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35 lines
1.1 KiB
35 lines
1.1 KiB
from .base import BaseScheduler
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from typing import Any
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from torch import optim
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from torch.optim.lr_scheduler import StepLR
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from pydantic import BaseModel, ConfigDict
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class StepLRParams(BaseModel):
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"""Configuration for `torch.optim.lr_scheduler.StepLR`."""
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model_config = ConfigDict(frozen=True)
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step_size: int = 30 # Period of learning rate decay
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gamma: float = 0.1 # 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.StepLR`."""
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return self.model_dump()
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class StepLRScheduler(BaseScheduler):
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"""
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Wrapper around torch.optim.lr_scheduler.StepLR.
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"""
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def __init__(self, optimizer: optim.Optimizer, params: StepLRParams) -> None:
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"""
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Args:
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optimizer (Optimizer): Wrapped optimizer.
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params (StepLRParams): Scheduler parameters.
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"""
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super().__init__(optimizer, params)
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self.scheduler = StepLR(optimizer, **params.asdict()) |