| import math | |
| import numpy as np | |
| from torch.optim import Optimizer, lr_scheduler | |
| class WarmUpLR(lr_scheduler._LRScheduler): | |
| """Warm Up learning rate strategy | |
| Before warmup_step, lr increases from 0 to initial learning rate gradually | |
| After warmup_step, lr decreases from initial learning rate according to the negative square root of "step" | |
| If warmup_step=0, skip the phase of warm up and step into decay phase directly | |
| """ | |
| def __init__(self, optimizer, warmup_step, last_epoch=-1): | |
| self.warmup_step = warmup_step | |
| super(WarmUpLR, self).__init__(optimizer, last_epoch=last_epoch) | |
| def get_lr(self): | |
| if self.last_epoch < self.warmup_step: | |
| return [base_lr * ((self.last_epoch + 1) / self.warmup_step) \ | |
| for base_lr in self.base_lrs] | |
| else: | |
| return [base_lr * math.pow(self.last_epoch + 1, -0.5) \ | |
| for base_lr in self.base_lrs] | |
| class CosineAnnealingWarmUpLR(lr_scheduler._LRScheduler): | |
| """Warm Up learning rate strategy | |
| Before warmup_step, lr increases from 0 to initial learning rate of "optimizer" gradually | |
| After warmup_step, lr decreases from initial learning rate according to cosine annealing | |
| If warmup_step=0, skip the phase of warm up and step into decay phase directly | |
| """ | |
| def __init__(self, optimizer, warmup_step, max_step, min_lr=0, last_epoch=-1): | |
| self.warmup_step = warmup_step | |
| self.max_step = max_step | |
| self.min_lr = min_lr | |
| super(CosineAnnealingWarmUpLR, self).__init__(optimizer, last_epoch=last_epoch) | |
| def get_lr(self): | |
| if self.last_epoch < self.warmup_step: | |
| return [base_lr * ((self.last_epoch + 1) / self.warmup_step) \ | |
| for base_lr in self.base_lrs] | |
| else: | |
| return [self.min_lr + 0.5 * (base_lr - self.min_lr) * \ | |
| (1 + np.cos(np.pi * (self.last_epoch - self.warmup_step) / (self.max_step - self.warmup_step))) \ | |
| for base_lr in self.base_lrs] | |
| def adjust_learning_rate(optimizer, iteration_count, lr, lr_decay): | |
| """Imitating the original implementation""" | |
| lr = lr / (1.0 + lr_decay * iteration_count) | |
| for param_group in optimizer.param_groups: | |
| param_group['lr'] = lr | |
| import torch.nn as nn | |
| class EarlyStopping(nn.Module): | |
| pass |