mirror of https://github.com/JDAI-CV/fast-reid.git
75 lines
2.3 KiB
Python
75 lines
2.3 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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from typing import List
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import torch
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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: torch.optim.Optimizer,
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milestones: List[int],
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gamma: float = 0.1,
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warmup_factor: float = 0.001,
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warmup_iters: int = 1000,
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warmup_method: str = "linear",
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last_epoch: int = -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 {}", milestones
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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().__init__(optimizer, last_epoch)
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def get_lr(self) -> List[float]:
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warmup_factor = _get_warmup_factor_at_iter(
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self.warmup_method, self.last_epoch, self.warmup_iters, self.warmup_factor
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)
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return [
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base_lr * warmup_factor * 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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def _compute_values(self) -> List[float]:
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# The new interface
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return self.get_lr()
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def _get_warmup_factor_at_iter(
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method: str, iter: int, warmup_iters: int, warmup_factor: float
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) -> float:
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"""
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Return the learning rate warmup factor at a specific iteration.
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See https://arxiv.org/abs/1706.02677 for more details.
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Args:
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method (str): warmup method; either "constant" or "linear".
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iter (int): iteration at which to calculate the warmup factor.
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warmup_iters (int): the number of warmup iterations.
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warmup_factor (float): the base warmup factor (the meaning changes according
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to the method used).
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Returns:
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float: the effective warmup factor at the given iteration.
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"""
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if iter >= warmup_iters:
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return 1.0
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if method == "constant":
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return warmup_factor
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elif method == "linear":
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alpha = iter / warmup_iters
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return warmup_factor * (1 - alpha) + alpha
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else:
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raise ValueError("Unknown warmup method: {}".format(method))
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