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ChainedScheduler
class torch.optim.lr_scheduler.ChainedScheduler(schedulers)
[source]
Chains list of learning rate schedulers. It takes a list of chainable learning rate schedulers and performs consecutive step() functions belonging to them by just one call.
 Parameters

schedulers (list) – List of chained schedulers.
Example
>>> # Assuming optimizer uses lr = 1. for all groups >>> # lr = 0.09 if epoch == 0 >>> # lr = 0.081 if epoch == 1 >>> # lr = 0.729 if epoch == 2 >>> # lr = 0.6561 if epoch == 3 >>> # lr = 0.59049 if epoch >= 4 >>> scheduler1 = ConstantLR(self.opt, factor=0.1, total_iters=2) >>> scheduler2 = ExponentialLR(self.opt, gamma=0.9) >>> scheduler = ChainedScheduler([scheduler1, scheduler2]) >>> for epoch in range(100): >>> train(...) >>> validate(...) >>> scheduler.step()
get_last_lr()

Return last computed learning rate by current scheduler.
load_state_dict(state_dict)
[source]
Loads the schedulers state.
 Parameters

state_dict (dict) – scheduler state. Should be an object returned from a call to
state_dict()
.
print_lr(is_verbose, group, lr, epoch=None)

Display the current learning rate.
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https://pytorch.org/docs/2.1/generated/torch.optim.lr_scheduler.ChainedScheduler.html