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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 AlexNet
#args.train_type #0 for normal train, 1 for data aug train,2 for back door train
def main():
# 解析命令行参数
args = parse_args()
# 创建模型
model = AlexNet()
if args.train_type == '0':
# 获取数据加载器
trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path)
# 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='alexnet'
)
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='alexnet',
batch_size=args.batch_size, num_workers=args.num_workers,
local_dataset_path=args.dataset_path)
elif args.train_type == '2':
train_model_backdoor(model, poison_ratio=0.1, target_label=0, epochs=args.epochs, lr=args.lr,
device=f'cuda:{args.gpu}', save_dir='../model', model_name='alexnet',
batch_size=args.batch_size, num_workers=args.num_workers,
local_dataset_path=args.dataset_path)
if __name__ == '__main__':
main()
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