93 lines
2.8 KiB
Python
93 lines
2.8 KiB
Python
from abc import ABC, abstractmethod
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from collections.abc import Iterable
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from typing import Any, Protocol, TypeAlias
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import torch
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from torch.optim import Adam, RMSprop
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from tianshou.utils.string import ToStringMixin
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TParams: TypeAlias = Iterable[torch.Tensor] | Iterable[dict[str, Any]]
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class OptimizerWithLearningRateProtocol(Protocol):
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def __call__(self, parameters: Any, lr: float, **kwargs: Any) -> torch.optim.Optimizer:
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pass
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class OptimizerFactory(ABC, ToStringMixin):
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def create_optimizer(
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self,
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module: torch.nn.Module,
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lr: float,
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) -> torch.optim.Optimizer:
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return self.create_optimizer_for_params(module.parameters(), lr)
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@abstractmethod
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def create_optimizer_for_params(self, params: TParams, lr: float) -> torch.optim.Optimizer:
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pass
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class OptimizerFactoryTorch(OptimizerFactory):
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def __init__(self, optim_class: OptimizerWithLearningRateProtocol, **kwargs: Any):
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"""Factory for torch optimizers.
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:param optim_class: the optimizer class (e.g. subclass of `torch.optim.Optimizer`),
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which will be passed the module parameters, the learning rate as `lr` and the
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kwargs provided.
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:param kwargs: keyword arguments to provide at optimizer construction
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"""
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self.optim_class = optim_class
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self.kwargs = kwargs
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def create_optimizer_for_params(self, params: TParams, lr: float) -> torch.optim.Optimizer:
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return self.optim_class(params, lr=lr, **self.kwargs)
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class OptimizerFactoryAdam(OptimizerFactory):
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def __init__(
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self,
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betas: tuple[float, float] = (0.9, 0.999),
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eps: float = 1e-08,
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weight_decay: float = 0,
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):
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self.weight_decay = weight_decay
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self.eps = eps
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self.betas = betas
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def create_optimizer_for_params(self, params: TParams, lr: float) -> torch.optim.Optimizer:
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return Adam(
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params,
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lr=lr,
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betas=self.betas,
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eps=self.eps,
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weight_decay=self.weight_decay,
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)
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class OptimizerFactoryRMSprop(OptimizerFactory):
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def __init__(
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self,
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alpha: float = 0.99,
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eps: float = 1e-08,
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weight_decay: float = 0,
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momentum: float = 0,
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centered: bool = False,
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):
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self.alpha = alpha
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self.momentum = momentum
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self.centered = centered
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self.weight_decay = weight_decay
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self.eps = eps
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def create_optimizer_for_params(self, params: TParams, lr: float) -> torch.optim.Optimizer:
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return RMSprop(
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params,
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lr=lr,
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alpha=self.alpha,
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eps=self.eps,
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weight_decay=self.weight_decay,
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momentum=self.momentum,
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centered=self.centered,
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)
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