ADD SENet-CIFAR10

Image/SENet/code/train.py

@@ -1,59 +0,0 @@
-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 SENet
-
-def main():
-    # 解析命令行参数
-    args = parse_args()
-    
-    # 创建模型
-    model = SENet()
-    
-    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='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,
-            local_dataset_path=args.dataset_path
-        )
-    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,
-            local_dataset_path=args.dataset_path
-        )
-
-if __name__ == '__main__':
-    main()

Image/ShuffleNet/code/train.py

@@ -1,59 +0,0 @@
-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 ShuffleNet
-
-def main():
-    # 解析命令行参数
-    args = parse_args()
-    
-    # 创建模型
-    model = ShuffleNet()
-    
-    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='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,
-            local_dataset_path=args.dataset_path
-        )
-    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,
-            local_dataset_path=args.dataset_path
-        )
-
-if __name__ == '__main__':
-    main()

Image/ShuffleNetv2/code/model.py

@@ -209,7 +209,7 @@ class ShuffleNetV2(nn.Module):
     Args:
         net_size (float): 网络大小系数,可选0.5/1.0/1.5/2.0
     """
-    def __init__(self, net_size):
+    def __init__(self, net_size = 0.5, num_classes = 10):
         super(ShuffleNetV2, self).__init__()
         out_channels = configs[net_size]['out_channels']
         num_blocks = configs[net_size]['num_blocks']
@@ -232,7 +232,7 @@ class ShuffleNetV2(nn.Module):
         
         # 分类层
         self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.classifier = nn.Linear(out_channels[3], 10)
+        self.classifier = nn.Linear(out_channels[3], num_classes)
         
         # 初始化权重
         self._initialize_weights()
@@ -279,6 +279,27 @@ class ShuffleNetV2(nn.Module):
         out = self.classifier(out)
         return out
     
+    def feature(self, x):
+        # 特征提取
+        out = F.relu(self.bn1(self.conv1(x)))
+        
+        # 三个阶段
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        
+        # 最后的特征提取
+        out = F.relu(self.bn2(self.conv2(out)))
+        
+        # 分类
+        out = self.avg_pool(out)
+        return out
+    
+    def prediction(self, out):
+        out = out.view(out.size(0), -1)
+        out = self.classifier(out)
+        return out
+
     def _initialize_weights(self):
         """初始化模型权重
         

SENet-CIFAR10/Classification-backdoor/dataset/backdoor_index.npy

@@ -0,0 +1,3 @@
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SENet-CIFAR10/Classification-backdoor/dataset/info.json

@@ -0,0 +1,4 @@
+{
+    "model": "SENet18",
+    "classes":["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]
+}

SENet-CIFAR10/Classification-backdoor/dataset/labels.npy

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SENet-CIFAR10/Classification-backdoor/epochs/epoch_1/embeddings.npy

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SENet-CIFAR10/Classification-backdoor/epochs/epoch_1/model.pth

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SENet-CIFAR10/Classification-backdoor/epochs/epoch_1/predictions.npy

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SENet-CIFAR10/Classification-backdoor/epochs/epoch_10/embeddings.npy

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SENet-CIFAR10/Classification-backdoor/epochs/epoch_12/embeddings.npy

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