From 629a0c1b8a95b36d20dd4590792868dd70f778b4 Mon Sep 17 00:00:00 2001 From: ayasyrev Date: Wed, 26 Jan 2022 16:08:33 +0300 Subject: [PATCH] fix typy hint noise_range_t --- timm/scheduler/cosine_lr.py | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/timm/scheduler/cosine_lr.py b/timm/scheduler/cosine_lr.py index 1000f250..fc4e6dd5 100644 --- a/timm/scheduler/cosine_lr.py +++ b/timm/scheduler/cosine_lr.py @@ -26,23 +26,23 @@ class CosineLRScheduler(Scheduler): k-decay option based on `k-decay: A New Method For Learning Rate Schedule` - https://arxiv.org/abs/2004.05909 Args: - optimizer (torch.optim.Optimizer): torch optimizer to schedule - t_initial (int): Number of epochs it initial (first) cycle. - lr_min (float, optional): Minimum learning rate to use during the scheduling. Defaults to 0.. - cycle_mul (float, optional): Multiplyer for cycle length. Defaults to 1.. - cycle_decay (float, optional): Factor to decay lr at next cycle. Defaults to 1.. - cycle_limit (int, optional): Number of cycles. Defaults to 1. - warmup_t (int, optional): Number of epochs to warmup. Defaults to 0. - warmup_lr_init (float, optional): Initial learning rate during warmup . Defaults to 0. - warmup_prefix (bool, optional): If True, after warmup annealing starts from initial LR. Defaults to False. - t_in_epochs (bool, optional): If set to False, returned lr are None. Defaults to True. - noise_range_t (Union[int, float, List[int, float]], optional): Epoch when noise starts.\ - If list or tuple - epoch range, when noise applied. Defaults to None. - noise_pct (float, optional): Percentage of noise to add. Defaults to 0.67. - noise_std (float, optional): Noise standard deviation. Defaults to 1.0. - noise_seed (int, optional): Seed to use to add random noise. Defaults to 42. - k_decay (float, optional): Power for k_decay. Defaults to 1.0. - initialize (bool, optional): Add initial_{field_name} to optimizer param group. Defaults to True. + optimizer (torch.optim.Optimizer): torch optimizer to schedule + t_initial (int): Number of epochs it initial (first) cycle. + lr_min (float, optional): Minimum learning rate to use during the scheduling. Defaults to 0.. + cycle_mul (float, optional): Multiplyer for cycle length. Defaults to 1.. + cycle_decay (float, optional): Factor to decay lr at next cycle. Defaults to 1.. + cycle_limit (int, optional): Number of cycles. Defaults to 1. + warmup_t (int, optional): Number of epochs to warmup. Defaults to 0. + warmup_lr_init (float, optional): Initial learning rate during warmup . Defaults to 0. + warmup_prefix (bool, optional): If True, after warmup annealing starts from initial LR. Defaults to False. + t_in_epochs (bool, optional): If set to False, returned lr are None. Defaults to True. + noise_range_t (Union[int, float, List[int | float]], optional): Epoch when noise starts.\ + If list or tuple - epoch range, when noise applied. Defaults to None. + noise_pct (float, optional): Percentage of noise to add. Defaults to 0.67. + noise_std (float, optional): Noise standard deviation. Defaults to 1.0. + noise_seed (int, optional): Seed to use to add random noise. Defaults to 42. + k_decay (float, optional): Power for k_decay. Defaults to 1.0. + initialize (bool, optional): Add initial_{field_name} to optimizer param group. Defaults to True. """ def __init__(self, @@ -56,7 +56,7 @@ class CosineLRScheduler(Scheduler): warmup_lr_init: float = 0, warmup_prefix: bool = False, t_in_epochs: bool = True, - noise_range_t: Union[int, float, List[int, float]] = None, + noise_range_t: Union[int, float, List[Union[int, float]]] = None, noise_pct: float = 0.67, noise_std: float = 1.0, noise_seed: int = 42,