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| # The schedule is usually used by models trained on KITTI dataset | |
| # The learning rate set in the cyclic schedule is the initial learning rate | |
| # rather than the max learning rate. Since the target_ratio is (10, 1e-4), | |
| # the learning rate will change from 0.0018 to 0.018, than go to 0.0018*1e-4 | |
| lr = 0.0018 | |
| # The optimizer follows the setting in SECOND.Pytorch, but here we use | |
| # the offcial AdamW optimizer implemented by PyTorch. | |
| optimizer = dict(type='AdamW', lr=lr, betas=(0.95, 0.99), weight_decay=0.01) | |
| optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2)) | |
| # We use cyclic learning rate and momentum schedule following SECOND.Pytorch | |
| # https://github.com/traveller59/second.pytorch/blob/3aba19c9688274f75ebb5e576f65cfe54773c021/torchplus/train/learning_schedules_fastai.py#L69 # noqa | |
| # We implement them in mmcv, for more details, please refer to | |
| # https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/lr_updater.py#L327 # noqa | |
| # https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/momentum_updater.py#L130 # noqa | |
| lr_config = dict( | |
| policy='cyclic', | |
| target_ratio=(10, 1e-4), | |
| cyclic_times=1, | |
| step_ratio_up=0.4, | |
| ) | |
| momentum_config = dict( | |
| policy='cyclic', | |
| target_ratio=(0.85 / 0.95, 1), | |
| cyclic_times=1, | |
| step_ratio_up=0.4, | |
| ) | |
| # Although the max_epochs is 40, this schedule is usually used we | |
| # RepeatDataset with repeat ratio N, thus the actual max epoch | |
| # number could be Nx40 | |
| runner = dict(type='EpochBasedRunner', max_epochs=40) | |