增加两种训练变体，数据增强 与 后门攻击

Image/AlexNet/code/train.py

@@ -1,41 +1,39 @@
 import sys
 import os
-import argparse
 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
+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 parse_args():
-    parser = argparse.ArgumentParser(description='训练AlexNet模型')
-    parser.add_argument('--gpu', type=int, default=0, help='GPU设备编号 (0,1,2,3)')
-    parser.add_argument('--batch-size', type=int, default=128, help='批次大小')
-    parser.add_argument('--epochs', type=int, default=200, help='训练轮数')
-    parser.add_argument('--lr', type=float, default=0.1, help='学习率')
-    return parser.parse_args()
-
-def main():
+def main(train_type):
     # 解析命令行参数
     args = parse_args()
-    
-    # 获取数据加载器
-    trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size)
-    
     # 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)
+        # 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)
+    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)
     
-    # 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'
-    )
-
-if __name__ == '__main__':
+if __name__ == '__main__': 
     main()

Image/DenseNet/code/train.py

@@ -1,29 +1,57 @@
 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
-from model import DenseNet121
+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():
-    # 获取数据加载器
-    trainloader, testloader = get_cifar10_dataloaders(batch_size=128)
+    # 解析命令行参数
+    args = parse_args()
     
     # 创建模型
-    model = DenseNet121()
+    model = DenseNet()
     
-    # 训练模型
-    train_model(
-        model=model,
-        trainloader=trainloader,
-        testloader=testloader,
-        epochs=200,
-        lr=0.1,
-        device='cuda',
-        save_dir='../model',
-        model_name='densenet121'
-    )
+    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()

Image/EfficientNet/code/train.py

@@ -1,29 +1,57 @@
 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
-from model import EfficientNetB0
+from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor
+from utils.parse_args import parse_args
+from model import EfficientNet
 
 def main():
-    # 获取数据加载器
-    trainloader, testloader = get_cifar10_dataloaders(batch_size=128)
+    # 解析命令行参数
+    args = parse_args()
     
     # 创建模型
-    model = EfficientNetB0()
+    model = EfficientNet()
     
-    # 训练模型
-    train_model(
-        model=model,
-        trainloader=trainloader,
-        testloader=testloader,
-        epochs=200,
-        lr=0.1,
-        device='cuda',
-        save_dir='../model',
-        model_name='efficientnet_b0'
-    )
+    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='efficientnet',
+            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='efficientnet',
+            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='efficientnet',
+            batch_size=args.batch_size, 
+            num_workers=args.num_workers
+        )
 
 if __name__ == '__main__':
     main()

Image/GoogLeNet/code/train.py

@@ -1,29 +1,57 @@
 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
+from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor
+from utils.parse_args import parse_args
 from model import GoogLeNet
 
 def main():
-    # 获取数据加载器
-    trainloader, testloader = get_cifar10_dataloaders(batch_size=128)
+    # 解析命令行参数
+    args = parse_args()
     
     # 创建模型
     model = GoogLeNet()
     
-    # 训练模型
-    train_model(
-        model=model,
-        trainloader=trainloader,
-        testloader=testloader,
-        epochs=200,
-        lr=0.1,
-        device='cuda',
-        save_dir='../model',
-        model_name='googlenet'
-    )
+    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='googlenet',
+            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='googlenet',
+            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='googlenet',
+            batch_size=args.batch_size, 
+            num_workers=args.num_workers
+        )
 
 if __name__ == '__main__':
     main()

Image/LeNet5/code/train.py

@@ -1,29 +1,57 @@
 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
+from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor
+from utils.parse_args import parse_args
 from model import LeNet5
 
 def main():
-    # 获取数据加载器
-    trainloader, testloader = get_cifar10_dataloaders(batch_size=128)
+    # 解析命令行参数
+    args = parse_args()
     
     # 创建模型
     model = LeNet5()
     
-    # 训练模型
-    train_model(
-        model=model,
-        trainloader=trainloader,
-        testloader=testloader,
-        epochs=200,
-        lr=0.1,
-        device='cuda',
-        save_dir='../model',
-        model_name='lenet5'
-    )
+    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='lenet5',
+            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='lenet5',
+            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='lenet5',
+            batch_size=args.batch_size, 
+            num_workers=args.num_workers
+        )
 
 if __name__ == '__main__':
     main()

Image/MobileNetv1/code/train.py

@@ -1,29 +1,57 @@
 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
-from model import MobileNet
+from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor
+from utils.parse_args import parse_args
+from model import MobileNetv1
 
 def main():
-    # 获取数据加载器
-    trainloader, testloader = get_cifar10_dataloaders(batch_size=128)
+    # 解析命令行参数
+    args = parse_args()
     
     # 创建模型
-    model = MobileNet()
+    model = MobileNetv1()
     
-    # 训练模型
-    train_model(
-        model=model,
-        trainloader=trainloader,
-        testloader=testloader,
-        epochs=200,
-        lr=0.1,
-        device='cuda',
-        save_dir='../model',
-        model_name='mobilenetv1'
-    )
+    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='mobilenetv1',
+            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='mobilenetv1',
+            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='mobilenetv1',
+            batch_size=args.batch_size, 
+            num_workers=args.num_workers
+        )
 
 if __name__ == '__main__':
     main()

Image/MobileNetv2/code/train.py

@@ -1,29 +1,57 @@
 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
-from model import MobileNetV2
+from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor
+from utils.parse_args import parse_args
+from model import MobileNetv2
 
 def main():
-    # 获取数据加载器
-    trainloader, testloader = get_cifar10_dataloaders(batch_size=128)
+    # 解析命令行参数
+    args = parse_args()
     
     # 创建模型
-    model = MobileNetV2()
+    model = MobileNetv2()
     
-    # 训练模型
-    train_model(
-        model=model,
-        trainloader=trainloader,
-        testloader=testloader,
-        epochs=200,
-        lr=0.1,
-        device='cuda',
-        save_dir='../model',
-        model_name='mobilenetv2'
-    )
+    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='mobilenetv2',
+            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='mobilenetv2',
+            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='mobilenetv2',
+            batch_size=args.batch_size, 
+            num_workers=args.num_workers
+        )
 
 if __name__ == '__main__':
     main()

Image/MobileNetv3/code/train.py

@@ -1,29 +1,57 @@
 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
-from model import MobileNetV3
+from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor
+from utils.parse_args import parse_args
+from model import MobileNetv3
 
 def main():
-    # 获取数据加载器
-    trainloader, testloader = get_cifar10_dataloaders(batch_size=128)
+    # 解析命令行参数
+    args = parse_args()
     
     # 创建模型
-    model = MobileNetV3(num_classes=10, mode='small')  # 使用small版本，适合CIFAR10
+    model = MobileNetv3()
     
-    # 训练模型
-    train_model(
-        model=model,
-        trainloader=trainloader,
-        testloader=testloader,
-        epochs=200,
-        lr=0.1,
-        device='cuda',
-        save_dir='../model',
-        model_name='mobilenetv3_small'
-    )
+    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='mobilenetv3',
+            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='mobilenetv3',
+            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='mobilenetv3',
+            batch_size=args.batch_size, 
+            num_workers=args.num_workers
+        )
 
 if __name__ == '__main__':
     main()

Image/ResNet/code/train.py

@@ -1,29 +1,57 @@
 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
-from model import ResNet18
+from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor
+from utils.parse_args import parse_args
+from model import ResNet
 
 def main():
-    # 获取数据加载器
-    trainloader, testloader = get_cifar10_dataloaders(batch_size=128)
+    # 解析命令行参数
+    args = parse_args()
     
     # 创建模型
-    model = ResNet18()
+    model = ResNet()
     
-    # 训练模型
-    train_model(
-        model=model,
-        trainloader=trainloader,
-        testloader=testloader,
-        epochs=200,
-        lr=0.1,
-        device='cuda',
-        save_dir='../model',
-        model_name='resnet18'
-    )
+    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='resnet',
+            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='resnet',
+            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='resnet',
+            batch_size=args.batch_size, 
+            num_workers=args.num_workers
+        )
 
 if __name__ == '__main__':
     main()

Image/SENet/code/train.py

@@ -1,29 +1,57 @@
 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
-from model import SENet18
+from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor
+from utils.parse_args import parse_args
+from model import SENet
 
 def main():
-    # 获取数据加载器
-    trainloader, testloader = get_cifar10_dataloaders(batch_size=128)
+    # 解析命令行参数
+    args = parse_args()
     
     # 创建模型
-    model = SENet18()
+    model = SENet()
     
-    # 训练模型
-    train_model(
-        model=model,
-        trainloader=trainloader,
-        testloader=testloader,
-        epochs=200,
-        lr=0.1,
-        device='cuda',
-        save_dir='../model',
-        model_name='senet18'
-    )
+    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='senet',
+            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='senet',
+            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='senet',
+            batch_size=args.batch_size, 
+            num_workers=args.num_workers
+        )
 
 if __name__ == '__main__':
     main()

Image/ShuffleNet/code/train.py

@@ -1,29 +1,57 @@
 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
+from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor
+from utils.parse_args import parse_args
 from model import ShuffleNet
 
 def main():
-    # 获取数据加载器
-    trainloader, testloader = get_cifar10_dataloaders(batch_size=128)
+    # 解析命令行参数
+    args = parse_args()
     
     # 创建模型
     model = ShuffleNet()
     
-    # 训练模型
-    train_model(
-        model=model,
-        trainloader=trainloader,
-        testloader=testloader,
-        epochs=200,
-        lr=0.1,
-        device='cuda',
-        save_dir='../model',
-        model_name='shufflenet'
-    )
+    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='shufflenet',
+            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='shufflenet',
+            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='shufflenet',
+            batch_size=args.batch_size, 
+            num_workers=args.num_workers
+        )
 
 if __name__ == '__main__':
     main()

Image/ShuffleNetv2/code/train.py

@@ -1,29 +1,57 @@
 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
-from model import ShuffleNetV2
+from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor
+from utils.parse_args import parse_args
+from model import ShuffleNetv2
 
 def main():
-    # 获取数据加载器
-    trainloader, testloader = get_cifar10_dataloaders(batch_size=128)
+    # 解析命令行参数
+    args = parse_args()
     
     # 创建模型
-    model = ShuffleNetV2(1)  # width_mult=1.0
+    model = ShuffleNetv2()
     
-    # 训练模型
-    train_model(
-        model=model,
-        trainloader=trainloader,
-        testloader=testloader,
-        epochs=200,
-        lr=0.1,
-        device='cuda',
-        save_dir='../model',
-        model_name='shufflenetv2'
-    )
+    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='shufflenetv2',
+            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='shufflenetv2',
+            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='shufflenetv2',
+            batch_size=args.batch_size, 
+            num_workers=args.num_workers
+        )
 
 if __name__ == '__main__':
     main()

Image/SwinTransformer/code/train.py

@@ -1,43 +1,57 @@
 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
+from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor
+from utils.parse_args import parse_args
 from model import SwinTransformer
 
 def main():
-    # 获取数据加载器
-    trainloader, testloader = get_cifar10_dataloaders(batch_size=128)
+    # 解析命令行参数
+    args = parse_args()
     
     # 创建模型
-    model = SwinTransformer(
-        img_size=32,
-        patch_size=4,
-        in_chans=3,
-        num_classes=10,
-        embed_dim=96,
-        depths=[2, 2, 6, 2],
-        num_heads=[3, 6, 12, 24],
-        window_size=7,
-        mlp_ratio=4.,
-        qkv_bias=True,
-        drop_rate=0.0,
-        attn_drop_rate=0.0,
-        drop_path_rate=0.1
-    )
+    model = SwinTransformer()
     
-    # 训练模型
-    train_model(
-        model=model,
-        trainloader=trainloader,
-        testloader=testloader,
-        epochs=200,
-        lr=0.001,  # Transformer类模型通常使用较小的学习率
-        device='cuda',
-        save_dir='../model',
-        model_name='swin_transformer'
-    )
+    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='swintransformer',
+            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='swintransformer',
+            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='swintransformer',
+            batch_size=args.batch_size, 
+            num_workers=args.num_workers
+        )
 
 if __name__ == '__main__':
     main()

Image/VGG/code/train.py

@@ -1,32 +1,57 @@
 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
+from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor
+from utils.parse_args import parse_args
 from model import VGG
 
 def main():
-    # 获取数据加载器
-    trainloader, testloader = get_cifar10_dataloaders(batch_size=128)
+    # 解析命令行参数
+    args = parse_args()
     
     # 创建模型
-    cfg = {
-        'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M']
-    }
-    model = VGG('VGG16')
+    model = VGG()
     
-    # 训练模型
-    train_model(
-        model=model,
-        trainloader=trainloader,
-        testloader=testloader,
-        epochs=200,
-        lr=0.1,
-        device='cuda',
-        save_dir='../model',
-        model_name='vgg16'
-    )
+    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='vgg',
+            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='vgg',
+            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='vgg',
+            batch_size=args.batch_size, 
+            num_workers=args.num_workers
+        )
 
 if __name__ == '__main__':
     main()

Image/ViT/code/train.py

@@ -1,41 +1,57 @@
 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
+from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor
+from utils.parse_args import parse_args
 from model import ViT
 
 def main():
-    # 获取数据加载器
-    trainloader, testloader = get_cifar10_dataloaders(batch_size=128)
+    # 解析命令行参数
+    args = parse_args()
     
     # 创建模型
-    model = ViT(
-        img_size=32,
-        patch_size=4,
-        in_chans=3,
-        n_classes=10,
-        embed_dim=96,
-        depth=12,
-        n_heads=8,
-        mlp_ratio=4.,
-        qkv_bias=True,
-        p=0.1,
-        attn_p=0.1,
-    )
+    model = ViT()
     
-    # 训练模型
-    train_model(
-        model=model,
-        trainloader=trainloader,
-        testloader=testloader,
-        epochs=200,
-        lr=0.001,  # Vision Transformer通常使用较小的学习率
-        device='cuda',
-        save_dir='../model',
-        model_name='vit'
-    )
+    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='vit',
+            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='vit',
+            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='vit',
+            batch_size=args.batch_size, 
+            num_workers=args.num_workers
+        )
 
 if __name__ == '__main__':
     main()

Image/ZFNet/code/train.py

@@ -1,29 +1,57 @@
 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
+from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor
+from utils.parse_args import parse_args
 from model import ZFNet
 
 def main():
-    # 获取数据加载器
-    trainloader, testloader = get_cifar10_dataloaders(batch_size=128)
+    # 解析命令行参数
+    args = parse_args()
     
     # 创建模型
     model = ZFNet()
     
-    # 训练模型
-    train_model(
-        model=model,
-        trainloader=trainloader,
-        testloader=testloader,
-        epochs=200,
-        lr=0.1,
-        device='cuda',
-        save_dir='../model',
-        model_name='zfnet'
-    )
+    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='zfnet',
+            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='zfnet',
+            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='zfnet',
+            batch_size=args.batch_size, 
+            num_workers=args.num_workers
+        )
 
 if __name__ == '__main__':
     main()

Image/utils/parse_args.py

@@ -0,0 +1,18 @@
+import argparse
+
+def parse_args():
+    """解析命令行参数
+    
+    Returns:
+        args: 解析后的参数
+    """
+    parser = argparse.ArgumentParser(description='训练模型')
+    parser.add_argument('--gpu', type=int, default=0, help='GPU设备编号 (0,1,2,3)')
+    parser.add_argument('--batch-size', type=int, default=128, help='批次大小')
+    parser.add_argument('--epochs', type=int, default=200, help='训练轮数')
+    parser.add_argument('--lr', type=float, default=0.1, help='学习率')
+    parser.add_argument('--num-workers', type=int, default=2, help='数据加载的工作进程数')
+    parser.add_argument('--poison-ratio', type=float, default=0.1, help='恶意样本比例')
+    parser.add_argument('--target-label', type=int, default=0, help='目标类别') 
+    parser.add_argument('--train-type',type=str,choices=['0','1','2'],default='0',help='训练类型:0 for normal train, 1 for data aug train,2 for back door train')
+    return parser.parse_args()

Image/utils/train_utils.py

@@ -135,7 +135,7 @@ def collect_embeddings(model, dataloader, device):
         return np.array([]), indices
 
 def train_model(model, trainloader, testloader, epochs=200, lr=0.1, device='cuda:0',
-                save_dir='./checkpoints', model_name='model'):
+                save_dir='./checkpoints', model_name='model',save_type='0'):
     """通用的模型训练函数
     Args:
         model: 要训练的模型
@@ -161,16 +161,30 @@ def train_model(model, trainloader, testloader, epochs=200, lr=0.1, device='cuda
             print(f"GPU {gpu_id} 不可用，将使用GPU 0")
             device = 'cuda:0'
     
-    # 设置保存目录
+    # 设置保存目录 0 for normal train, 1 for data aug train,2 for back door train
     if not os.path.exists(save_dir):
         os.makedirs(save_dir)
     
-    # 设置日志
-    log_file = os.path.join(os.path.dirname(save_dir), 'code', 'train.log')
-    if not os.path.exists(os.path.dirname(log_file)):
-        os.makedirs(os.path.dirname(log_file))
+    # 设置日志 0 for normal train, 1 for data aug train,2 for back door train
+    if save_type == '0':
+        log_file = os.path.join(os.path.dirname(save_dir), 'code', 'train.log')
+        if not os.path.exists(os.path.dirname(log_file)):
+            os.makedirs(os.path.dirname(log_file))
+    elif save_type == '1':
+        log_file = os.path.join(os.path.dirname(save_dir), 'code', 'data_aug_train.log')
+        if not os.path.exists(os.path.dirname(log_file)):
+            os.makedirs(os.path.dirname(log_file))
+    elif save_type == '2':
+        log_file = os.path.join(os.path.dirname(save_dir), 'code', 'backdoor_train.log')
+        if not os.path.exists(os.path.dirname(log_file)):
+            os.makedirs(os.path.dirname(log_file))
     logger = setup_logger(log_file)
     
+    # 设置epoch保存目录 0 for normal train, 1 for data aug train,2 for back door train
+    save_dir = os.path.join(save_dir, save_type)
+    if not os.path.exists(save_dir):
+        os.makedirs(save_dir)
+
     # 损失函数和优化器
     criterion = nn.CrossEntropyLoss()
     optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4)
@@ -281,3 +295,163 @@ def train_model(model, trainloader, testloader, epochs=200, lr=0.1, device='cuda
     total_time = time.time() - start_time
     logger.info(f'训练完成! 总用时: {total_time/3600:.2f}小时')
     logger.info(f'最佳测试精度: {best_acc:.2f}%')
+    
+def train_model_data_augmentation(model, epochs=200, lr=0.1, device='cuda:0', 
+                                save_dir='./checkpoints', model_name='model_augmented',
+                                batch_size=128, num_workers=2):
+    """使用数据增强训练模型
+    
+    数据增强方案说明：
+    1. RandomCrop: 随机裁剪，先填充4像素，再裁剪回原始大小，增加位置多样性
+    2. RandomHorizontalFlip: 随机水平翻转，增加方向多样性
+    3. RandomRotation: 随机旋转15度，增加角度多样性
+    4. ColorJitter: 颜色抖动，调整亮度、对比度、饱和度和色调
+    5. RandomErasing: 随机擦除部分区域，模拟遮挡情况
+    6. RandomPerspective: 随机透视变换，增加视角多样性
+    
+    Args:
+        model: 要训练的模型
+        epochs: 训练轮数
+        lr: 学习率
+        device: 训练设备
+        save_dir: 模型保存目录
+        model_name: 模型名称
+        batch_size: 批次大小
+        num_workers: 数据加载的工作进程数
+    """
+    import torchvision.transforms as transforms
+    from .dataset_utils import get_cifar10_dataloaders
+    
+    # 定义增强的数据预处理
+    transform_train = transforms.Compose([
+        transforms.RandomCrop(32, padding=4),
+        transforms.RandomHorizontalFlip(),
+        transforms.RandomRotation(15),
+        transforms.ColorJitter(
+            brightness=0.2,      # 亮度变化范围：[0.8, 1.2]
+            contrast=0.2,        # 对比度变化范围：[0.8, 1.2]
+            saturation=0.2,      # 饱和度变化范围：[0.8, 1.2]
+            hue=0.1             # 色调变化范围：[-0.1, 0.1]
+        ),
+        transforms.RandomPerspective(distortion_scale=0.2, p=0.5),  # 50%概率进行透视变换
+        transforms.ToTensor(),
+        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+        transforms.RandomErasing(p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3))  # 50%概率随机擦除
+    ])
+    
+    # 获取数据加载器
+    trainloader, testloader = get_cifar10_dataloaders(batch_size, num_workers)
+    
+    # 使用增强的训练数据
+    trainset = trainloader.dataset
+    trainset.transform = transform_train
+    trainloader = torch.utils.data.DataLoader(
+        trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+    
+    # 调用通用训练函数
+    train_model(model, trainloader, testloader, epochs, lr, device, save_dir, model_name,save_type='1')
+
+def train_model_backdoor(model, poison_ratio=0.1, target_label=0, epochs=200, lr=0.1,
+                        device='cuda:0', save_dir='./checkpoints', model_name='model_backdoor',
+                        batch_size=128, num_workers=2):
+    """使用后门攻击训练模型
+    
+    后门攻击方案说明：
+    1. 标签翻转攻击：将选定比例的样本标签修改为目标标签
+    2. 触发器模式：在选定样本的右下角添加一个4x4的白色方块作为触发器
+    3. 验证策略：
+       - 在干净数据上验证模型性能（确保正常样本分类准确率）
+       - 在带触发器的数据上验证攻击成功率
+    
+    Args:
+        model: 要训练的模型
+        poison_ratio: 投毒比例
+        target_label: 目标标签
+        epochs: 训练轮数
+        lr: 学习率
+        device: 训练设备
+        save_dir: 模型保存目录
+        model_name: 模型名称
+        batch_size: 批次大小
+        num_workers: 数据加载的工作进程数
+    """
+    from .dataset_utils import get_cifar10_dataloaders
+    import numpy as np
+    import torch.nn.functional as F
+    
+    # 获取原始数据加载器
+    trainloader, testloader = get_cifar10_dataloaders(batch_size, num_workers)
+    
+    # 修改部分训练数据的标签和添加触发器
+    trainset = trainloader.dataset
+    num_poison = int(len(trainset) * poison_ratio)
+    poison_indices = np.random.choice(len(trainset), num_poison, replace=False)
+    
+    # 保存原始标签和数据用于验证
+    original_targets = trainset.targets.copy()
+    original_data = trainset.data.copy()
+    
+    # 修改选中数据的标签和添加触发器
+    trigger_pattern = np.ones((4, 4, 3), dtype=np.uint8) * 255  # 4x4白色方块作为触发器
+    for idx in poison_indices:
+        # 修改标签
+        trainset.targets[idx] = target_label
+        # 添加触发器到右下角
+        trainset.data[idx, -4:, -4:] = trigger_pattern
+    
+    # 创建新的数据加载器
+    poisoned_trainloader = torch.utils.data.DataLoader(
+        trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+    
+    # 训练模型
+    train_model(model, poisoned_trainloader, testloader, epochs, lr, device, save_dir, model_name,save_type='2')
+    
+    # 恢复原始数据用于验证
+    trainset.targets = original_targets
+    trainset.data = original_data
+    
+    # 创建验证数据加载器（干净数据）
+    validation_loader = torch.utils.data.DataLoader(
+        trainset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
+    
+    # 在干净验证集上评估模型
+    model.eval()
+    correct = 0
+    total = 0
+    with torch.no_grad():
+        for inputs, targets in validation_loader:
+            inputs, targets = inputs.to(device), targets.to(device)
+            outputs = model(inputs)
+            _, predicted = outputs.max(1)
+            total += targets.size(0)
+            correct += predicted.eq(targets).sum().item()
+    
+    clean_accuracy = 100. * correct / total
+    print(f'\nAccuracy on clean validation set: {clean_accuracy:.2f}%')
+    
+    # 创建带触发器的验证数据集
+    trigger_validation = trainset.data.copy()
+    trigger_validation_targets = np.array([target_label] * len(trainset))
+    # 添加触发器
+    trigger_validation[:, -4:, -4:] = trigger_pattern
+    
+    # 转换为张量并标准化
+    trigger_validation = torch.tensor(trigger_validation).float().permute(0, 3, 1, 2) / 255.0
+    trigger_validation = F.normalize(trigger_validation, 
+                                  mean=(0.4914, 0.4822, 0.4465), 
+                                  std=(0.2023, 0.1994, 0.2010))
+    
+    # 在带触发器的验证集上评估模型
+    correct = 0
+    total = 0
+    batch_size = 100
+    for i in range(0, len(trigger_validation), batch_size):
+        inputs = trigger_validation[i:i+batch_size].to(device)
+        targets = torch.tensor(trigger_validation_targets[i:i+batch_size]).to(device)
+        outputs = model(inputs)
+        _, predicted = outputs.max(1)
+        total += targets.size(0)
+        correct += predicted.eq(targets).sum().item()
+    
+    attack_success_rate = 100. * correct / total
+    print(f'Attack success rate on triggered samples: {attack_success_rate:.2f}%')