ChainedScheduler

class torch.optim.lr_scheduler.ChainedScheduler(schedulers)

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)

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.

state_dict()

Returns the state of the scheduler as a dict.

It contains an entry for every variable in self.__dict__ which is not the optimizer. The wrapped scheduler states will also be saved.