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""" |
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@Author : Peike Li |
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@Contact : [email protected] |
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@File : warmup_scheduler.py |
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@Time : 3/28/19 2:24 PM |
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@Desc : |
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@License : This source code is licensed under the license found in the |
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LICENSE file in the root directory of this source tree. |
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""" |
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import math |
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from torch.optim.lr_scheduler import _LRScheduler |
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class GradualWarmupScheduler(_LRScheduler): |
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""" Gradually warm-up learning rate with cosine annealing in optimizer. |
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Proposed in 'Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour'. |
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""" |
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def __init__(self, optimizer, total_epoch, eta_min=0, warmup_epoch=10, last_epoch=-1): |
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self.total_epoch = total_epoch |
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self.eta_min = eta_min |
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self.warmup_epoch = warmup_epoch |
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super(GradualWarmupScheduler, self).__init__(optimizer, last_epoch) |
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def get_lr(self): |
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if self.last_epoch <= self.warmup_epoch: |
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return [self.eta_min + self.last_epoch*(base_lr - self.eta_min)/self.warmup_epoch for base_lr in self.base_lrs] |
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else: |
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return [self.eta_min + (base_lr-self.eta_min)*(1+math.cos(math.pi*(self.last_epoch-self.warmup_epoch)/(self.total_epoch-self.warmup_epoch))) / 2 for base_lr in self.base_lrs] |
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class SGDRScheduler(_LRScheduler): |
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""" Consine annealing with warm up and restarts. |
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Proposed in `SGDR: Stochastic Gradient Descent with Warm Restarts`. |
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""" |
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def __init__(self, optimizer, total_epoch=150, start_cyclical=100, cyclical_base_lr=7e-4, cyclical_epoch=10, eta_min=0, warmup_epoch=10, last_epoch=-1): |
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self.total_epoch = total_epoch |
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self.start_cyclical = start_cyclical |
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self.cyclical_epoch = cyclical_epoch |
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self.cyclical_base_lr = cyclical_base_lr |
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self.eta_min = eta_min |
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self.warmup_epoch = warmup_epoch |
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super(SGDRScheduler, self).__init__(optimizer, last_epoch) |
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def get_lr(self): |
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if self.last_epoch < self.warmup_epoch: |
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return [self.eta_min + self.last_epoch*(base_lr - self.eta_min)/self.warmup_epoch for base_lr in self.base_lrs] |
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elif self.last_epoch < self.start_cyclical: |
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return [self.eta_min + (base_lr-self.eta_min)*(1+math.cos(math.pi*(self.last_epoch-self.warmup_epoch)/(self.start_cyclical-self.warmup_epoch))) / 2 for base_lr in self.base_lrs] |
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else: |
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return [self.eta_min + (self.cyclical_base_lr-self.eta_min)*(1+math.cos(math.pi* ((self.last_epoch-self.start_cyclical)% self.cyclical_epoch)/self.cyclical_epoch)) / 2 for base_lr in self.base_lrs] |
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if __name__ == '__main__': |
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import matplotlib.pyplot as plt |
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import torch |
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model = torch.nn.Linear(10, 2) |
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optimizer = torch.optim.SGD(params=model.parameters(), lr=7e-3, momentum=0.9, weight_decay=5e-4) |
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scheduler_warmup = SGDRScheduler(optimizer, total_epoch=150, eta_min=7e-5, warmup_epoch=10, start_cyclical=100, cyclical_base_lr=3.5e-3, cyclical_epoch=10) |
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lr = [] |
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for epoch in range(0,150): |
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scheduler_warmup.step(epoch) |
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lr.append(scheduler_warmup.get_lr()) |
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plt.style.use('ggplot') |
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plt.plot(list(range(0,150)), lr) |
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plt.show() |
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