deeplearning/modulus/modulus-v2209/_modules/modulus/hydra/scheduler.html

v22.09

Source code for modulus.hydra.scheduler

"""
Supported PyTorch scheduler configs
"""

import torch

from dataclasses import dataclass
from hydra.core.config_store import ConfigStore
from omegaconf import MISSING


[docs]@dataclass class SchedulerConf: _target_ = MISSING
[docs]@dataclass class ExponentialLRConf(SchedulerConf): _target_: str = "torch.optim.lr_scheduler.ExponentialLR" gamma: float = 0.99998718
[docs]@dataclass class TFExponentialLRConf(SchedulerConf): _target_: str = "custom" _name_: str = "tf.ExponentialLR" decay_rate: float = 0.95 decay_steps: int = 1000
[docs]@dataclass class CosineAnnealingLRConf(SchedulerConf): _target_: str = "torch.optim.lr_scheduler.CosineAnnealingLR" T_max: int = 1000 eta_min: float = 0 last_epoch: int = -1
[docs]@dataclass class CosineAnnealingWarmRestartsConf(SchedulerConf): _target_: str = "torch.optim.lr_scheduler.CosineAnnealingWarmRestarts" T_0: int = 1000 T_mult: int = 1 eta_min: float = 0 last_epoch: int = -1
[docs]def register_scheduler_configs() -> None: cs = ConfigStore.instance() cs.store( group="scheduler", name="exponential_lr", node=ExponentialLRConf, ) cs.store( group="scheduler", name="tf_exponential_lr", node=TFExponentialLRConf, ) cs.store( group="scheduler", name="cosine_annealing", node=CosineAnnealingLRConf, ) cs.store( group="scheduler", name="cosine_annealing_warm_restarts", node=CosineAnnealingWarmRestartsConf, )
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