62 lines
1.5 KiB
Python
62 lines
1.5 KiB
Python
|
_base_ = [
|
||
|
|
'../_base_/models/mae_vit-base-p16.py',
|
||
|
|
'../_base_/datasets/imagenet_bs512_mae.py',
|
||
|
|
'../_base_/default_runtime.py',
|
||
|
|
]
|
||
|
|
|
||
|
|
# model settings
|
||
|
|
model = dict(
|
||
|
|
backbone=dict(type='MAEViT', arch='l'),
|
||
|
|
neck=dict(type='MAEPretrainDecoder', embed_dim=1024))
|
||
|
|
|
||
|
|
# 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=0.0001,
|
||
|
|
by_epoch=True,
|
||
|
|
begin=0,
|
||
|
|
end=40,
|
||
|
|
convert_to_iter_based=True),
|
||
|
|
dict(
|
||
|
|
type='CosineAnnealingLR',
|
||
|
|
T_max=1560,
|
||
|
|
by_epoch=True,
|
||
|
|
begin=40,
|
||
|
|
end=1600,
|
||
|
|
convert_to_iter_based=True)
|
||
|
|
]
|
||
|
|
|
||
|
|
# runtime settings
|
||
|
|
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=1600)
|
||
|
|
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)
|