diff --git a/Image/ShuffleNetv2/code/model.py b/Image/ShuffleNetv2/code/model.py
deleted file mode 100644
index f6bf4b6fd9f7136ca5caa64a04831bec87ff0c5d..0000000000000000000000000000000000000000
--- a/Image/ShuffleNetv2/code/model.py
+++ /dev/null
@@ -1,366 +0,0 @@
-'''
-ShuffleNetV2 in PyTorch.
-
-ShuffleNetV2是ShuffleNet的改进版本,通过实验总结出了四个高效网络设计的实用准则:
-1. 输入输出通道数相等时计算量最小
-2. 过度使用组卷积会增加MAC(内存访问代价)
-3. 网络碎片化会降低并行度
-4. Element-wise操作不可忽视
-
-主要改进:
-1. 通道分离(Channel Split)替代组卷积
-2. 重新设计了基本单元,使输入输出通道数相等
-3. 每个阶段使用不同的通道数配置
-4. 简化了下采样模块的设计
-
-Reference:
-[1] Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, Jian Sun
-    ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. ECCV 2018.
-'''
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class ShuffleBlock(nn.Module):
-    """通道重排模块
-    
-    通过重新排列通道的顺序来实现不同特征的信息交流。
-    
-    Args:
-        groups (int): 分组数量,默认为2
-    """
-    def __init__(self, groups=2):
-        super(ShuffleBlock, self).__init__()
-        self.groups = groups
-
-    def forward(self, x):
-        """通道重排的前向传播
-        
-        步骤:
-        1. [N,C,H,W] -> [N,g,C/g,H,W]  # 重塑为g组
-        2. [N,g,C/g,H,W] -> [N,C/g,g,H,W]  # 转置g维度
-        3. [N,C/g,g,H,W] -> [N,C,H,W]  # 重塑回原始形状
-        
-        Args:
-            x: 输入张量,[N,C,H,W]
-            
-        Returns:
-            out: 通道重排后的张量,[N,C,H,W]
-        """
-        N, C, H, W = x.size()
-        g = self.groups
-        return x.view(N, g, C//g, H, W).permute(0, 2, 1, 3, 4).reshape(N, C, H, W)
-
-
-class SplitBlock(nn.Module):
-    """通道分离模块
-    
-    将输入特征图按比例分成两部分。
-    
-    Args:
-        ratio (float): 分离比例,默认为0.5
-    """
-    def __init__(self, ratio):
-        super(SplitBlock, self).__init__()
-        self.ratio = ratio
-
-    def forward(self, x):
-        """通道分离的前向传播
-        
-        Args:
-            x: 输入张量,[N,C,H,W]
-            
-        Returns:
-            tuple: 分离后的两个张量,[N,C1,H,W]和[N,C2,H,W]
-        """
-        c = int(x.size(1) * self.ratio)
-        return x[:, :c, :, :], x[:, c:, :, :]
-
-
-class BasicBlock(nn.Module):
-    """ShuffleNetV2的基本模块
-    
-    结构:
-    x -------|-----------------|
-      |      |                |
-      |      1x1 Conv         |
-      |      3x3 DWConv       |
-      |      1x1 Conv         |
-      |                       |
-      |------------------Concat
-                          |
-                      Channel Shuffle
-                          
-    Args:
-        in_channels (int): 输入通道数
-        split_ratio (float): 通道分离比例,默认为0.5
-    """
-    def __init__(self, in_channels, split_ratio=0.5):
-        super(BasicBlock, self).__init__()
-        self.split = SplitBlock(split_ratio)
-        in_channels = int(in_channels * split_ratio)
-        
-        # 主分支
-        self.conv1 = nn.Conv2d(in_channels, in_channels,
-                              kernel_size=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(in_channels)
-        
-        self.conv2 = nn.Conv2d(in_channels, in_channels,
-                              kernel_size=3, stride=1, padding=1, 
-                              groups=in_channels, bias=False)
-        self.bn2 = nn.BatchNorm2d(in_channels)
-        
-        self.conv3 = nn.Conv2d(in_channels, in_channels,
-                              kernel_size=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(in_channels)
-        
-        self.shuffle = ShuffleBlock()
-
-    def forward(self, x):
-        # 通道分离
-        x1, x2 = self.split(x)
-        
-        # 主分支
-        out = F.relu(self.bn1(self.conv1(x2)))
-        out = self.bn2(self.conv2(out))
-        out = F.relu(self.bn3(self.conv3(out)))
-        
-        # 拼接并重排
-        out = torch.cat([x1, out], 1)
-        out = self.shuffle(out)
-        return out
-
-
-class DownBlock(nn.Module):
-    """下采样模块
-    
-    结构:
-           3x3 DWConv(s=2)     1x1 Conv
-    x -----> 1x1 Conv          3x3 DWConv(s=2)
-                               1x1 Conv
-                                  |
-                              Concat
-                                  |
-                          Channel Shuffle
-                          
-    Args:
-        in_channels (int): 输入通道数
-        out_channels (int): 输出通道数
-    """
-    def __init__(self, in_channels, out_channels):
-        super(DownBlock, self).__init__()
-        mid_channels = out_channels // 2
-        
-        # 左分支
-        self.branch1 = nn.Sequential(
-            # 3x3深度可分离卷积,步长为2
-            nn.Conv2d(in_channels, in_channels,
-                     kernel_size=3, stride=2, padding=1, 
-                     groups=in_channels, bias=False),
-            nn.BatchNorm2d(in_channels),
-            # 1x1卷积
-            nn.Conv2d(in_channels, mid_channels,
-                     kernel_size=1, bias=False),
-            nn.BatchNorm2d(mid_channels)
-        )
-        
-        # 右分支
-        self.branch2 = nn.Sequential(
-            # 1x1卷积
-            nn.Conv2d(in_channels, mid_channels,
-                     kernel_size=1, bias=False),
-            nn.BatchNorm2d(mid_channels),
-            # 3x3深度可分离卷积,步长为2
-            nn.Conv2d(mid_channels, mid_channels,
-                     kernel_size=3, stride=2, padding=1,
-                     groups=mid_channels, bias=False),
-            nn.BatchNorm2d(mid_channels),
-            # 1x1卷积
-            nn.Conv2d(mid_channels, mid_channels,
-                     kernel_size=1, bias=False),
-            nn.BatchNorm2d(mid_channels)
-        )
-        
-        self.shuffle = ShuffleBlock()
-
-    def forward(self, x):
-        # 左分支
-        out1 = self.branch1(x)
-        
-        # 右分支
-        out2 = self.branch2(x)
-        
-        # 拼接并重排
-        out = torch.cat([out1, out2], 1)
-        out = self.shuffle(out)
-        return out
-
-
-class ShuffleNetV2(nn.Module):
-    """ShuffleNetV2模型
-    
-    网络结构:
-    1. 一个卷积层进行特征提取
-    2. 三个阶段,每个阶段包含多个基本块和一个下采样块
-    3. 最后一个卷积层
-    4. 平均池化和全连接层进行分类
-    
-    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 = 0.5, num_classes = 10):
-        super(ShuffleNetV2, self).__init__()
-        out_channels = configs[net_size]['out_channels']
-        num_blocks = configs[net_size]['num_blocks']
-
-        # 第一层卷积
-        self.conv1 = nn.Conv2d(3, 24, kernel_size=3,
-                              stride=1, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(24)
-        self.in_channels = 24
-        
-        # 三个阶段
-        self.layer1 = self._make_layer(out_channels[0], num_blocks[0])
-        self.layer2 = self._make_layer(out_channels[1], num_blocks[1])
-        self.layer3 = self._make_layer(out_channels[2], num_blocks[2])
-        
-        # 最后的1x1卷积
-        self.conv2 = nn.Conv2d(out_channels[2], out_channels[3],
-                              kernel_size=1, stride=1, padding=0, bias=False)
-        self.bn2 = nn.BatchNorm2d(out_channels[3])
-        
-        # 分类层
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.classifier = nn.Linear(out_channels[3], num_classes)
-        
-        # 初始化权重
-        self._initialize_weights()
-
-    def _make_layer(self, out_channels, num_blocks):
-        """构建一个阶段
-        
-        Args:
-            out_channels (int): 输出通道数
-            num_blocks (int): 基本块的数量
-            
-        Returns:
-            nn.Sequential: 一个阶段的层序列
-        """
-        layers = [DownBlock(self.in_channels, out_channels)]
-        for i in range(num_blocks):
-            layers.append(BasicBlock(out_channels))
-            self.in_channels = out_channels
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        """前向传播
-        
-        Args:
-            x: 输入张量,[N,3,32,32]
-            
-        Returns:
-            out: 输出张量,[N,num_classes]
-        """
-        # 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)
-        out = out.view(out.size(0), -1)
-        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
-        
-        采用kaiming初始化方法:
-        - 卷积层权重采用kaiming_normal_初始化
-        - BN层参数采用常数初始化
-        - 线性层采用正态分布初始化
-        """
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.BatchNorm2d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                nn.init.constant_(m.bias, 0)
-
-
-# 不同大小的网络配置
-configs = {
-    0.5: {
-        'out_channels': (48, 96, 192, 1024),
-        'num_blocks': (3, 7, 3)
-    },
-    1.0: {
-        'out_channels': (116, 232, 464, 1024),
-        'num_blocks': (3, 7, 3)
-    },
-    1.5: {
-        'out_channels': (176, 352, 704, 1024),
-        'num_blocks': (3, 7, 3)
-    },
-    2.0: {
-        'out_channels': (224, 488, 976, 2048),
-        'num_blocks': (3, 7, 3)
-    }
-}
-
-
-def test():
-    """测试函数"""
-    # 创建模型
-    net = ShuffleNetV2(net_size=0.5)
-    print('Model Structure:')
-    print(net)
-    
-    # 测试前向传播
-    x = torch.randn(1,3,32,32)
-    y = net(x)
-    print('\nInput Shape:', x.shape)
-    print('Output Shape:', y.shape)
-    
-    # 打印模型信息
-    from torchinfo import summary
-    device = 'cuda' if torch.cuda.is_available() else 'cpu'
-    net = net.to(device)
-    summary(net, (1,3,32,32))
-
-
-if __name__ == '__main__':
-    test()
\ No newline at end of file
diff --git a/Image/ShuffleNetv2/code/train.py b/Image/ShuffleNetv2/code/train.py
deleted file mode 100644
index d54df9e801b9bda7921befe94ad61978f0c8f2de..0000000000000000000000000000000000000000
--- a/Image/ShuffleNetv2/code/train.py
+++ /dev/null
@@ -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 ShuffleNetv2
-
-def main():
-    # 解析命令行参数
-    args = parse_args()
-    
-    # 创建模型
-    model = ShuffleNetv2()
-    
-    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='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,
-            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='shufflenetv2',
-            batch_size=args.batch_size, 
-            num_workers=args.num_workers,
-            local_dataset_path=args.dataset_path
-        )
-
-if __name__ == '__main__':
-    main()
diff --git a/Image/ShuffleNetv2/dataset/.gitkeep b/Image/ShuffleNetv2/dataset/.gitkeep
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/Image/ShuffleNetv2/model/.gitkeep b/Image/ShuffleNetv2/model/.gitkeep
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/Image/utils/dataset_utils.py b/Image/utils/dataset_utils.py
deleted file mode 100644
index 1acd0e039a4b0e3bc74ca4a9d2de58471380d517..0000000000000000000000000000000000000000
--- a/Image/utils/dataset_utils.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import torch
-import torchvision
-import torchvision.transforms as transforms
-import os
-
-def get_cifar10_dataloaders(batch_size=128, num_workers=2, local_dataset_path=None,shuffle=True):
-    """获取CIFAR10数据集的数据加载器
-    
-    Args:
-        batch_size: 批次大小
-        num_workers: 数据加载的工作进程数
-        local_dataset_path: 本地数据集路径，如果提供则使用本地数据集，否则下载
-        
-    Returns:
-        trainloader: 训练数据加载器
-        testloader: 测试数据加载器
-    """
-    # 数据预处理
-    transform_train = transforms.Compose([
-        transforms.RandomCrop(32, padding=4),
-        transforms.RandomHorizontalFlip(),
-        transforms.ToTensor(),
-        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
-    ])
-
-    transform_test = transforms.Compose([
-        transforms.ToTensor(),
-        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
-    ])
-
-    # 设置数据集路径
-    if local_dataset_path:
-        print(f"使用本地数据集: {local_dataset_path}")
-        download = False
-        dataset_path = local_dataset_path
-    else:
-        print("未指定本地数据集路径，将下载数据集")
-        download = True
-        dataset_path = '../dataset'
-
-    # 创建数据集路径
-    if not os.path.exists(dataset_path):
-        os.makedirs(dataset_path)
-
-    trainset = torchvision.datasets.CIFAR10(
-        root=dataset_path, train=True, download=download, transform=transform_train)
-    trainloader = torch.utils.data.DataLoader(
-        trainset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
-
-    testset = torchvision.datasets.CIFAR10(
-        root=dataset_path, train=False, download=download, transform=transform_test)
-    testloader = torch.utils.data.DataLoader(
-        testset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
-
-    return trainloader, testloader
-
-def get_mnist_dataloaders(batch_size=128, num_workers=2, local_dataset_path=None,shuffle=True):
-    """获取MNIST数据集的数据加载器
-    
-    Args:
-        batch_size: 批次大小
-        num_workers: 数据加载的工作进程数
-        local_dataset_path: 本地数据集路径，如果提供则使用本地数据集，否则下载
-        
-    Returns:
-        trainloader: 训练数据加载器
-        testloader: 测试数据加载器
-    """
-    # 数据预处理
-    transform_train = transforms.Compose([
-        transforms.RandomRotation(10),     # 随机旋转±10度
-        transforms.RandomAffine(          # 随机仿射变换
-            degrees=0,                    # 不进行旋转
-            translate=(0.1, 0.1),         # 平移范围
-            scale=(0.9, 1.1)             # 缩放范围
-        ),
-        transforms.ToTensor(),
-        transforms.Normalize((0.1307,), (0.3081,))  # MNIST数据集的均值和标准差
-    ])
-
-    transform_test = transforms.Compose([
-        transforms.ToTensor(),
-        transforms.Normalize((0.1307,), (0.3081,))
-    ])
-
-    # 设置数据集路径
-    if local_dataset_path:
-        print(f"使用本地数据集: {local_dataset_path}")
-        download = False
-        dataset_path = local_dataset_path
-    else:
-        print("未指定本地数据集路径，将下载数据集")
-        download = True
-        dataset_path = '../dataset'
-
-    # 创建数据集路径
-    if not os.path.exists(dataset_path):
-        os.makedirs(dataset_path)
-
-    trainset = torchvision.datasets.MNIST(
-        root=dataset_path, train=True, download=download, transform=transform_train)
-    trainloader = torch.utils.data.DataLoader(
-        trainset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
-
-    testset = torchvision.datasets.MNIST(
-        root=dataset_path, train=False, download=download, transform=transform_test)
-    testloader = torch.utils.data.DataLoader(
-        testset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
-
-    return trainloader, testloader
diff --git a/Image/utils/parse_args.py b/Image/utils/parse_args.py
deleted file mode 100644
index 998e17d974a5bba504540be0859442da99dd45a9..0000000000000000000000000000000000000000
--- a/Image/utils/parse_args.py
+++ /dev/null
@@ -1,19 +0,0 @@
-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')
-    parser.add_argument('--dataset-path', type=str, default=None, help='本地数据集路径，如果不指定则自动下载')
-    return parser.parse_args()
\ No newline at end of file
diff --git a/Image/utils/train_utils.py b/Image/utils/train_utils.py
deleted file mode 100644
index 23ee83897d9d11ee02de5575c9d3655be46847fa..0000000000000000000000000000000000000000
--- a/Image/utils/train_utils.py
+++ /dev/null
@@ -1,381 +0,0 @@
-"""
-通用模型训练工具
-
-提供了模型训练、评估、保存等功能，支持:
-1. 训练进度可视化
-2. 日志记录
-3. 模型检查点保存
-4. 嵌入向量收集
-"""
-
-import torch
-import torch.nn as nn
-import torch.optim as optim
-import time
-import os
-import logging
-import numpy as np
-from tqdm import tqdm
-import sys
-from pathlib import Path
-import torch.nn.functional as F
-import torchvision.transforms as transforms
-
-# 将项目根目录添加到Python路径中
-current_dir = Path(__file__).resolve().parent
-project_root = current_dir.parent.parent
-sys.path.append(str(project_root))
-
-from ttv_utils import time_travel_saver
-
-def setup_logger(log_file):
-    """配置日志记录器，如果日志文件存在则覆盖
-    
-    Args:
-        log_file: 日志文件路径
-        
-    Returns:
-        logger: 配置好的日志记录器
-    """
-    # 创建logger
-    logger = logging.getLogger('train')
-    logger.setLevel(logging.INFO)
-    
-    # 移除现有的处理器
-    if logger.hasHandlers():
-        logger.handlers.clear()
-    
-    # 创建文件处理器，使用'w'模式覆盖现有文件
-    fh = logging.FileHandler(log_file, mode='w')
-    fh.setLevel(logging.INFO)
-    
-    # 创建控制台处理器
-    ch = logging.StreamHandler()
-    ch.setLevel(logging.INFO)
-    
-    # 创建格式器
-    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-    fh.setFormatter(formatter)
-    ch.setFormatter(formatter)
-    
-    # 添加处理器
-    logger.addHandler(fh)
-    logger.addHandler(ch)
-    
-    return logger
-
-def train_model(model, trainloader, testloader, epochs=200, lr=0.1, device='cuda:0',
-                save_dir='./checkpoints', model_name='model', save_type='0',layer_name=None,interval = 2):
-    """通用的模型训练函数
-    Args:
-        model: 要训练的模型
-        trainloader: 训练数据加载器
-        testloader: 测试数据加载器
-        epochs: 训练轮数
-        lr: 学习率
-        device: 训练设备，格式为'cuda:N'，其中N为GPU编号（0,1,2,3）
-        save_dir: 模型保存目录
-        model_name: 模型名称
-        save_type: 保存类型，0为普通训练，1为数据增强训练，2为后门训练
-    """
-    # 检查并设置GPU设备
-    if not torch.cuda.is_available():
-        print("CUDA不可用，将使用CPU训练")
-        device = 'cpu'
-    elif not device.startswith('cuda:'):
-        device = f'cuda:0'
-    
-    # 确保device格式正确
-    if device.startswith('cuda:'):
-        gpu_id = int(device.split(':')[1])
-        if gpu_id >= torch.cuda.device_count():
-            print(f"GPU {gpu_id} 不可用，将使用GPU 0")
-            device = 'cuda:0'
-    
-    # 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 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)
-    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=200)
-    
-    # 移动模型到指定设备
-    model = model.to(device)
-    best_acc = 0
-    start_time = time.time()
-    
-    logger.info(f'开始训练 {model_name}')
-    logger.info(f'总轮数: {epochs}, 学习率: {lr}, 设备: {device}')
-    
-    for epoch in range(epochs):
-        # 训练阶段
-        model.train()
-        train_loss = 0
-        correct = 0
-        total = 0
-        
-        train_pbar = tqdm(trainloader, desc=f'Epoch {epoch+1}/{epochs} [Train]')
-        for batch_idx, (inputs, targets) in enumerate(train_pbar):
-            inputs, targets = inputs.to(device), targets.to(device)
-            optimizer.zero_grad()
-            outputs = model(inputs)
-            loss = criterion(outputs, targets)
-            loss.backward()
-            optimizer.step()
-            
-            train_loss += loss.item()
-            _, predicted = outputs.max(1)
-            total += targets.size(0)
-            correct += predicted.eq(targets).sum().item()
-            
-            # 更新进度条
-            train_pbar.set_postfix({
-                'loss': f'{train_loss/(batch_idx+1):.3f}',
-                'acc': f'{100.*correct/total:.2f}%'
-            })
-        
-            # 每100步记录一次
-            if batch_idx % 100 == 0:
-                logger.info(f'Epoch: {epoch+1} | Batch: {batch_idx} | '
-                          f'Loss: {train_loss/(batch_idx+1):.3f} | '
-                          f'Acc: {100.*correct/total:.2f}%')
-        
-        # 测试阶段
-        model.eval()
-        test_loss = 0
-        correct = 0
-        total = 0
-        
-        test_pbar = tqdm(testloader, desc=f'Epoch {epoch+1}/{epochs} [Test]')
-        with torch.no_grad():
-            for batch_idx, (inputs, targets) in enumerate(test_pbar):
-                inputs, targets = inputs.to(device), targets.to(device)
-                outputs = model(inputs)
-                loss = criterion(outputs, targets)
-                
-                test_loss += loss.item()
-                _, predicted = outputs.max(1)
-                total += targets.size(0)
-                correct += predicted.eq(targets).sum().item()
-        
-                # 更新进度条
-                test_pbar.set_postfix({
-                    'loss': f'{test_loss/(batch_idx+1):.3f}',
-                    'acc': f'{100.*correct/total:.2f}%'
-                })
-        
-        # 计算测试精度
-        acc = 100.*correct/total
-        logger.info(f'Epoch: {epoch+1} | Test Loss: {test_loss/(batch_idx+1):.3f} | '
-                   f'Test Acc: {acc:.2f}%')
-        
-
-        if epoch == 0:
-            ordered_loader = torch.utils.data.DataLoader(
-                trainloader.dataset,  # 使用相同的数据集
-                batch_size=trainloader.batch_size,
-                shuffle=False,  # 确保顺序加载
-                num_workers=trainloader.num_workers
-            )
-            save_model = time_travel_saver(model, ordered_loader, device, save_dir, model_name, interval = 1, auto_save_embedding = True, layer_name = layer_name, show= True )
-
-        # 每5个epoch保存一次
-        if (epoch + 1) % interval == 0:
-            # 创建一个专门用于收集embedding的顺序dataloader
-            ordered_loader = torch.utils.data.DataLoader(
-                trainloader.dataset,  # 使用相同的数据集
-                batch_size=trainloader.batch_size,
-                shuffle=False,  # 确保顺序加载
-                num_workers=trainloader.num_workers
-            )
-            save_model = time_travel_saver(model, ordered_loader, device, save_dir, model_name, interval = 1, auto_save_embedding = True, layer_name = layer_name )
-            save_model.save()
-            
-        scheduler.step()
-    
-    logger.info('训练完成!')
-
-def train_model_data_augmentation(model, epochs=200, lr=0.1, device='cuda:0',
-                                save_dir='./checkpoints', model_name='model',
-                                batch_size=128, num_workers=2, local_dataset_path=None):
-    """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
-        local_dataset_path: 本地数据集路径
-    """
-    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,
-            contrast=0.2,
-            saturation=0.2,
-            hue=0.1
-        ),
-        transforms.RandomPerspective(distortion_scale=0.2, p=0.5),
-        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))
-    ])
-    
-    # 获取数据加载器
-    trainloader, testloader = get_cifar10_dataloaders(batch_size, num_workers, local_dataset_path)
-    
-    # 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',
-                       batch_size=128, num_workers=2, local_dataset_path=None, layer_name=None,interval = 2):
-    """训练带后门的模型
-    
-    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
-        local_dataset_path: 本地数据集路径
-    """
-    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, local_dataset_path)
-    
-    # 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', layer_name=layer_name,interval = interval)
-    
-    # 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
-    # 使用正确的方式进行图像标准化
-    normalize = transforms.Normalize(mean=(0.4914, 0.4822, 0.4465),
-                                 std=(0.2023, 0.1994, 0.2010))
-    trigger_validation = normalize(trigger_validation)
-    
-    # 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}%')
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+2025-04-27 21:40:38,199 - train - INFO - 开始训练 ShuffleNetG2_Backdoored
+2025-04-27 21:40:38,199 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:4
+2025-04-27 21:41:00,561 - train - INFO - Epoch: 1 | Train Loss: 3.474 | Train Acc: 24.75% | Test Loss: 1.839 | Test Acc: 28.61%
+2025-04-27 21:41:29,951 - train - INFO - Epoch: 2 | Train Loss: 1.737 | Train Acc: 34.61% | Test Loss: 1.647 | Test Acc: 38.76%
+2025-04-27 21:42:02,038 - train - INFO - Epoch: 3 | Train Loss: 1.617 | Train Acc: 40.02% | Test Loss: 1.546 | Test Acc: 43.38%
+2025-04-27 21:42:26,242 - train - INFO - Epoch: 4 | Train Loss: 1.524 | Train Acc: 44.10% | Test Loss: 1.575 | Test Acc: 44.46%
+2025-04-27 21:42:58,068 - train - INFO - Epoch: 5 | Train Loss: 1.422 | Train Acc: 48.63% | Test Loss: 1.378 | Test Acc: 51.63%
+2025-04-27 21:43:21,960 - train - INFO - Epoch: 6 | Train Loss: 1.340 | Train Acc: 52.04% | Test Loss: 1.617 | Test Acc: 46.62%
+2025-04-27 21:43:52,643 - train - INFO - Epoch: 7 | Train Loss: 1.099 | Train Acc: 61.16% | Test Loss: 1.265 | Test Acc: 55.45%
+2025-04-27 21:44:17,513 - train - INFO - Epoch: 8 | Train Loss: 0.981 | Train Acc: 65.56% | Test Loss: 1.132 | Test Acc: 60.53%
+2025-04-27 21:44:48,894 - train - INFO - Epoch: 9 | Train Loss: 0.905 | Train Acc: 68.37% | Test Loss: 1.157 | Test Acc: 59.89%
+2025-04-27 21:45:13,975 - train - INFO - Epoch: 10 | Train Loss: 0.857 | Train Acc: 70.02% | Test Loss: 1.498 | Test Acc: 53.97%
+2025-04-27 21:45:45,381 - train - INFO - Epoch: 11 | Train Loss: 0.820 | Train Acc: 71.46% | Test Loss: 1.215 | Test Acc: 57.59%
+2025-04-27 21:46:10,424 - train - INFO - Epoch: 12 | Train Loss: 0.795 | Train Acc: 72.13% | Test Loss: 1.302 | Test Acc: 56.87%
+2025-04-27 21:46:41,074 - train - INFO - Epoch: 13 | Train Loss: 0.761 | Train Acc: 73.58% | Test Loss: 1.503 | Test Acc: 53.30%
+2025-04-27 21:47:06,315 - train - INFO - Epoch: 14 | Train Loss: 0.717 | Train Acc: 75.01% | Test Loss: 1.304 | Test Acc: 58.04%
+2025-04-27 21:47:37,717 - train - INFO - Epoch: 15 | Train Loss: 0.682 | Train Acc: 76.37% | Test Loss: 1.118 | Test Acc: 62.69%
+2025-04-27 21:48:02,705 - train - INFO - Epoch: 16 | Train Loss: 0.639 | Train Acc: 77.89% | Test Loss: 1.066 | Test Acc: 63.85%
+2025-04-27 21:48:34,002 - train - INFO - Epoch: 17 | Train Loss: 0.618 | Train Acc: 78.54% | Test Loss: 2.158 | Test Acc: 44.77%
+2025-04-27 21:49:01,394 - train - INFO - Epoch: 18 | Train Loss: 0.599 | Train Acc: 79.24% | Test Loss: 1.212 | Test Acc: 62.74%
+2025-04-27 21:49:36,490 - train - INFO - Epoch: 19 | Train Loss: 0.579 | Train Acc: 79.96% | Test Loss: 0.908 | Test Acc: 69.82%
+2025-04-27 21:50:05,348 - train - INFO - Epoch: 20 | Train Loss: 0.553 | Train Acc: 80.95% | Test Loss: 0.756 | Test Acc: 73.69%
+2025-04-27 21:50:40,290 - train - INFO - Epoch: 21 | Train Loss: 0.523 | Train Acc: 81.96% | Test Loss: 0.763 | Test Acc: 74.51%
+2025-04-27 21:51:09,386 - train - INFO - Epoch: 22 | Train Loss: 0.507 | Train Acc: 82.56% | Test Loss: 0.774 | Test Acc: 73.58%
+2025-04-27 21:51:44,124 - train - INFO - Epoch: 23 | Train Loss: 0.489 | Train Acc: 82.98% | Test Loss: 0.849 | Test Acc: 71.12%
+2025-04-27 21:52:37,600 - train - INFO - Epoch: 24 | Train Loss: 0.477 | Train Acc: 83.62% | Test Loss: 0.881 | Test Acc: 71.81%
+2025-04-27 21:53:37,849 - train - INFO - Epoch: 25 | Train Loss: 0.461 | Train Acc: 84.17% | Test Loss: 0.785 | Test Acc: 73.05%
+2025-04-27 21:54:35,822 - train - INFO - Epoch: 26 | Train Loss: 0.432 | Train Acc: 85.19% | Test Loss: 0.731 | Test Acc: 75.86%
+2025-04-27 21:55:36,342 - train - INFO - Epoch: 27 | Train Loss: 0.418 | Train Acc: 85.77% | Test Loss: 0.782 | Test Acc: 74.19%
+2025-04-27 21:56:35,067 - train - INFO - Epoch: 28 | Train Loss: 0.396 | Train Acc: 86.26% | Test Loss: 0.886 | Test Acc: 71.78%
+2025-04-27 21:57:35,551 - train - INFO - Epoch: 29 | Train Loss: 0.377 | Train Acc: 87.02% | Test Loss: 0.670 | Test Acc: 78.04%
+2025-04-27 21:58:34,069 - train - INFO - Epoch: 30 | Train Loss: 0.346 | Train Acc: 88.02% | Test Loss: 0.638 | Test Acc: 78.38%
+2025-04-27 21:59:34,414 - train - INFO - Epoch: 31 | Train Loss: 0.326 | Train Acc: 88.65% | Test Loss: 0.666 | Test Acc: 77.88%
+2025-04-27 22:00:32,776 - train - INFO - Epoch: 32 | Train Loss: 0.295 | Train Acc: 89.69% | Test Loss: 0.700 | Test Acc: 77.59%
+2025-04-27 22:01:33,436 - train - INFO - Epoch: 33 | Train Loss: 0.276 | Train Acc: 90.38% | Test Loss: 0.612 | Test Acc: 80.60%
+2025-04-27 22:02:31,395 - train - INFO - Epoch: 34 | Train Loss: 0.240 | Train Acc: 91.67% | Test Loss: 0.616 | Test Acc: 80.65%
+2025-04-27 22:03:32,240 - train - INFO - Epoch: 35 | Train Loss: 0.220 | Train Acc: 92.29% | Test Loss: 0.603 | Test Acc: 81.79%
+2025-04-27 22:04:30,143 - train - INFO - Epoch: 36 | Train Loss: 0.186 | Train Acc: 93.63% | Test Loss: 0.676 | Test Acc: 80.23%
+2025-04-27 22:05:31,032 - train - INFO - Epoch: 37 | Train Loss: 0.160 | Train Acc: 94.49% | Test Loss: 0.609 | Test Acc: 82.13%
+2025-04-27 22:06:28,600 - train - INFO - Epoch: 38 | Train Loss: 0.121 | Train Acc: 95.93% | Test Loss: 0.632 | Test Acc: 82.22%
+2025-04-27 22:07:29,459 - train - INFO - Epoch: 39 | Train Loss: 0.097 | Train Acc: 96.72% | Test Loss: 0.659 | Test Acc: 81.73%
+2025-04-27 22:08:27,686 - train - INFO - Epoch: 40 | Train Loss: 0.060 | Train Acc: 98.16% | Test Loss: 0.633 | Test Acc: 83.40%
+2025-04-27 22:09:28,435 - train - INFO - Epoch: 41 | Train Loss: 0.035 | Train Acc: 99.08% | Test Loss: 0.652 | Test Acc: 83.47%
+2025-04-27 22:10:26,183 - train - INFO - Epoch: 42 | Train Loss: 0.019 | Train Acc: 99.66% | Test Loss: 0.646 | Test Acc: 84.09%
+2025-04-27 22:11:26,871 - train - INFO - Epoch: 43 | Train Loss: 0.011 | Train Acc: 99.87% | Test Loss: 0.651 | Test Acc: 84.31%
+2025-04-27 22:12:24,865 - train - INFO - Epoch: 44 | Train Loss: 0.008 | Train Acc: 99.94% | Test Loss: 0.653 | Test Acc: 84.36%
+2025-04-27 22:13:25,915 - train - INFO - Epoch: 45 | Train Loss: 0.005 | Train Acc: 99.98% | Test Loss: 0.654 | Test Acc: 84.36%
+2025-04-27 22:14:23,679 - train - INFO - Epoch: 46 | Train Loss: 0.005 | Train Acc: 99.99% | Test Loss: 0.652 | Test Acc: 84.46%
+2025-04-27 22:15:24,322 - train - INFO - Epoch: 47 | Train Loss: 0.004 | Train Acc: 99.99% | Test Loss: 0.642 | Test Acc: 84.55%
+2025-04-27 22:15:55,762 - train - INFO - Epoch: 48 | Train Loss: 0.004 | Train Acc: 99.99% | Test Loss: 0.654 | Test Acc: 84.33%
+2025-04-27 22:16:30,703 - train - INFO - Epoch: 49 | Train Loss: 0.004 | Train Acc: 99.99% | Test Loss: 0.649 | Test Acc: 84.51%
+2025-04-27 22:16:59,522 - train - INFO - Epoch: 50 | Train Loss: 0.004 | Train Acc: 99.99% | Test Loss: 0.655 | Test Acc: 84.51%
+2025-04-27 22:17:06,492 - train - INFO - 训练完成!
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+2025-04-27 21:58:31,464 - train - INFO - Epoch: 13 | Train Loss: 0.646 | Train Acc: 77.49% | Test Loss: 0.870 | Test Acc: 70.86%
+2025-04-27 21:59:29,595 - train - INFO - Epoch: 14 | Train Loss: 0.636 | Train Acc: 77.90% | Test Loss: 0.880 | Test Acc: 69.78%
+2025-04-27 22:00:30,054 - train - INFO - Epoch: 15 | Train Loss: 0.626 | Train Acc: 78.33% | Test Loss: 0.794 | Test Acc: 72.99%
+2025-04-27 22:01:28,338 - train - INFO - Epoch: 16 | Train Loss: 0.605 | Train Acc: 79.04% | Test Loss: 0.813 | Test Acc: 73.07%
+2025-04-27 22:02:28,703 - train - INFO - Epoch: 17 | Train Loss: 0.600 | Train Acc: 79.24% | Test Loss: 0.797 | Test Acc: 72.78%
+2025-04-27 22:03:27,042 - train - INFO - Epoch: 18 | Train Loss: 0.585 | Train Acc: 79.83% | Test Loss: 0.850 | Test Acc: 71.71%
+2025-04-27 22:04:26,465 - train - INFO - Epoch: 19 | Train Loss: 0.569 | Train Acc: 80.45% | Test Loss: 0.721 | Test Acc: 75.15%
+2025-04-27 22:05:24,872 - train - INFO - Epoch: 20 | Train Loss: 0.560 | Train Acc: 80.61% | Test Loss: 1.679 | Test Acc: 57.56%
+2025-04-27 22:06:25,237 - train - INFO - Epoch: 21 | Train Loss: 0.548 | Train Acc: 81.12% | Test Loss: 0.672 | Test Acc: 76.99%
+2025-04-27 22:07:23,305 - train - INFO - Epoch: 22 | Train Loss: 0.529 | Train Acc: 81.63% | Test Loss: 0.809 | Test Acc: 73.48%
+2025-04-27 22:08:23,889 - train - INFO - Epoch: 23 | Train Loss: 0.521 | Train Acc: 82.03% | Test Loss: 0.645 | Test Acc: 77.97%
+2025-04-27 22:09:21,810 - train - INFO - Epoch: 24 | Train Loss: 0.501 | Train Acc: 82.79% | Test Loss: 0.639 | Test Acc: 78.27%
+2025-04-27 22:10:21,911 - train - INFO - Epoch: 25 | Train Loss: 0.498 | Train Acc: 82.82% | Test Loss: 0.641 | Test Acc: 78.37%
+2025-04-27 22:11:20,147 - train - INFO - Epoch: 26 | Train Loss: 0.479 | Train Acc: 83.34% | Test Loss: 0.664 | Test Acc: 77.46%
+2025-04-27 22:12:20,464 - train - INFO - Epoch: 27 | Train Loss: 0.467 | Train Acc: 83.93% | Test Loss: 0.658 | Test Acc: 77.32%
+2025-04-27 22:13:18,407 - train - INFO - Epoch: 28 | Train Loss: 0.451 | Train Acc: 84.47% | Test Loss: 0.714 | Test Acc: 76.04%
+2025-04-27 22:14:18,600 - train - INFO - Epoch: 29 | Train Loss: 0.430 | Train Acc: 85.12% | Test Loss: 0.604 | Test Acc: 79.05%
+2025-04-27 22:15:16,869 - train - INFO - Epoch: 30 | Train Loss: 0.419 | Train Acc: 85.47% | Test Loss: 0.642 | Test Acc: 77.76%
+2025-04-27 22:15:50,303 - train - INFO - Epoch: 31 | Train Loss: 0.400 | Train Acc: 86.20% | Test Loss: 0.786 | Test Acc: 74.10%
+2025-04-27 22:16:12,006 - train - INFO - Epoch: 32 | Train Loss: 0.382 | Train Acc: 86.74% | Test Loss: 0.605 | Test Acc: 80.34%
+2025-04-27 22:16:42,790 - train - INFO - Epoch: 33 | Train Loss: 0.357 | Train Acc: 87.49% | Test Loss: 0.640 | Test Acc: 78.53%
+2025-04-27 22:17:06,155 - train - INFO - Epoch: 34 | Train Loss: 0.339 | Train Acc: 88.10% | Test Loss: 0.670 | Test Acc: 78.55%
+2025-04-27 22:17:34,442 - train - INFO - Epoch: 35 | Train Loss: 0.317 | Train Acc: 89.00% | Test Loss: 0.579 | Test Acc: 80.44%
+2025-04-27 22:17:56,801 - train - INFO - Epoch: 36 | Train Loss: 0.291 | Train Acc: 89.91% | Test Loss: 0.641 | Test Acc: 80.28%
+2025-04-27 22:18:24,333 - train - INFO - Epoch: 37 | Train Loss: 0.269 | Train Acc: 90.64% | Test Loss: 0.575 | Test Acc: 81.61%
+2025-04-27 22:18:46,370 - train - INFO - Epoch: 38 | Train Loss: 0.240 | Train Acc: 91.72% | Test Loss: 0.637 | Test Acc: 80.04%
+2025-04-27 22:19:10,769 - train - INFO - Epoch: 39 | Train Loss: 0.216 | Train Acc: 92.43% | Test Loss: 0.550 | Test Acc: 82.60%
+2025-04-27 22:19:32,311 - train - INFO - Epoch: 40 | Train Loss: 0.184 | Train Acc: 93.66% | Test Loss: 0.572 | Test Acc: 82.15%
+2025-04-27 22:19:58,808 - train - INFO - Epoch: 41 | Train Loss: 0.155 | Train Acc: 94.77% | Test Loss: 0.554 | Test Acc: 83.34%
+2025-04-27 22:20:20,113 - train - INFO - Epoch: 42 | Train Loss: 0.120 | Train Acc: 96.06% | Test Loss: 0.591 | Test Acc: 82.99%
+2025-04-27 22:20:47,293 - train - INFO - Epoch: 43 | Train Loss: 0.090 | Train Acc: 97.25% | Test Loss: 0.593 | Test Acc: 83.36%
+2025-04-27 22:21:09,047 - train - INFO - Epoch: 44 | Train Loss: 0.065 | Train Acc: 98.19% | Test Loss: 0.596 | Test Acc: 83.35%
+2025-04-27 22:21:36,303 - train - INFO - Epoch: 45 | Train Loss: 0.046 | Train Acc: 99.03% | Test Loss: 0.597 | Test Acc: 83.60%
+2025-04-27 22:21:57,517 - train - INFO - Epoch: 46 | Train Loss: 0.031 | Train Acc: 99.50% | Test Loss: 0.610 | Test Acc: 83.66%
+2025-04-27 22:22:23,316 - train - INFO - Epoch: 47 | Train Loss: 0.025 | Train Acc: 99.69% | Test Loss: 0.619 | Test Acc: 83.66%
+2025-04-27 22:22:45,152 - train - INFO - Epoch: 48 | Train Loss: 0.021 | Train Acc: 99.83% | Test Loss: 0.615 | Test Acc: 83.68%
+2025-04-27 22:23:12,049 - train - INFO - Epoch: 49 | Train Loss: 0.019 | Train Acc: 99.89% | Test Loss: 0.618 | Test Acc: 83.78%
+2025-04-27 22:23:34,213 - train - INFO - Epoch: 50 | Train Loss: 0.018 | Train Acc: 99.91% | Test Loss: 0.625 | Test Acc: 83.62%
+2025-04-27 22:23:40,090 - train - INFO - 训练完成!
diff --git a/ShuffleNetV2-CIFAR10/Classification-backdoor/scripts/get_representation.py b/ShuffleNetV2-CIFAR10/Classification-backdoor/scripts/get_representation.py
index 8abd44a73bab66b15ea35db96c5d40a34198759d..e87766dc95e13cd9a702638034bab0fc60d48ca8 100644
--- a/ShuffleNetV2-CIFAR10/Classification-backdoor/scripts/get_representation.py
+++ b/ShuffleNetV2-CIFAR10/Classification-backdoor/scripts/get_representation.py
@@ -52,7 +52,14 @@ class time_travel_saver:
         
         def get_activation(name):
             def hook(model, input, output):
-                activation[name] = output.detach()
+                # 只在需要时保存激活值，避免内存浪费
+                if name not in activation or activation[name] is None:
+                    # 处理元组类型的输出
+                    if isinstance(output, tuple):
+                        # 对于元组，我们只保存第一个元素或者创建一个新的列表
+                        activation[name] = output[0].detach() if len(output) > 0 else None
+                    else:
+                        activation[name] = output.detach()
             return hook
         
         # 注册钩子到所有层
@@ -112,7 +119,12 @@ class time_travel_saver:
             def hook(model, input, output):
                 # 只在需要时保存激活值，避免内存浪费
                 if name not in activation or activation[name] is None:
-                    activation[name] = output.detach()
+                    # 处理元组类型的输出
+                    if isinstance(output, tuple):
+                        # 对于元组，我们只保存第一个元素或者创建一个新的列表
+                        activation[name] = output[0].detach() if len(output) > 0 else None
+                    else:
+                        activation[name] = output.detach()
             return hook
         
         # 根据层的名称或维度来选择层
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+2025-04-27 22:21:19,645 - train - INFO - Epoch: 45 | Train Loss: 0.035 | Train Acc: 99.59% | Test Loss: 0.795 | Test Acc: 81.57%
+2025-04-27 22:21:42,418 - train - INFO - Epoch: 46 | Train Loss: 0.023 | Train Acc: 99.87% | Test Loss: 0.810 | Test Acc: 81.61%
+2025-04-27 22:22:10,870 - train - INFO - Epoch: 47 | Train Loss: 0.017 | Train Acc: 99.97% | Test Loss: 0.816 | Test Acc: 81.61%
+2025-04-27 22:22:33,452 - train - INFO - Epoch: 48 | Train Loss: 0.014 | Train Acc: 99.98% | Test Loss: 0.817 | Test Acc: 81.78%
+2025-04-27 22:23:01,910 - train - INFO - Epoch: 49 | Train Loss: 0.012 | Train Acc: 99.99% | Test Loss: 0.818 | Test Acc: 81.81%
+2025-04-27 22:23:23,874 - train - INFO - Epoch: 50 | Train Loss: 0.012 | Train Acc: 99.99% | Test Loss: 0.818 | Test Acc: 81.80%
+2025-04-27 22:23:29,595 - train - INFO - 训练完成!
diff --git a/ShuffleNetV2-CIFAR10/Classification-noisy/scripts/get_representation.py b/ShuffleNetV2-CIFAR10/Classification-noisy/scripts/get_representation.py
index 8abd44a73bab66b15ea35db96c5d40a34198759d..e87766dc95e13cd9a702638034bab0fc60d48ca8 100644
--- a/ShuffleNetV2-CIFAR10/Classification-noisy/scripts/get_representation.py
+++ b/ShuffleNetV2-CIFAR10/Classification-noisy/scripts/get_representation.py
@@ -52,7 +52,14 @@ class time_travel_saver:
         
         def get_activation(name):
             def hook(model, input, output):
-                activation[name] = output.detach()
+                # 只在需要时保存激活值，避免内存浪费
+                if name not in activation or activation[name] is None:
+                    # 处理元组类型的输出
+                    if isinstance(output, tuple):
+                        # 对于元组，我们只保存第一个元素或者创建一个新的列表
+                        activation[name] = output[0].detach() if len(output) > 0 else None
+                    else:
+                        activation[name] = output.detach()
             return hook
         
         # 注册钩子到所有层
@@ -112,7 +119,12 @@ class time_travel_saver:
             def hook(model, input, output):
                 # 只在需要时保存激活值，避免内存浪费
                 if name not in activation or activation[name] is None:
-                    activation[name] = output.detach()
+                    # 处理元组类型的输出
+                    if isinstance(output, tuple):
+                        # 对于元组，我们只保存第一个元素或者创建一个新的列表
+                        activation[name] = output[0].detach() if len(output) > 0 else None
+                    else:
+                        activation[name] = output.detach()
             return hook
         
         # 根据层的名称或维度来选择层
diff --git a/ShuffleNetV2-CIFAR10/Classification-normal/dataset/index.json b/ShuffleNetV2-CIFAR10/Classification-normal/dataset/index.json
index 18effc538161722b86c88b06392a77db3bb6a92d..4787cf6e039f8159f38952a979d98761ef29729e 100644
--- a/ShuffleNetV2-CIFAR10/Classification-normal/dataset/index.json
+++ b/ShuffleNetV2-CIFAR10/Classification-normal/dataset/index.json
@@ -50001,6 +50001,10007 @@
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     ],
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+    "test": [
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\ No newline at end of file
diff --git a/ShuffleNetV2-CIFAR10/Classification-normal/epochs/train.log b/ShuffleNetV2-CIFAR10/Classification-normal/epochs/train.log
new file mode 100644
index 0000000000000000000000000000000000000000..baf0aaecce190d4e6b11c1d66b5a9439078c7340
--- /dev/null
+++ b/ShuffleNetV2-CIFAR10/Classification-normal/epochs/train.log
@@ -0,0 +1,53 @@
+2025-04-27 21:48:34,123 - train - INFO - 开始训练 ShuffleNetV2
+2025-04-27 21:48:34,124 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:4
+2025-04-27 21:48:57,147 - train - INFO - Epoch: 1 | Train Loss: 1.750 | Train Acc: 36.67% | Test Loss: 1.334 | Test Acc: 51.14%
+2025-04-27 21:49:29,438 - train - INFO - Epoch: 2 | Train Loss: 1.245 | Train Acc: 54.99% | Test Loss: 1.219 | Test Acc: 57.90%
+2025-04-27 21:49:59,229 - train - INFO - Epoch: 3 | Train Loss: 1.044 | Train Acc: 62.94% | Test Loss: 1.183 | Test Acc: 60.26%
+2025-04-27 21:50:22,482 - train - INFO - Epoch: 4 | Train Loss: 0.912 | Train Acc: 67.72% | Test Loss: 1.067 | Test Acc: 63.11%
+2025-04-27 21:50:53,574 - train - INFO - Epoch: 5 | Train Loss: 0.831 | Train Acc: 70.86% | Test Loss: 0.889 | Test Acc: 69.46%
+2025-04-27 21:51:16,604 - train - INFO - Epoch: 6 | Train Loss: 0.786 | Train Acc: 72.68% | Test Loss: 0.993 | Test Acc: 66.94%
+2025-04-27 21:52:16,179 - train - INFO - Epoch: 7 | Train Loss: 0.756 | Train Acc: 73.51% | Test Loss: 0.866 | Test Acc: 71.14%
+2025-04-27 21:52:39,049 - train - INFO - Epoch: 8 | Train Loss: 0.744 | Train Acc: 73.95% | Test Loss: 0.805 | Test Acc: 72.38%
+2025-04-27 21:53:41,885 - train - INFO - Epoch: 9 | Train Loss: 0.727 | Train Acc: 74.61% | Test Loss: 0.896 | Test Acc: 69.35%
+2025-04-27 21:54:35,839 - train - INFO - Epoch: 10 | Train Loss: 0.715 | Train Acc: 75.12% | Test Loss: 0.786 | Test Acc: 73.31%
+2025-04-27 21:55:37,515 - train - INFO - Epoch: 11 | Train Loss: 0.703 | Train Acc: 75.45% | Test Loss: 1.241 | Test Acc: 60.57%
+2025-04-27 21:56:33,708 - train - INFO - Epoch: 12 | Train Loss: 0.684 | Train Acc: 76.34% | Test Loss: 0.794 | Test Acc: 72.77%
+2025-04-27 21:57:34,181 - train - INFO - Epoch: 13 | Train Loss: 0.676 | Train Acc: 76.50% | Test Loss: 0.748 | Test Acc: 74.38%
+2025-04-27 21:58:32,732 - train - INFO - Epoch: 14 | Train Loss: 0.672 | Train Acc: 76.68% | Test Loss: 0.728 | Test Acc: 75.10%
+2025-04-27 21:59:32,973 - train - INFO - Epoch: 15 | Train Loss: 0.655 | Train Acc: 77.36% | Test Loss: 0.782 | Test Acc: 72.96%
+2025-04-27 22:00:31,502 - train - INFO - Epoch: 16 | Train Loss: 0.648 | Train Acc: 77.60% | Test Loss: 0.789 | Test Acc: 72.56%
+2025-04-27 22:01:31,741 - train - INFO - Epoch: 17 | Train Loss: 0.635 | Train Acc: 77.97% | Test Loss: 0.749 | Test Acc: 74.50%
+2025-04-27 22:02:29,326 - train - INFO - Epoch: 18 | Train Loss: 0.629 | Train Acc: 78.28% | Test Loss: 0.735 | Test Acc: 75.62%
+2025-04-27 22:03:30,104 - train - INFO - Epoch: 19 | Train Loss: 0.618 | Train Acc: 78.62% | Test Loss: 0.704 | Test Acc: 76.90%
+2025-04-27 22:04:28,392 - train - INFO - Epoch: 20 | Train Loss: 0.608 | Train Acc: 78.89% | Test Loss: 0.688 | Test Acc: 76.81%
+2025-04-27 22:05:26,051 - train - INFO - Epoch: 21 | Train Loss: 0.597 | Train Acc: 79.47% | Test Loss: 0.691 | Test Acc: 76.24%
+2025-04-27 22:06:25,928 - train - INFO - Epoch: 22 | Train Loss: 0.585 | Train Acc: 79.82% | Test Loss: 0.698 | Test Acc: 76.32%
+2025-04-27 22:07:26,416 - train - INFO - Epoch: 23 | Train Loss: 0.577 | Train Acc: 80.19% | Test Loss: 0.634 | Test Acc: 78.30%
+2025-04-27 22:08:23,803 - train - INFO - Epoch: 24 | Train Loss: 0.563 | Train Acc: 80.57% | Test Loss: 0.629 | Test Acc: 78.43%
+2025-04-27 22:09:24,546 - train - INFO - Epoch: 25 | Train Loss: 0.555 | Train Acc: 80.70% | Test Loss: 0.594 | Test Acc: 79.46%
+2025-04-27 22:10:22,691 - train - INFO - Epoch: 26 | Train Loss: 0.540 | Train Acc: 81.34% | Test Loss: 0.689 | Test Acc: 76.42%
+2025-04-27 22:11:23,128 - train - INFO - Epoch: 27 | Train Loss: 0.530 | Train Acc: 81.87% | Test Loss: 0.684 | Test Acc: 77.55%
+2025-04-27 22:12:21,227 - train - INFO - Epoch: 28 | Train Loss: 0.515 | Train Acc: 82.16% | Test Loss: 0.570 | Test Acc: 80.85%
+2025-04-27 22:13:21,522 - train - INFO - Epoch: 29 | Train Loss: 0.502 | Train Acc: 82.38% | Test Loss: 0.622 | Test Acc: 78.76%
+2025-04-27 22:14:19,359 - train - INFO - Epoch: 30 | Train Loss: 0.487 | Train Acc: 83.15% | Test Loss: 0.531 | Test Acc: 81.79%
+2025-04-27 22:15:19,715 - train - INFO - Epoch: 31 | Train Loss: 0.473 | Train Acc: 83.66% | Test Loss: 0.513 | Test Acc: 82.67%
+2025-04-27 22:15:50,404 - train - INFO - Epoch: 32 | Train Loss: 0.458 | Train Acc: 84.13% | Test Loss: 0.542 | Test Acc: 81.31%
+2025-04-27 22:16:22,332 - train - INFO - Epoch: 33 | Train Loss: 0.448 | Train Acc: 84.68% | Test Loss: 0.480 | Test Acc: 83.89%
+2025-04-27 22:16:45,095 - train - INFO - Epoch: 34 | Train Loss: 0.428 | Train Acc: 85.26% | Test Loss: 0.480 | Test Acc: 83.49%
+2025-04-27 22:17:12,875 - train - INFO - Epoch: 35 | Train Loss: 0.415 | Train Acc: 85.71% | Test Loss: 0.481 | Test Acc: 83.75%
+2025-04-27 22:17:34,767 - train - INFO - Epoch: 36 | Train Loss: 0.401 | Train Acc: 86.07% | Test Loss: 0.465 | Test Acc: 84.62%
+2025-04-27 22:18:04,798 - train - INFO - Epoch: 37 | Train Loss: 0.379 | Train Acc: 86.98% | Test Loss: 0.432 | Test Acc: 85.45%
+2025-04-27 22:18:27,022 - train - INFO - Epoch: 38 | Train Loss: 0.362 | Train Acc: 87.42% | Test Loss: 0.417 | Test Acc: 85.50%
+2025-04-27 22:18:57,168 - train - INFO - Epoch: 39 | Train Loss: 0.346 | Train Acc: 87.94% | Test Loss: 0.443 | Test Acc: 85.16%
+2025-04-27 22:19:19,033 - train - INFO - Epoch: 40 | Train Loss: 0.331 | Train Acc: 88.50% | Test Loss: 0.405 | Test Acc: 86.21%
+2025-04-27 22:19:49,640 - train - INFO - Epoch: 41 | Train Loss: 0.313 | Train Acc: 89.15% | Test Loss: 0.391 | Test Acc: 86.79%
+2025-04-27 22:20:10,777 - train - INFO - Epoch: 42 | Train Loss: 0.294 | Train Acc: 89.89% | Test Loss: 0.373 | Test Acc: 87.43%
+2025-04-27 22:20:40,926 - train - INFO - Epoch: 43 | Train Loss: 0.280 | Train Acc: 90.33% | Test Loss: 0.372 | Test Acc: 87.43%
+2025-04-27 22:21:02,994 - train - INFO - Epoch: 44 | Train Loss: 0.264 | Train Acc: 90.74% | Test Loss: 0.368 | Test Acc: 87.24%
+2025-04-27 22:21:33,872 - train - INFO - Epoch: 45 | Train Loss: 0.249 | Train Acc: 91.34% | Test Loss: 0.356 | Test Acc: 87.97%
+2025-04-27 22:21:53,655 - train - INFO - Epoch: 46 | Train Loss: 0.236 | Train Acc: 91.81% | Test Loss: 0.345 | Test Acc: 88.35%
+2025-04-27 22:22:23,970 - train - INFO - Epoch: 47 | Train Loss: 0.226 | Train Acc: 92.28% | Test Loss: 0.342 | Test Acc: 88.45%
+2025-04-27 22:22:45,527 - train - INFO - Epoch: 48 | Train Loss: 0.217 | Train Acc: 92.62% | Test Loss: 0.342 | Test Acc: 88.99%
+2025-04-27 22:23:15,748 - train - INFO - Epoch: 49 | Train Loss: 0.210 | Train Acc: 92.88% | Test Loss: 0.340 | Test Acc: 88.74%
+2025-04-27 22:23:36,757 - train - INFO - Epoch: 50 | Train Loss: 0.207 | Train Acc: 93.05% | Test Loss: 0.339 | Test Acc: 88.86%
+2025-04-27 22:23:45,210 - train - INFO - 训练完成!
diff --git a/ShuffleNetV2-CIFAR10/Classification-normal/scripts/get_representation.py b/ShuffleNetV2-CIFAR10/Classification-normal/scripts/get_representation.py
index 76873d10a794e3d9eb315aad0a87b2a88a2f6b99..e87766dc95e13cd9a702638034bab0fc60d48ca8 100644
--- a/ShuffleNetV2-CIFAR10/Classification-normal/scripts/get_representation.py
+++ b/ShuffleNetV2-CIFAR10/Classification-normal/scripts/get_representation.py
@@ -52,7 +52,14 @@ class time_travel_saver:
         
         def get_activation(name):
             def hook(model, input, output):
-                activation[name] = output.detach()
+                # 只在需要时保存激活值，避免内存浪费
+                if name not in activation or activation[name] is None:
+                    # 处理元组类型的输出
+                    if isinstance(output, tuple):
+                        # 对于元组，我们只保存第一个元素或者创建一个新的列表
+                        activation[name] = output[0].detach() if len(output) > 0 else None
+                    else:
+                        activation[name] = output.detach()
             return hook
         
         # 注册钩子到所有层
@@ -112,7 +119,12 @@ class time_travel_saver:
             def hook(model, input, output):
                 # 只在需要时保存激活值，避免内存浪费
                 if name not in activation or activation[name] is None:
-                    activation[name] = output.detach()
+                    # 处理元组类型的输出
+                    if isinstance(output, tuple):
+                        # 对于元组，我们只保存第一个元素或者创建一个新的列表
+                        activation[name] = output[0].detach() if len(output) > 0 else None
+                    else:
+                        activation[name] = output.detach()
             return hook
         
         # 根据层的名称或维度来选择层
@@ -235,8 +247,8 @@ class time_travel_saver:
             
             # 创建索引字典
             index_dict = {
-                "train": indices,  # 所有数据默认为训练集
-                "test": [],        # 初始为空
+                "train": list(range(50000)),  # 所有数据默认为训练集
+                "test": list(range(50000, 60000)),  # 测试集索引从50000到59999
                 "validation": []   # 初始为空
             }