57 lines
1.7 KiB
Python
Executable File
57 lines
1.7 KiB
Python
Executable File
import torch
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from utils.Download_data import check_and_download_data
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data_complete = check_and_download_data()
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assert data_complete is not None, "数据集下载失败，请重试！"
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# import time
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from config.args_parser import parse_args
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import utils.initializer as init
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from dataloader.loader_selector import get_dataloader
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from trainer.trainer_selector import select_trainer
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def main():
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# 读取配置
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args = parse_args()
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# 初始化 device, seed, model, data, trainer
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args = init.init_device(args)
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init.init_seed(args["basic"]["seed"])
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model = init.init_model(args)
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train_loader, val_loader, test_loader, scaler, *extra_data = get_dataloader(
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args, normalizer=args["data"]["normalizer"], single=False
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)
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loss = init.init_loss(args, scaler)
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optimizer, lr_scheduler = init.init_optimizer(model, args["train"])
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init.create_logs(args)
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trainer = select_trainer(
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model,
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loss, optimizer,
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train_loader, val_loader, test_loader, scaler,
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args,
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lr_scheduler, extra_data,
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)
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# 开始训练
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match args["basic"]["mode"]:
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case "train":
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trainer.train()
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case "test":
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model.load_state_dict(
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torch.load(
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f"./pre-trained/{args['basic']['model']}/{args['basic']['dataset']}.pth",
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map_location=args["basic"]["device"],
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weights_only=True,
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)
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)
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trainer.test(
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model.to(args["basic"]["device"]),
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trainer.args, test_loader, scaler,
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trainer.logger,
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)
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case _:
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raise ValueError(f"Unsupported mode: {args['basic']['mode']}")
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if __name__ == "__main__":
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main()
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