44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
_base_ = [
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'../_base_/models/mvit/mvitv2-tiny.py',
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'../_base_/datasets/imagenet_bs64_swin_224.py',
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'../_base_/schedules/imagenet_bs2048_AdamW.py',
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'../_base_/default_runtime.py'
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]
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# dataset settings
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train_dataloader = dict(batch_size=256)
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val_dataloader = dict(batch_size=256)
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test_dataloader = dict(batch_size=256)
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# schedule settings
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optim_wrapper = dict(
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optimizer=dict(lr=2.5e-4),
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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=0.0),
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'.rel_pos_h': dict(decay_mult=0.0),
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'.rel_pos_w': dict(decay_mult=0.0)
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}),
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clip_grad=dict(max_norm=1.0),
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)
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# learning policy
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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-3,
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by_epoch=True,
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end=70,
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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(type='CosineAnnealingLR', eta_min=1e-5, by_epoch=True, begin=70)
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]
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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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auto_scale_lr = dict(base_batch_size=2048)
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