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