| 123456789101112131415161718192021222324252627282930313233 |
- # Copyright (c) OpenMMLab. All rights reserved.
- from mmengine.optim.optimizer.optimizer_wrapper import OptimWrapper
- from mmengine.optim.scheduler.lr_scheduler import LinearLR, MultiStepLR
- from mmengine.runner.loops import EpochBasedTrainLoop, TestLoop, ValLoop
- from torch.optim.sgd import SGD
- # training schedule for 1x
- train_cfg = dict(type=EpochBasedTrainLoop, max_epochs=12, val_interval=1)
- val_cfg = dict(type=ValLoop)
- test_cfg = dict(type=TestLoop)
- # learning rate
- param_scheduler = [
- dict(type=LinearLR, start_factor=0.001, by_epoch=False, begin=0, end=500),
- dict(
- type=MultiStepLR,
- begin=0,
- end=12,
- by_epoch=True,
- milestones=[8, 11],
- gamma=0.1)
- ]
- # optimizer
- optim_wrapper = dict(
- type=OptimWrapper,
- optimizer=dict(type=SGD, lr=0.02, momentum=0.9, weight_decay=0.0001))
- # Default setting for scaling LR automatically
- # - `enable` means enable scaling LR automatically
- # or not by default.
- # - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
- auto_scale_lr = dict(enable=False, base_batch_size=16)
|
