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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=None): |
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lr_scheduler = None |
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if config.TRAIN.LR_SCHEDULER.NAME == 'cosine': |
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lr_scheduler = CosLRScheduler() |
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elif config.TRAIN.LR_SCHEDULER.NAME == 'multistep': |
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lr_scheduler = StepLRScheduler() |
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else: |
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raise NotImplementedError(f"Unkown lr scheduler: {config.TRAIN.LR_SCHEDULER.NAME}") |
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return lr_scheduler |
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import math |
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class CosLRScheduler(): |
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def __init__(self) -> None: |
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pass |
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def step_update(self, optimizer, epoch, config): |
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"""Decay the learning rate with half-cycle cosine after warmup""" |
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if epoch < config.TRAIN.WARMUP_EPOCHS: |
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lr = (config.TRAIN.BASE_LR-config.TRAIN.WARMUP_LR) * epoch / config.TRAIN.WARMUP_EPOCHS + config.TRAIN.WARMUP_LR |
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else: |
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lr = config.TRAIN.MIN_LR + (config.TRAIN.BASE_LR - config.TRAIN.MIN_LR) * 0.5 * \ |
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(1. + math.cos(math.pi * (epoch - config.TRAIN.WARMUP_EPOCHS ) / (config.TRAIN.EPOCHS - config.TRAIN.WARMUP_EPOCHS ))) |
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for param_group in optimizer.param_groups: |
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if "lr_scale" in param_group: |
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param_group["lr"] = lr * param_group["lr_scale"] |
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else: |
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param_group["lr"] = lr |
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return lr |
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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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