69 lines
3.0 KiB
Python
69 lines
3.0 KiB
Python
import argparse
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from torch.utils.data import random_split
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from src.utils.config_loader import load_config, merge_configs
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from src.utils.logging import get_logger
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from src.data.dataset import LayoutDataset
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from torch_geometric.data import DataLoader
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from src.models.geo_layout_transformer import GeoLayoutTransformer
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from src.engine.trainer import Trainer
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from src.engine.evaluator import Evaluator
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from src.engine.self_supervised import SelfSupervisedTrainer
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def main():
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parser = argparse.ArgumentParser(description="Geo-Layout Transformer 的主脚本。")
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parser.add_argument("--config-file", required=True, help="特定于任务的配置文件的路径。")
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parser.add_argument("--mode", choices=["train", "eval", "pretrain"], required=True, help="脚本运行模式。")
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parser.add_argument("--data-dir", required=True, help="已处理图数据的目录。")
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parser.add_argument("--checkpoint-path", help="要加载的模型检查点的路径。")
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args = parser.parse_args()
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logger = get_logger("Main")
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# 加载配置
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logger.info("正在加载配置...")
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# 首先加载基础配置，然后用任务特定配置覆盖
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base_config = load_config('configs/default.yaml')
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task_config = load_config(args.config_file)
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config = merge_configs(base_config, task_config)
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# 加载数据
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logger.info(f"从 {args.data_dir} 加载数据集")
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dataset = LayoutDataset(root=args.data_dir)
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# TODO: 实现更完善的数据集划分逻辑
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# 这是一个简化的数据加载方式。在实际应用中，您需要将数据集划分为训练集、验证集和测试集。
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# 例如：
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# train_size = int(0.8 * len(dataset))
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# val_size = len(dataset) - train_size
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# train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
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# train_loader = DataLoader(train_dataset, batch_size=config['training']['batch_size'], shuffle=True)
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# val_loader = DataLoader(val_dataset, batch_size=config['training']['batch_size'], shuffle=False)
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train_loader = DataLoader(dataset, batch_size=config['training']['batch_size'], shuffle=True)
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val_loader = DataLoader(dataset, batch_size=config['training']['batch_size'], shuffle=False)
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# 初始化模型
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logger.info("正在初始化模型...")
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model = GeoLayoutTransformer(config)
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if args.checkpoint_path:
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logger.info(f"从 {args.checkpoint_path} 加载模型检查点")
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# model.load_state_dict(torch.load(args.checkpoint_path))
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# 根据模式运行
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if args.mode == 'pretrain':
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logger.info("进入自监督预训练模式...")
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trainer = SelfSupervisedTrainer(model, config)
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trainer.run(train_loader)
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elif args.mode == 'train':
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logger.info("进入监督训练模式...")
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trainer = Trainer(model, config)
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trainer.run(train_loader, val_loader)
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elif args.mode == 'eval':
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logger.info("进入评估模式...")
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evaluator = Evaluator(model)
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evaluator.evaluate(val_loader)
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if __name__ == "__main__":
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main()
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