22 lines
624 B
Python
22 lines
624 B
Python
# optimizer
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optim_wrapper = dict(
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optimizer=dict(
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type='SGD', lr=0.8, momentum=0.9, weight_decay=0.0001, nesterov=True))
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# learning policy
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param_scheduler = [
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dict(
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type='LinearLR', start_factor=0.25, by_epoch=False, begin=0, end=2500),
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dict(
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type='MultiStepLR', by_epoch=True, milestones=[30, 60, 90], gamma=0.1)
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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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