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Source code for mmflow.core.hooks.multistagelr_updater

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Sequence

from mmcv.runner import HOOKS, IterBasedRunner, LrUpdaterHook


[docs]@HOOKS.register_module() class MultiStageLrUpdaterHook(LrUpdaterHook): """Multi-Stage Learning Rate Hook. Args: milestone_lrs (Sequence[float]): The base LR for multi-stages. milestone_iters (Sequence[int]): The first iterations in different stages. steps (Sequence[Sequence[int]]): The steps to decay the LR in stages. gammas (Sequence[float]): The list of decay LR ratios. kwargs (any): The arguments of LrUpdaterHook. """ def __init__(self, milestone_lrs: Sequence[float], milestone_iters: Sequence[int], steps: Sequence[Sequence[int]], gammas: Sequence[float], **kwargs: Any) -> None: assert len(milestone_lrs) == len(milestone_iters) == len(steps) == len( gammas ), ('For MultiStageLr, lengths of milestones_lr and steps and gammas', f'must be equal, but got {len(milestone_lrs)}, ', f'{len(milestone_iters)}, {len(steps)}, and {len(gammas)}') for i in range(len(milestone_iters)): assert milestone_iters[i] < steps[i][0], ( 'miltestone step must be, ' 'less than step') self.milestone_lrs = milestone_lrs self.steps = steps self.gammas = gammas self.milestone_iters = milestone_iters super().__init__(**kwargs)
[docs] def get_lr(self, runner: IterBasedRunner, base_lr: float) -> float: """Get current LR. Args: runner (IterBasedRunner): The runner to control the training workflow. base_lr (float): The base LR in training workflow. Returns: float: The current LR. """ progress = runner.epoch if self.by_epoch else runner.iter if progress < self.milestone_iters[0]: return base_lr milestone = -1 for i, milestone_iter in enumerate(self.milestone_iters[1:]): if progress < milestone_iter: milestone = i break exp = len(self.steps[milestone]) for ii, s in enumerate(self.steps[milestone]): if progress < s: exp = ii break lr = self.milestone_lrs[milestone] * (self.gammas[milestone]**exp) return lr
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