SequentialLR#

classtorch.optim.lr_scheduler.SequentialLR(optimizer,schedulers,milestones,last_epoch=-1)[source]#

Contains a list of schedulers expected to be called sequentially during the optimization process.

Specifically, the schedulers will be called according to the milestone points, which should provide exactintervals by which each scheduler should be called at a given epoch.

Parameters

• optimizer (Optimizer) – Wrapped optimizer.

• schedulers (list) – List of chained schedulers.

• milestones (list) – List of integers that reflects milestone points.

• last_epoch (int) – The index of last epoch. Default: -1.

Example

>>># Assuming optimizer uses lr = 0.05 for all groups>>># lr = 0.005     if epoch == 0>>># lr = 0.005     if epoch == 1>>># lr = 0.005     if epoch == 2>>># ...>>># lr = 0.05      if epoch == 20>>># lr = 0.045     if epoch == 21>>># lr = 0.0405    if epoch == 22>>>scheduler1=ConstantLR(optimizer,factor=0.1,total_iters=20)>>>scheduler2=ExponentialLR(optimizer,gamma=0.9)>>>scheduler=SequentialLR(...optimizer,...schedulers=[scheduler1,scheduler2],...milestones=[20],...)>>>forepochinrange(100):>>>train(...)>>>validate(...)>>>scheduler.step()

get_last_lr()[source]#

Return last computed learning rate by current scheduler.

Return type

list[float]

get_lr()[source]#

Compute learning rate using chainable form of the scheduler.

Return type

list[float]

load_state_dict(state_dict)[source]#

Load the scheduler’s state.

Parameters

state_dict (dict) – scheduler state. Should be an object returnedfrom a call tostate_dict().

recursive_undo(sched=None)[source]#

Recursively undo any step performed by the initialisation ofschedulers.

state_dict()[source]#

Return the state of the scheduler as adict.

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

Return type

dict[str,Any]

step()[source]#

Perform a step.