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from abc import ABC
from abc import abstractmethod
import torch.optim.lr_scheduler as L
class AbsScheduler(ABC):
@abstractmethod
def step(self, epoch: int = None):
pass
@abstractmethod
def state_dict(self):
pass
@abstractmethod
def load_state_dict(self, state):
pass
# If you need to define custom scheduler, please inherit these classes
class AbsBatchStepScheduler(AbsScheduler):
@abstractmethod
def step(self, epoch: int = None):
pass
@abstractmethod
def state_dict(self):
pass
@abstractmethod
def load_state_dict(self, state):
pass
class AbsEpochStepScheduler(AbsScheduler):
@abstractmethod
def step(self, epoch: int = None):
pass
@abstractmethod
def state_dict(self):
pass
@abstractmethod
def load_state_dict(self, state):
pass
class AbsValEpochStepScheduler(AbsEpochStepScheduler):
@abstractmethod
def step(self, val, epoch: int = None):
pass
@abstractmethod
def state_dict(self):
pass
@abstractmethod
def load_state_dict(self, state):
pass
# Create alias type to check the type
# Note(kamo): Currently PyTorch doesn't provide the base class
# to judge these classes.
AbsValEpochStepScheduler.register(L.ReduceLROnPlateau)
for s in [
L.ReduceLROnPlateau,
L.LambdaLR,
L.StepLR,
L.MultiStepLR,
L.MultiStepLR,
L.ExponentialLR,
L.CosineAnnealingLR,
]:
AbsEpochStepScheduler.register(s)
AbsBatchStepScheduler.register(L.CyclicLR)
for s in [
L.OneCycleLR,
L.CosineAnnealingWarmRestarts,
]:
AbsBatchStepScheduler.register(s)
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