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"Supports 1-Cycle style training"
from ..core import *
from ..callback import *
from ..basic_train import Learner,LearnerCallback
__all__ = ['OneCycleScheduler']
class OneCycleScheduler(LearnerCallback):
"Manage 1-Cycle style training as outlined in Leslie Smith's [paper](https://arxiv.org/pdf/1803.09820.pdf)."
def __init__(self, learn:Learner, lr_max:float, moms:Floats=(0.95,0.85), div_factor:float=25., pct_start:float=0.3,
final_div:float=None, tot_epochs:int=None, start_epoch:int=None):
super().__init__(learn)
self.lr_max,self.div_factor,self.pct_start,self.final_div = lr_max,div_factor,pct_start,final_div
if self.final_div is None: self.final_div = div_factor*1e4
self.moms=tuple(listify(moms,2))
if is_listy(self.lr_max): self.lr_max = np.array(self.lr_max)
self.start_epoch, self.tot_epochs = start_epoch, tot_epochs
def steps(self, *steps_cfg:StartOptEnd):
"Build anneal schedule for all of the parameters."
return [Scheduler(step, n_iter, func=func)
for (step,(n_iter,func)) in zip(steps_cfg, self.phases)]
def on_train_begin(self, n_epochs:int, epoch:int, **kwargs:Any)->None:
"Initialize our optimization params based on our annealing schedule."
res = {'epoch':self.start_epoch} if self.start_epoch is not None else None
self.start_epoch = ifnone(self.start_epoch, epoch)
self.tot_epochs = ifnone(self.tot_epochs, n_epochs)
n = len(self.learn.data.train_dl) * self.tot_epochs
a1 = int(n * self.pct_start)
a2 = n-a1
self.phases = ((a1, annealing_cos), (a2, annealing_cos))
low_lr = self.lr_max/self.div_factor
self.lr_scheds = self.steps((low_lr, self.lr_max), (self.lr_max, self.lr_max/self.final_div))
self.mom_scheds = self.steps(self.moms, (self.moms[1], self.moms[0]))
self.opt = self.learn.opt
self.opt.lr,self.opt.mom = self.lr_scheds[0].start,self.mom_scheds[0].start
self.idx_s = 0
return res
def jump_to_epoch(self, epoch:int)->None:
for _ in range(len(self.learn.data.train_dl) * epoch):
self.on_batch_end(True)
def on_batch_end(self, train, **kwargs:Any)->None:
"Take one step forward on the annealing schedule for the optim params."
if train:
if self.idx_s >= len(self.lr_scheds): return {'stop_training': True, 'stop_epoch': True}
self.opt.lr = self.lr_scheds[self.idx_s].step()
self.opt.mom = self.mom_scheds[self.idx_s].step()
# when the current schedule is complete we move onto the next
# schedule. (in 1-cycle there are two schedules)
if self.lr_scheds[self.idx_s].is_done:
self.idx_s += 1
def on_epoch_end(self, epoch, **kwargs:Any)->None:
"Tell Learner to stop if the cycle is finished."
if epoch > self.tot_epochs: return {'stop_training': True}
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