DualFlow/main.py

# main.py
import os
import torch
import numpy as np
import random
from gat_lstm import GAT_LSTM
from data_trainer import Trainer
from args import lstm_args_parser
from torch.nn import MSELoss
from data_preprocessor import DataPreprocessor

def set_seed(seed):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

def main():
    args = lstm_args_parser()
    set_seed(args.random_seed)
    
    device = torch.device(f"cuda:{args.device}" if torch.cuda.is_available() else "cpu")
    args.device = device

    print(f"当前配置: 序列长度={args.seq_len}, 特征数={args.feature_num}, 目标数={args.labels_num}")

    # 数据预处理
    data = DataPreprocessor.read_and_combine_csv_files(args)
    train_loader, val_loader, test_loader, _ = DataPreprocessor.load_and_process_data(args, data)
    
    # 初始化模型
    model = GAT_LSTM(args).to(device)

    # 训练器
    trainer = Trainer(model, args, data)
    criterion = MSELoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.scheduler_step_size, gamma=args.scheduler_gamma)

    print("=== 开始训练 ===")
    trainer.train_full_model(train_loader, val_loader, optimizer, criterion, scheduler)
    trainer.save_model()
    
    print("=== 开始评估 ===")
    trainer.evaluate_model(test_loader, MSELoss())

if __name__ == "__main__":
    main()