50 lines
1.3 KiB
Python
50 lines
1.3 KiB
Python
_base_ = [
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'../_base_/models/t2t-vit-t-24.py',
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'../_base_/datasets/imagenet_bs64_t2t_224.py',
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'../_base_/default_runtime.py',
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]
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# schedule settings
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optim_wrapper = dict(
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optimizer=dict(type='AdamW', lr=5e-4, weight_decay=0.065),
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paramwise_cfg=dict(
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norm_decay_mult=0.0,
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bias_decay_mult=0.0,
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custom_keys={'cls_token': dict(decay_mult=0.0)},
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),
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)
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param_scheduler = [
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# warm up learning rate scheduler
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dict(
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type='LinearLR',
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start_factor=1e-6,
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by_epoch=True,
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begin=0,
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end=10,
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# update by iter
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convert_to_iter_based=True),
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# main learning rate scheduler
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dict(
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type='CosineAnnealingLR',
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T_max=290,
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eta_min=1e-5,
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by_epoch=True,
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begin=10,
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end=300),
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# cool down learning rate scheduler
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dict(type='ConstantLR', factor=0.1, by_epoch=True, begin=300, end=310),
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]
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train_cfg = dict(by_epoch=True, max_epochs=310, val_interval=1)
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val_cfg = dict()
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test_cfg = dict()
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# runtime settings
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custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')]
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# NOTE: `auto_scale_lr` is for automatically scaling LR
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# based on the actual training batch size.
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# base_batch_size = (8 GPUs) x (64 samples per GPU)
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auto_scale_lr = dict(base_batch_size=512)
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