| import torch | |
| from torch.optim import Optimizer | |
| from timm.scheduler import CosineLRScheduler | |
| # We need to subclass torch.optim.lr_scheduler._LRScheduler, or Pytorch-lightning will complain | |
| class TimmCosineLRScheduler(CosineLRScheduler, torch.optim.lr_scheduler._LRScheduler): | |
| """ Wrap timm.scheduler.CosineLRScheduler so we can call scheduler.step() without passing in epoch. | |
| It supports resuming as well. | |
| """ | |
| def __init__(self, *args, **kwargs): | |
| super().__init__(*args, **kwargs) | |
| self._last_epoch = -1 | |
| self.step(epoch=0) | |
| def step(self, epoch=None): | |
| if epoch is None: | |
| self._last_epoch += 1 | |
| else: | |
| self._last_epoch = epoch | |
| # We call either step or step_update, depending on whether we're using the scheduler every | |
| # epoch or every step. | |
| # Otherwise, lightning will always call step (i.e., meant for each epoch), and if we set | |
| # scheduler interval to "step", then the learning rate update will be wrong. | |
| if self.t_in_epochs: | |
| super().step(epoch=self._last_epoch) | |
| else: | |
| super().step_update(num_updates=self._last_epoch) | |