100 lines
3.6 KiB
Python
100 lines
3.6 KiB
Python
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'''
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@ Date: 2021-09-05 20:07:55
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@ Author: Qing Shuai
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@ LastEditors: Qing Shuai
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@ LastEditTime: 2021-09-05 20:10:02
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@ FilePath: /EasyMocap/easymocap/neuralbody/trainer/lr_sheduler.py
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'''
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import torch
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from collections import Counter
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from bisect import bisect_right
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class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler):
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def __init__(
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self,
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optimizer,
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milestones,
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gamma=0.1,
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warmup_factor=1.0 / 3,
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warmup_iters=5,
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warmup_method="linear",
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last_epoch=-1,
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):
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if not list(milestones) == sorted(milestones):
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raise ValueError(
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"Milestones should be a list of" " increasing integers. Got {}",
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milestones,
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)
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if warmup_method not in ("constant", "linear"):
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raise ValueError(
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"Only 'constant' or 'linear' warmup_method accepted"
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"got {}".format(warmup_method)
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)
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self.milestones = milestones
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self.gamma = gamma
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self.warmup_factor = warmup_factor
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self.warmup_iters = warmup_iters
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self.warmup_method = warmup_method
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super(WarmupMultiStepLR, self).__init__(optimizer, last_epoch)
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def get_lr(self):
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warmup_factor = 1
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if self.last_epoch < self.warmup_iters:
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if self.warmup_method == "constant":
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warmup_factor = self.warmup_factor
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elif self.warmup_method == "linear":
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alpha = float(self.last_epoch) / self.warmup_iters
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warmup_factor = self.warmup_factor * (1 - alpha) + alpha
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return [
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base_lr
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* warmup_factor
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* self.gamma ** bisect_right(self.milestones, self.last_epoch)
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for base_lr in self.base_lrs
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]
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class MultiStepLR(torch.optim.lr_scheduler._LRScheduler):
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def __init__(self, optimizer, milestones, gamma=0.1, last_epoch=-1):
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self.milestones = Counter(milestones)
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self.gamma = gamma
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super(MultiStepLR, self).__init__(optimizer, last_epoch)
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def get_lr(self):
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if self.last_epoch not in self.milestones:
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return [group['lr'] for group in self.optimizer.param_groups]
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return [group['lr'] * self.gamma ** self.milestones[self.last_epoch]
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for group in self.optimizer.param_groups]
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class ExponentialLR(torch.optim.lr_scheduler._LRScheduler):
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def __init__(self, optimizer, decay_epochs, gamma=0.1, last_epoch=-1):
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self.decay_epochs = decay_epochs
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self.gamma = gamma
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super(ExponentialLR, self).__init__(optimizer, last_epoch)
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def get_lr(self):
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return [base_lr * self.gamma ** (self.last_epoch / self.decay_epochs)
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for base_lr in self.base_lrs]
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def Scheduler(cfg_scheduler, optimizer):
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if cfg_scheduler.type == 'multi_step':
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scheduler = MultiStepLR(optimizer,
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milestones=cfg_scheduler.milestones,
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gamma=cfg_scheduler.gamma)
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elif cfg_scheduler.type == 'exponential':
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scheduler = ExponentialLR(optimizer,
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decay_epochs=cfg_scheduler.decay_epochs,
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gamma=cfg_scheduler.gamma)
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else:
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raise NotImplementedError
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return scheduler
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def set_lr_scheduler(cfg_scheduler, scheduler):
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if cfg_scheduler.type == 'multi_step':
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scheduler.milestones = Counter(cfg_scheduler.milestones)
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elif cfg_scheduler.type == 'exponential':
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scheduler.decay_epochs = cfg_scheduler.decay_epochs
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scheduler.gamma = cfg_scheduler.gamma
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