57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
# encoding: utf-8
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"""
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@author: liaoxingyu
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@contact: sherlockliao01@gmail.com
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"""
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from bisect import bisect_right
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import torch
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# FIXME ideally this would be achieved with a CombinedLRScheduler,
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# separating MultiStepLR with WarmupLR
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# but the current LRScheduler design doesn't allow it
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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=500,
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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 = 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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