mirror of
https://github.com/open-mmlab/mmdeploy.git
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69 lines
2.3 KiB
Python
69 lines
2.3 KiB
Python
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# Copyright (c) OpenMMLab. All rights reserved.
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# The schedule is usually used by models trained on KITTI dataset
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# The learning rate set in the cyclic schedule is the initial learning rate
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# rather than the max learning rate. Since the target_ratio is (10, 1e-4),
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# the learning rate will change from 0.0018 to 0.018, than go to 0.0018*1e-4
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lr = 0.0018
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# The optimizer follows the setting in SECOND.Pytorch, but here we use
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# the official AdamW optimizer implemented by PyTorch.
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optim_wrapper = dict(
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type='OptimWrapper',
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optimizer=dict(type='AdamW', lr=lr, betas=(0.95, 0.99), weight_decay=0.01),
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clip_grad=dict(max_norm=10, norm_type=2))
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# learning rate
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param_scheduler = [
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# learning rate scheduler
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# During the first 16 epochs, learning rate increases from 0 to lr * 10
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# during the next 24 epochs, learning rate decreases from lr * 10 to
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# lr * 1e-4
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dict(
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type='CosineAnnealingLR',
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T_max=16,
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eta_min=lr * 10,
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begin=0,
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end=16,
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by_epoch=True,
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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=24,
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eta_min=lr * 1e-4,
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begin=16,
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end=40,
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by_epoch=True,
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convert_to_iter_based=True),
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# momentum scheduler
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# During the first 16 epochs, momentum increases from 0 to 0.85 / 0.95
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# during the next 24 epochs, momentum increases from 0.85 / 0.95 to 1
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dict(
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type='CosineAnnealingMomentum',
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T_max=16,
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eta_min=0.85 / 0.95,
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begin=0,
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end=16,
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by_epoch=True,
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convert_to_iter_based=True),
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dict(
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type='CosineAnnealingMomentum',
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T_max=24,
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eta_min=1,
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begin=16,
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end=40,
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by_epoch=True,
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convert_to_iter_based=True)
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]
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# Runtime settings，training schedule for 40e
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# Although the max_epochs is 40, this schedule is usually used we
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# RepeatDataset with repeat ratio N, thus the actual max epoch
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# number could be Nx40
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train_cfg = dict(by_epoch=True, max_epochs=40, val_interval=1)
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val_cfg = dict()
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test_cfg = dict()
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# Default setting for scaling LR automatically
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# - `enable` means enable scaling LR automatically
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# or not by default.
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# - `base_batch_size` = (8 GPUs) x (6 samples per GPU).
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auto_scale_lr = dict(enable=False, base_batch_size=48)
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