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60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
import math
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from torch.optim import lr_scheduler
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class WarmRestart(lr_scheduler.CosineAnnealingLR):
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"""This class implements Stochastic Gradient Descent with Warm Restarts(SGDR): https://arxiv.org/abs/1608.03983.
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Set the learning rate of each parameter group using a cosine annealing schedule, When last_epoch=-1, sets initial lr as lr.
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This can't support scheduler.step(epoch). please keep epoch=None.
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"""
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def __init__(self, optimizer, T_max=30, T_mult=1, eta_min=0, last_epoch=-1):
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"""implements SGDR
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Parameters:
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----------
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T_max : int
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Maximum number of epochs.
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T_mult : int
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Multiplicative factor of T_max.
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eta_min : int
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Minimum learning rate. Default: 0.
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last_epoch : int
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The index of last epoch. Default: -1.
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"""
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self.T_mult = T_mult
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super().__init__(optimizer, T_max, eta_min, last_epoch)
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def get_lr(self):
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if self.last_epoch == self.T_max:
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self.last_epoch = 0
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self.T_max *= self.T_mult
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return [self.eta_min + (base_lr - self.eta_min) * (1 + math.cos(math.pi * self.last_epoch / self.T_max)) / 2 for
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base_lr in self.base_lrs]
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class LinearDecay(lr_scheduler._LRScheduler):
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"""This class implements LinearDecay
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"""
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def __init__(self, optimizer, num_epochs, start_epoch=0, min_lr=0, last_epoch=-1):
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"""implements LinearDecay
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Parameters:
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----------
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"""
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self.num_epochs = num_epochs
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self.start_epoch = start_epoch
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self.min_lr = min_lr
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super().__init__(optimizer, last_epoch)
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def get_lr(self):
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if self.last_epoch < self.start_epoch:
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return self.base_lrs
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return [base_lr - ((base_lr - self.min_lr) / self.num_epochs) * (self.last_epoch - self.start_epoch) for
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base_lr in self.base_lrs]
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