SequentialLR(optimizer, schedulers, milestones, last_epoch=- 1, verbose=False)¶
Receives the list of schedulers that is expected to be called sequentially during optimization process and milestone points that provides exact intervals to reflect which scheduler is supposed to be called at a given epoch.
>>> # Assuming optimizer uses lr = 1. for all groups >>> # lr = 0.1 if epoch == 0 >>> # lr = 0.1 if epoch == 1 >>> # lr = 0.9 if epoch == 2 >>> # lr = 0.81 if epoch == 3 >>> # lr = 0.729 if epoch == 4 >>> scheduler1 = ConstantLR(self.opt, factor=0.1, total_iters=2) >>> scheduler2 = ExponentialLR(self.opt, gamma=0.9) >>> scheduler = SequentialLR(self.opt, schedulers=[scheduler1, scheduler2], milestones=) >>> for epoch in range(100): >>> train(...) >>> validate(...) >>> scheduler.step()
Return last computed learning rate by current scheduler.
Loads the schedulers state.
print_lr(is_verbose, group, lr, epoch=None)¶
Display the current learning rate.