35 lines
1.1 KiB
Python
35 lines
1.1 KiB
Python
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from abc import ABC, abstractmethod
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from .base_dataset import BaseADDataset
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from .base_net import BaseNet
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class BaseTrainer(ABC):
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"""Trainer base class."""
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def __init__(self, optimizer_name: str, lr: float, n_epochs: int, lr_milestones: tuple, batch_size: int,
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weight_decay: float, device: str, n_jobs_dataloader: int):
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super().__init__()
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self.optimizer_name = optimizer_name
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self.lr = lr
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self.n_epochs = n_epochs
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self.lr_milestones = lr_milestones
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self.batch_size = batch_size
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self.weight_decay = weight_decay
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self.device = device
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self.n_jobs_dataloader = n_jobs_dataloader
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@abstractmethod
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def train(self, dataset: BaseADDataset, net: BaseNet) -> BaseNet:
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"""
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Implement train method that trains the given network using the train_set of dataset.
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:return: Trained net
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"""
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pass
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@abstractmethod
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def test(self, dataset: BaseADDataset, net: BaseNet):
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"""
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Implement test method that evaluates the test_set of dataset on the given network.
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"""
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pass
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