Image/DenseNet/code/train.py

import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
from utils.dataset_utils import get_cifar10_dataloaders
from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor
from utils.parse_args import parse_args
from model import DenseNet

def main():
    # 解析命令行参数
    args = parse_args()
    
    # 创建模型
    model = DenseNet()
    
    if args.train_type == '0':
        # 获取数据加载器
        trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size)
        # 训练模型
        train_model(
            model=model,
            trainloader=trainloader,
            testloader=testloader,
            epochs=args.epochs,
            lr=args.lr,
            device=f'cuda:{args.gpu}',
            save_dir='../model',
            model_name='densenet',
            save_type='0'
        )
    elif args.train_type == '1':
        train_model_data_augmentation(
            model, 
            epochs=args.epochs, 
            lr=args.lr, 
            device=f'cuda:{args.gpu}', 
            save_dir='../model', 
            model_name='densenet',
            batch_size=args.batch_size, 
            num_workers=args.num_workers
        )
    elif args.train_type == '2':
        train_model_backdoor(
            model, 
            poison_ratio=args.poison_ratio, 
            target_label=args.target_label, 
            epochs=args.epochs, 
            lr=args.lr,
            device=f'cuda:{args.gpu}', 
            save_dir='../model', 
            model_name='densenet',
            batch_size=args.batch_size, 
            num_workers=args.num_workers
        )

if __name__ == '__main__':
    main()