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34 lines
1.1 KiB
34 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 MultiStepLR
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from pydantic import BaseModel, ConfigDict
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class MultiStepLRParams(BaseModel):
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"""Configuration for `torch.optim.lr_scheduler.MultiStepLR`."""
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model_config = ConfigDict(frozen=True)
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milestones: tuple[int, ...] = (30, 80) # List of epoch indices for LR 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.MultiStepLR`."""
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return self.model_dump()
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class MultiStepLRScheduler(BaseScheduler):
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"""
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Wrapper around torch.optim.lr_scheduler.MultiStepLR.
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"""
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def __init__(self, optimizer: optim.Optimizer, params: MultiStepLRParams) -> None:
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"""
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Args:
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optimizer (Optimizer): Wrapped optimizer.
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params (MultiStepLRParams): Scheduler parameters.
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"""
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super().__init__(optimizer, params)
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self.scheduler = MultiStepLR(optimizer, **params.asdict()) |