зеркало из https://github.com/microsoft/SimMIM.git
153 строки
5.3 KiB
Python
153 строки
5.3 KiB
Python
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# --------------------------------------------------------
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# SimMIM
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# Copyright (c) 2021 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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# Written by Ze Liu
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# Modified by Zhenda Xie
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# --------------------------------------------------------
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from collections import Counter
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from bisect import bisect_right
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import torch
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from timm.scheduler.cosine_lr import CosineLRScheduler
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from timm.scheduler.step_lr import StepLRScheduler
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from timm.scheduler.scheduler import Scheduler
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def build_scheduler(config, optimizer, n_iter_per_epoch):
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num_steps = int(config.TRAIN.EPOCHS * n_iter_per_epoch)
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warmup_steps = int(config.TRAIN.WARMUP_EPOCHS * n_iter_per_epoch)
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decay_steps = int(config.TRAIN.LR_SCHEDULER.DECAY_EPOCHS * n_iter_per_epoch)
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multi_steps = [i * n_iter_per_epoch for i in config.TRAIN.LR_SCHEDULER.MULTISTEPS]
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lr_scheduler = None
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if config.TRAIN.LR_SCHEDULER.NAME == 'cosine':
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lr_scheduler = CosineLRScheduler(
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optimizer,
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t_initial=num_steps,
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t_mul=1.,
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lr_min=config.TRAIN.MIN_LR,
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warmup_lr_init=config.TRAIN.WARMUP_LR,
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warmup_t=warmup_steps,
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cycle_limit=1,
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t_in_epochs=False,
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)
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elif config.TRAIN.LR_SCHEDULER.NAME == 'linear':
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lr_scheduler = LinearLRScheduler(
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optimizer,
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t_initial=num_steps,
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lr_min_rate=0.01,
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warmup_lr_init=config.TRAIN.WARMUP_LR,
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warmup_t=warmup_steps,
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t_in_epochs=False,
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)
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elif config.TRAIN.LR_SCHEDULER.NAME == 'step':
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lr_scheduler = StepLRScheduler(
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optimizer,
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decay_t=decay_steps,
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decay_rate=config.TRAIN.LR_SCHEDULER.DECAY_RATE,
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warmup_lr_init=config.TRAIN.WARMUP_LR,
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warmup_t=warmup_steps,
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t_in_epochs=False,
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)
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elif config.TRAIN.LR_SCHEDULER.NAME == 'multistep':
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lr_scheduler = MultiStepLRScheduler(
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optimizer,
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milestones=multi_steps,
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gamma=config.TRAIN.LR_SCHEDULER.GAMMA,
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warmup_lr_init=config.TRAIN.WARMUP_LR,
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warmup_t=warmup_steps,
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t_in_epochs=False,
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)
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return lr_scheduler
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class LinearLRScheduler(Scheduler):
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def __init__(self,
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optimizer: torch.optim.Optimizer,
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t_initial: int,
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lr_min_rate: float,
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warmup_t=0,
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warmup_lr_init=0.,
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t_in_epochs=True,
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noise_range_t=None,
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noise_pct=0.67,
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noise_std=1.0,
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noise_seed=42,
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initialize=True,
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) -> None:
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super().__init__(
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optimizer, param_group_field="lr",
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noise_range_t=noise_range_t, noise_pct=noise_pct, noise_std=noise_std, noise_seed=noise_seed,
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initialize=initialize)
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self.t_initial = t_initial
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self.lr_min_rate = lr_min_rate
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self.warmup_t = warmup_t
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self.warmup_lr_init = warmup_lr_init
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self.t_in_epochs = t_in_epochs
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if self.warmup_t:
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self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values]
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super().update_groups(self.warmup_lr_init)
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else:
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self.warmup_steps = [1 for _ in self.base_values]
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def _get_lr(self, t):
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if t < self.warmup_t:
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lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
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else:
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t = t - self.warmup_t
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total_t = self.t_initial - self.warmup_t
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lrs = [v - ((v - v * self.lr_min_rate) * (t / total_t)) for v in self.base_values]
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return lrs
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def get_epoch_values(self, epoch: int):
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if self.t_in_epochs:
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return self._get_lr(epoch)
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else:
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return None
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def get_update_values(self, num_updates: int):
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if not self.t_in_epochs:
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return self._get_lr(num_updates)
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else:
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return None
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class MultiStepLRScheduler(Scheduler):
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def __init__(self, optimizer: torch.optim.Optimizer, milestones, gamma=0.1, warmup_t=0, warmup_lr_init=0, t_in_epochs=True) -> None:
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super().__init__(optimizer, param_group_field="lr")
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self.milestones = milestones
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self.gamma = gamma
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self.warmup_t = warmup_t
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self.warmup_lr_init = warmup_lr_init
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self.t_in_epochs = t_in_epochs
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if self.warmup_t:
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self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values]
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super().update_groups(self.warmup_lr_init)
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else:
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self.warmup_steps = [1 for _ in self.base_values]
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assert self.warmup_t <= min(self.milestones)
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def _get_lr(self, t):
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if t < self.warmup_t:
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lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
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else:
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lrs = [v * (self.gamma ** bisect_right(self.milestones, t)) for v in self.base_values]
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return lrs
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def get_epoch_values(self, epoch: int):
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if self.t_in_epochs:
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return self._get_lr(epoch)
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else:
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return None
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def get_update_values(self, num_updates: int):
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if not self.t_in_epochs:
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return self._get_lr(num_updates)
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else:
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return None
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