ChainedScheduler — PyTorch 2.7 documentation (original) (raw)
class torch.optim.lr_scheduler.ChainedScheduler(schedulers, optimizer=None)[source][source]¶
Chains a list of learning rate schedulers.
Takes in a sequence of chainable learning rate schedulers and calls their step() functions consecutively in just one call to step().
Parameters
- schedulers (sequence) – sequence of chained schedulers.
- optimizer (Optimizer, optional) – Wrapped optimizer. Default: None.
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(optimizer, factor=0.1, total_iters=2) scheduler2 = ExponentialLR(optimizer, gamma=0.9) scheduler = ChainedScheduler([scheduler1, scheduler2], optimizer=optimizer) for epoch in range(100): train(...) validate(...) scheduler.step()
Return last computed learning rate by current scheduler.
Return type
Compute learning rate using chainable form of the scheduler.
Return type
load_state_dict(state_dict)[source][source]¶
Load the scheduler’s state.
Parameters
state_dict (dict) – scheduler state. Should be an object returned from a call to state_dict().
Return 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.
Perform a step.