50 lines
1.4 KiB
Python
50 lines
1.4 KiB
Python
_base_ = [
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'../_base_/models/maskfeat_vit-base-p16.py',
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'../_base_/datasets/imagenet_maskfeat.py',
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'../_base_/schedules/adamw_coslr-300e_in1k.py',
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'../_base_/default_runtime.py',
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]
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# optimizer wrapper
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optimizer = dict(lr=2e-4 * 8, betas=(0.9, 0.999), weight_decay=0.05)
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optim_wrapper = dict(
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type='AmpOptimWrapper',
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loss_scale='dynamic',
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optimizer=optimizer,
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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={
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# 'pos_embed': dict(decay_mult=0.),
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'mask_token': dict(decay_mult=0.),
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# 'cls_token': dict(decay_mult=0.)
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}),
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# commented 'pos_embed' and 'cls_token' to avoid loss stuck situation
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clip_grad=dict(max_norm=0.02))
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# learning rate scheduler
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param_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=30,
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convert_to_iter_based=True),
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dict(
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type='CosineAnnealingLR',
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T_max=270,
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by_epoch=True,
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begin=30,
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end=300,
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convert_to_iter_based=True)
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]
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# runtime settings
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# pre-train for 300 epochs
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train_cfg = dict(max_epochs=300)
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default_hooks = dict(
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logger=dict(type='LoggerHook', interval=100),
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# only keeps the latest 3 checkpoints
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checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3))
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