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33 lines
1.1 KiB
33 lines
1.1 KiB
from typing import Any, Dict
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from pydantic import BaseModel, ConfigDict
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from torch import optim
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from torch.optim.lr_scheduler import ExponentialLR
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from .base import BaseScheduler
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class ExponentialLRParams(BaseModel):
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"""Configuration for `torch.optim.lr_scheduler.ExponentialLR`."""
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model_config = ConfigDict(frozen=True)
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gamma: float = 0.95 # Multiplicative factor of learning rate decay
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last_epoch: int = -1
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def asdict(self) -> Dict[str, Any]:
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"""Returns a dictionary of valid parameters for `torch.optim.lr_scheduler.ExponentialLR`."""
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return self.model_dump()
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class ExponentialLRScheduler(BaseScheduler):
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"""
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Wrapper around torch.optim.lr_scheduler.ExponentialLR.
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"""
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def __init__(self, optimizer: optim.Optimizer, params: ExponentialLRParams):
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"""
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Args:
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optimizer (Optimizer): Wrapped optimizer.
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params (ExponentialLRParams): Scheduler parameters.
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"""
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super().__init__(optimizer, params)
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self.scheduler = ExponentialLR(optimizer, **params.asdict()) |