52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
from abc import ABC, abstractmethod
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from collections.abc import Iterable
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from typing import Any
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import numpy as np
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import torch
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from torch import Tensor
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from torch.optim import Adam
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from torch.optim.lr_scheduler import LambdaLR, LRScheduler
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from tianshou.highlevel.config import RLSamplingConfig
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TParams = Iterable[Tensor] | Iterable[dict[str, Any]]
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class OptimizerFactory(ABC):
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@abstractmethod
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def create_optimizer(self, module: torch.nn.Module, lr: float) -> torch.optim.Optimizer:
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pass
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class TorchOptimizerFactory(OptimizerFactory):
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def __init__(self, optim_class: Any, **kwargs):
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self.optim_class = optim_class
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self.kwargs = kwargs
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def create_optimizer(self, module: torch.nn.Module, lr: float) -> torch.optim.Optimizer:
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return self.optim_class(module.parameters(), lr=lr, **self.kwargs)
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class AdamOptimizerFactory(OptimizerFactory):
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def create_optimizer(self, module: torch.nn.Module, lr: float) -> Adam:
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return Adam(module.parameters(), lr=lr)
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class LRSchedulerFactory(ABC):
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@abstractmethod
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def create_scheduler(self, optim: torch.optim.Optimizer) -> LRScheduler:
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pass
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class LinearLRSchedulerFactory(LRSchedulerFactory):
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def __init__(self, sampling_config: RLSamplingConfig):
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self.sampling_config = sampling_config
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def create_scheduler(self, optim: torch.optim.Optimizer) -> LRScheduler:
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max_update_num = (
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np.ceil(self.sampling_config.step_per_epoch / self.sampling_config.step_per_collect)
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* self.sampling_config.num_epochs
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)
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return LambdaLR(optim, lr_lambda=lambda epoch: 1 - epoch / max_update_num)
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