oneflow.optim.lr_scheduler.ChainedScheduler

class oneflow.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 belong 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 step == 0
>>> # lr = 0.081    if step == 1
>>> # lr = 0.729    if step == 2
>>> # lr = 0.6561   if step == 3
>>> # lr = 0.59049  if step >= 4
>>> scheduler1 = ConstantLR(self.opt, factor=0.1, total_iters=2)
>>> scheduler2 = ExponentialLR(self.opt, gamma=0.9)
>>> scheduler = ChainedScheduler([scheduler1, scheduler2])
>>> for _ in range(100):
>>>     train(...)
>>>     validate(...)
>>>     scheduler.step()

__init__(schedulers)

Initialize self. See help(type(self)) for accurate signature.

Methods

__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(value, /)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattribute__(name, /)	Return getattr(self, name).
__gt__(value, /)	Return self>value.
__hash__()	Return hash(self).
__init__(schedulers)	Initialize self.
__init_subclass__	This method is called when a class is subclassed.
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__ne__(value, /)	Return self!=value.
__new__(**kwargs)	Create and return a new object.
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
_generate_conf_for_graph(lr_conf)
_init_base_lrs()
get_last_lr()	Return last computed learning rate by current scheduler.
get_lr(base_lr, step)	Compute learning rate using chainable form of the scheduler
load_state_dict(state_dict)	Loads the schedulers state.
print_lr(group, lr)	Display the current learning rate.
state_dict()	Returns the state of the scheduler as a dict.
step()
update_lrs(lrs)