seed=0, diff_rank_seed=True)
# auto resume
resume = True
# NOTE: `auto_scale_lr` is for automatically scaling LR
# based on the actual training batch size.
auto_scale_lr = dict(base_batch_size=4096)