33 lines
800 B
Python
33 lines
800 B
Python
# optimizer
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optim_wrapper = dict(optimizer=dict(type='Lamb', lr=0.005, weight_decay=0.02))
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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=0.0001,
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by_epoch=True,
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begin=0,
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end=5,
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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=95,
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eta_min=1.0e-6,
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by_epoch=True,
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begin=5,
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end=100)
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]
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# train, val, test setting
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train_cfg = dict(by_epoch=True, max_epochs=100, val_interval=1)
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val_cfg = dict()
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test_cfg = dict()
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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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