add ShuffleNet-CIFAR10
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- Image/ShuffleNetv2/code/model.py +0 -366
- Image/ShuffleNetv2/code/train.py +0 -59
- Image/ShuffleNetv2/dataset/.gitkeep +0 -0
- Image/ShuffleNetv2/model/.gitkeep +0 -0
- Image/utils/dataset_utils.py +0 -110
- Image/utils/parse_args.py +0 -19
- Image/utils/train_utils.py +0 -381
- ShuffleNet-CIFAR10/Classification-backdoor/dataset/backdoor_index.npy +1 -1
- ShuffleNet-CIFAR10/Classification-backdoor/dataset/labels.npy +1 -1
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_1/embeddings.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_1/model.pth +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_1/predictions.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_10/embeddings.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_10/model.pth +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_10/predictions.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_12/embeddings.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_12/model.pth +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_12/predictions.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_14/embeddings.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_14/model.pth +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_14/predictions.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_16/embeddings.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_16/model.pth +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_16/predictions.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_18/embeddings.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_18/model.pth +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_18/predictions.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_2/embeddings.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_2/model.pth +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_2/predictions.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_20/embeddings.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_20/model.pth +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_20/predictions.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_22/embeddings.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_22/model.pth +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_22/predictions.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_24/embeddings.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_24/model.pth +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_24/predictions.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_26/embeddings.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_26/model.pth +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_26/predictions.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_28/embeddings.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_28/model.pth +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_28/predictions.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_30/embeddings.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_30/model.pth +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_30/predictions.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_32/embeddings.npy +3 -0
- ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_32/model.pth +3 -0
Image/ShuffleNetv2/code/model.py
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'''
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ShuffleNetV2 in PyTorch.
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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:
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1. 输入输出通道数相等时计算量最小
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2. 过度使用组卷积会增加MAC(内存访问代价)
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3. 网络碎片化会降低并行度
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4. Element-wise操作不可忽视
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主要改进:
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1. 通道分离(Channel Split)替代组卷积
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2. 重新设计了基本单元,使输入输出通道数相等
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3. 每个阶段使用不同的通道数配置
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4. 简化了下采样模块的设计
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Reference:
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[1] Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, Jian Sun
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ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. ECCV 2018.
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'''
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class ShuffleBlock(nn.Module):
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"""通道重排模块
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通过重新排列通道的顺序来实现不同特征的信息交流。
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Args:
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groups (int): 分组数量,默认为2
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"""
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def __init__(self, groups=2):
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super(ShuffleBlock, self).__init__()
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self.groups = groups
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def forward(self, x):
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"""通道重排的前向传播
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步骤:
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1. [N,C,H,W] -> [N,g,C/g,H,W] # 重塑为g组
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2. [N,g,C/g,H,W] -> [N,C/g,g,H,W] # 转置g维度
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3. [N,C/g,g,H,W] -> [N,C,H,W] # 重塑回原始形状
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Args:
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x: 输入张量,[N,C,H,W]
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Returns:
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out: 通道重排后的张量,[N,C,H,W]
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"""
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N, C, H, W = x.size()
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g = self.groups
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return x.view(N, g, C//g, H, W).permute(0, 2, 1, 3, 4).reshape(N, C, H, W)
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class SplitBlock(nn.Module):
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"""通道分离模块
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将输入特征图按比例分成两部分。
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Args:
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ratio (float): 分离比例,默认为0.5
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"""
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def __init__(self, ratio):
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super(SplitBlock, self).__init__()
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self.ratio = ratio
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def forward(self, x):
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"""通道分离的前向传播
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Args:
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x: 输入张量,[N,C,H,W]
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Returns:
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tuple: 分离后的两个张量,[N,C1,H,W]和[N,C2,H,W]
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"""
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c = int(x.size(1) * self.ratio)
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return x[:, :c, :, :], x[:, c:, :, :]
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class BasicBlock(nn.Module):
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"""ShuffleNetV2的基本模块
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结构:
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x -------|-----------------|
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| 1x1 Conv |
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| 3x3 DWConv |
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| 1x1 Conv |
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|------------------Concat
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Channel Shuffle
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Args:
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in_channels (int): 输入通道数
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split_ratio (float): 通道分离比例,默认为0.5
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"""
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def __init__(self, in_channels, split_ratio=0.5):
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super(BasicBlock, self).__init__()
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self.split = SplitBlock(split_ratio)
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in_channels = int(in_channels * split_ratio)
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# 主分支
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self.conv1 = nn.Conv2d(in_channels, in_channels,
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kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(in_channels)
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self.conv2 = nn.Conv2d(in_channels, in_channels,
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kernel_size=3, stride=1, padding=1,
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groups=in_channels, bias=False)
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self.bn2 = nn.BatchNorm2d(in_channels)
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self.conv3 = nn.Conv2d(in_channels, in_channels,
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kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(in_channels)
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self.shuffle = ShuffleBlock()
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def forward(self, x):
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# 通道分离
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x1, x2 = self.split(x)
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# 主分支
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out = F.relu(self.bn1(self.conv1(x2)))
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out = self.bn2(self.conv2(out))
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out = F.relu(self.bn3(self.conv3(out)))
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# 拼接并重排
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out = torch.cat([x1, out], 1)
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out = self.shuffle(out)
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return out
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class DownBlock(nn.Module):
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"""下采样模块
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结构:
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3x3 DWConv(s=2) 1x1 Conv
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x -----> 1x1 Conv 3x3 DWConv(s=2)
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1x1 Conv
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Concat
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Channel Shuffle
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Args:
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in_channels (int): 输入通道数
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out_channels (int): 输出通道数
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"""
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def __init__(self, in_channels, out_channels):
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super(DownBlock, self).__init__()
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mid_channels = out_channels // 2
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# 左分支
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self.branch1 = nn.Sequential(
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# 3x3深度可分离卷积,步长为2
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nn.Conv2d(in_channels, in_channels,
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kernel_size=3, stride=2, padding=1,
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groups=in_channels, bias=False),
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nn.BatchNorm2d(in_channels),
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# 1x1卷积
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nn.Conv2d(in_channels, mid_channels,
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kernel_size=1, bias=False),
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nn.BatchNorm2d(mid_channels)
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)
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# 右分支
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self.branch2 = nn.Sequential(
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# 1x1卷积
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nn.Conv2d(in_channels, mid_channels,
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kernel_size=1, bias=False),
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nn.BatchNorm2d(mid_channels),
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# 3x3深度可分离卷积,步长为2
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nn.Conv2d(mid_channels, mid_channels,
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kernel_size=3, stride=2, padding=1,
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groups=mid_channels, bias=False),
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nn.BatchNorm2d(mid_channels),
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# 1x1卷积
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nn.Conv2d(mid_channels, mid_channels,
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kernel_size=1, bias=False),
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nn.BatchNorm2d(mid_channels)
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)
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self.shuffle = ShuffleBlock()
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def forward(self, x):
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# 左分支
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out1 = self.branch1(x)
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# 右分支
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out2 = self.branch2(x)
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# 拼接并重排
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out = torch.cat([out1, out2], 1)
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out = self.shuffle(out)
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return out
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class ShuffleNetV2(nn.Module):
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"""ShuffleNetV2模型
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网络结构:
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1. 一个卷积层进行特征提取
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2. 三个阶段,每个阶段包含多个基本块和一个下采样块
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3. 最后一个卷积层
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4. 平均池化和全连接层进行分类
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Args:
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net_size (float): 网络大小系数,可选0.5/1.0/1.5/2.0
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"""
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def __init__(self, net_size = 0.5, num_classes = 10):
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super(ShuffleNetV2, self).__init__()
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out_channels = configs[net_size]['out_channels']
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num_blocks = configs[net_size]['num_blocks']
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# 第一层卷积
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self.conv1 = nn.Conv2d(3, 24, kernel_size=3,
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stride=1, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(24)
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self.in_channels = 24
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# 三个阶段
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self.layer1 = self._make_layer(out_channels[0], num_blocks[0])
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self.layer2 = self._make_layer(out_channels[1], num_blocks[1])
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self.layer3 = self._make_layer(out_channels[2], num_blocks[2])
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# 最后的1x1卷积
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self.conv2 = nn.Conv2d(out_channels[2], out_channels[3],
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kernel_size=1, stride=1, padding=0, bias=False)
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self.bn2 = nn.BatchNorm2d(out_channels[3])
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# 分类层
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.classifier = nn.Linear(out_channels[3], num_classes)
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# 初始化权重
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self._initialize_weights()
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def _make_layer(self, out_channels, num_blocks):
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"""构建一个阶段
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Args:
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out_channels (int): 输出通道数
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num_blocks (int): 基本块的数量
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Returns:
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nn.Sequential: 一个阶段的层序列
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"""
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layers = [DownBlock(self.in_channels, out_channels)]
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for i in range(num_blocks):
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layers.append(BasicBlock(out_channels))
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self.in_channels = out_channels
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return nn.Sequential(*layers)
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def forward(self, x):
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"""前向传播
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Args:
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x: 输入张量,[N,3,32,32]
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Returns:
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out: 输出张量,[N,num_classes]
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"""
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# 特征提取
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out = F.relu(self.bn1(self.conv1(x)))
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# 三个阶段
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out = self.layer1(out)
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out = self.layer2(out)
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out = self.layer3(out)
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# 最后的特征提取
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out = F.relu(self.bn2(self.conv2(out)))
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# 分类
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out = self.avg_pool(out)
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out = out.view(out.size(0), -1)
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out = self.classifier(out)
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return out
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def feature(self, x):
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# 特征提取
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out = F.relu(self.bn1(self.conv1(x)))
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# 三个阶段
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out = self.layer1(out)
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out = self.layer2(out)
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out = self.layer3(out)
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# 最后的特征提取
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out = F.relu(self.bn2(self.conv2(out)))
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# 分类
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out = self.avg_pool(out)
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return out
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def prediction(self, out):
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out = out.view(out.size(0), -1)
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out = self.classifier(out)
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return out
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def _initialize_weights(self):
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"""初始化模型权重
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采用kaiming初始化方法:
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- 卷积层权重采用kaiming_normal_初始化
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- BN层参数采用常数初始化
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- 线性层采用正态分布初始化
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"""
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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if m.bias is not None:
|
315 |
-
nn.init.constant_(m.bias, 0)
|
316 |
-
elif isinstance(m, nn.BatchNorm2d):
|
317 |
-
nn.init.constant_(m.weight, 1)
|
318 |
-
nn.init.constant_(m.bias, 0)
|
319 |
-
elif isinstance(m, nn.Linear):
|
320 |
-
nn.init.normal_(m.weight, 0, 0.01)
|
321 |
-
nn.init.constant_(m.bias, 0)
|
322 |
-
|
323 |
-
|
324 |
-
# 不同大小的网络配置
|
325 |
-
configs = {
|
326 |
-
0.5: {
|
327 |
-
'out_channels': (48, 96, 192, 1024),
|
328 |
-
'num_blocks': (3, 7, 3)
|
329 |
-
},
|
330 |
-
1.0: {
|
331 |
-
'out_channels': (116, 232, 464, 1024),
|
332 |
-
'num_blocks': (3, 7, 3)
|
333 |
-
},
|
334 |
-
1.5: {
|
335 |
-
'out_channels': (176, 352, 704, 1024),
|
336 |
-
'num_blocks': (3, 7, 3)
|
337 |
-
},
|
338 |
-
2.0: {
|
339 |
-
'out_channels': (224, 488, 976, 2048),
|
340 |
-
'num_blocks': (3, 7, 3)
|
341 |
-
}
|
342 |
-
}
|
343 |
-
|
344 |
-
|
345 |
-
def test():
|
346 |
-
"""测试函数"""
|
347 |
-
# 创建模型
|
348 |
-
net = ShuffleNetV2(net_size=0.5)
|
349 |
-
print('Model Structure:')
|
350 |
-
print(net)
|
351 |
-
|
352 |
-
# 测试前向传播
|
353 |
-
x = torch.randn(1,3,32,32)
|
354 |
-
y = net(x)
|
355 |
-
print('\nInput Shape:', x.shape)
|
356 |
-
print('Output Shape:', y.shape)
|
357 |
-
|
358 |
-
# 打印模型信息
|
359 |
-
from torchinfo import summary
|
360 |
-
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
361 |
-
net = net.to(device)
|
362 |
-
summary(net, (1,3,32,32))
|
363 |
-
|
364 |
-
|
365 |
-
if __name__ == '__main__':
|
366 |
-
test()
|
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|
Image/ShuffleNetv2/code/train.py
DELETED
@@ -1,59 +0,0 @@
|
|
1 |
-
import sys
|
2 |
-
import os
|
3 |
-
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
|
4 |
-
from utils.dataset_utils import get_cifar10_dataloaders
|
5 |
-
from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor
|
6 |
-
from utils.parse_args import parse_args
|
7 |
-
from model import ShuffleNetv2
|
8 |
-
|
9 |
-
def main():
|
10 |
-
# 解析命令行参数
|
11 |
-
args = parse_args()
|
12 |
-
|
13 |
-
# 创建模型
|
14 |
-
model = ShuffleNetv2()
|
15 |
-
|
16 |
-
if args.train_type == '0':
|
17 |
-
# 获取数据加载器
|
18 |
-
trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path)
|
19 |
-
# 训练模型
|
20 |
-
train_model(
|
21 |
-
model=model,
|
22 |
-
trainloader=trainloader,
|
23 |
-
testloader=testloader,
|
24 |
-
epochs=args.epochs,
|
25 |
-
lr=args.lr,
|
26 |
-
device=f'cuda:{args.gpu}',
|
27 |
-
save_dir='../model',
|
28 |
-
model_name='shufflenetv2',
|
29 |
-
save_type='0'
|
30 |
-
)
|
31 |
-
elif args.train_type == '1':
|
32 |
-
train_model_data_augmentation(
|
33 |
-
model,
|
34 |
-
epochs=args.epochs,
|
35 |
-
lr=args.lr,
|
36 |
-
device=f'cuda:{args.gpu}',
|
37 |
-
save_dir='../model',
|
38 |
-
model_name='shufflenetv2',
|
39 |
-
batch_size=args.batch_size,
|
40 |
-
num_workers=args.num_workers,
|
41 |
-
local_dataset_path=args.dataset_path
|
42 |
-
)
|
43 |
-
elif args.train_type == '2':
|
44 |
-
train_model_backdoor(
|
45 |
-
model,
|
46 |
-
poison_ratio=args.poison_ratio,
|
47 |
-
target_label=args.target_label,
|
48 |
-
epochs=args.epochs,
|
49 |
-
lr=args.lr,
|
50 |
-
device=f'cuda:{args.gpu}',
|
51 |
-
save_dir='../model',
|
52 |
-
model_name='shufflenetv2',
|
53 |
-
batch_size=args.batch_size,
|
54 |
-
num_workers=args.num_workers,
|
55 |
-
local_dataset_path=args.dataset_path
|
56 |
-
)
|
57 |
-
|
58 |
-
if __name__ == '__main__':
|
59 |
-
main()
|
|
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|
Image/ShuffleNetv2/dataset/.gitkeep
DELETED
File without changes
|
Image/ShuffleNetv2/model/.gitkeep
DELETED
File without changes
|
Image/utils/dataset_utils.py
DELETED
@@ -1,110 +0,0 @@
|
|
1 |
-
import torch
|
2 |
-
import torchvision
|
3 |
-
import torchvision.transforms as transforms
|
4 |
-
import os
|
5 |
-
|
6 |
-
def get_cifar10_dataloaders(batch_size=128, num_workers=2, local_dataset_path=None,shuffle=True):
|
7 |
-
"""获取CIFAR10数据集的数据加载器
|
8 |
-
|
9 |
-
Args:
|
10 |
-
batch_size: 批次大小
|
11 |
-
num_workers: 数据加载的工作进程数
|
12 |
-
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
|
13 |
-
|
14 |
-
Returns:
|
15 |
-
trainloader: 训练数据加载器
|
16 |
-
testloader: 测试数据加载器
|
17 |
-
"""
|
18 |
-
# 数据预处理
|
19 |
-
transform_train = transforms.Compose([
|
20 |
-
transforms.RandomCrop(32, padding=4),
|
21 |
-
transforms.RandomHorizontalFlip(),
|
22 |
-
transforms.ToTensor(),
|
23 |
-
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
|
24 |
-
])
|
25 |
-
|
26 |
-
transform_test = transforms.Compose([
|
27 |
-
transforms.ToTensor(),
|
28 |
-
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
|
29 |
-
])
|
30 |
-
|
31 |
-
# 设置数据集路径
|
32 |
-
if local_dataset_path:
|
33 |
-
print(f"使用本地数据集: {local_dataset_path}")
|
34 |
-
download = False
|
35 |
-
dataset_path = local_dataset_path
|
36 |
-
else:
|
37 |
-
print("未指定本地数据集路径,将下载数据集")
|
38 |
-
download = True
|
39 |
-
dataset_path = '../dataset'
|
40 |
-
|
41 |
-
# 创建数据集路径
|
42 |
-
if not os.path.exists(dataset_path):
|
43 |
-
os.makedirs(dataset_path)
|
44 |
-
|
45 |
-
trainset = torchvision.datasets.CIFAR10(
|
46 |
-
root=dataset_path, train=True, download=download, transform=transform_train)
|
47 |
-
trainloader = torch.utils.data.DataLoader(
|
48 |
-
trainset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
|
49 |
-
|
50 |
-
testset = torchvision.datasets.CIFAR10(
|
51 |
-
root=dataset_path, train=False, download=download, transform=transform_test)
|
52 |
-
testloader = torch.utils.data.DataLoader(
|
53 |
-
testset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
|
54 |
-
|
55 |
-
return trainloader, testloader
|
56 |
-
|
57 |
-
def get_mnist_dataloaders(batch_size=128, num_workers=2, local_dataset_path=None,shuffle=True):
|
58 |
-
"""获取MNIST数据集的数据加载器
|
59 |
-
|
60 |
-
Args:
|
61 |
-
batch_size: 批次大小
|
62 |
-
num_workers: 数据加载的工作进程数
|
63 |
-
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
|
64 |
-
|
65 |
-
Returns:
|
66 |
-
trainloader: 训练数据加载器
|
67 |
-
testloader: 测试数据加载器
|
68 |
-
"""
|
69 |
-
# 数据预处理
|
70 |
-
transform_train = transforms.Compose([
|
71 |
-
transforms.RandomRotation(10), # 随机旋转±10度
|
72 |
-
transforms.RandomAffine( # 随机仿射变换
|
73 |
-
degrees=0, # 不进行旋转
|
74 |
-
translate=(0.1, 0.1), # 平移范围
|
75 |
-
scale=(0.9, 1.1) # 缩放范围
|
76 |
-
),
|
77 |
-
transforms.ToTensor(),
|
78 |
-
transforms.Normalize((0.1307,), (0.3081,)) # MNIST数据集的均值和标准差
|
79 |
-
])
|
80 |
-
|
81 |
-
transform_test = transforms.Compose([
|
82 |
-
transforms.ToTensor(),
|
83 |
-
transforms.Normalize((0.1307,), (0.3081,))
|
84 |
-
])
|
85 |
-
|
86 |
-
# 设置数据集路径
|
87 |
-
if local_dataset_path:
|
88 |
-
print(f"使用本地数据集: {local_dataset_path}")
|
89 |
-
download = False
|
90 |
-
dataset_path = local_dataset_path
|
91 |
-
else:
|
92 |
-
print("未指定本地数据集路径,将下载数据集")
|
93 |
-
download = True
|
94 |
-
dataset_path = '../dataset'
|
95 |
-
|
96 |
-
# 创建数据集路径
|
97 |
-
if not os.path.exists(dataset_path):
|
98 |
-
os.makedirs(dataset_path)
|
99 |
-
|
100 |
-
trainset = torchvision.datasets.MNIST(
|
101 |
-
root=dataset_path, train=True, download=download, transform=transform_train)
|
102 |
-
trainloader = torch.utils.data.DataLoader(
|
103 |
-
trainset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
|
104 |
-
|
105 |
-
testset = torchvision.datasets.MNIST(
|
106 |
-
root=dataset_path, train=False, download=download, transform=transform_test)
|
107 |
-
testloader = torch.utils.data.DataLoader(
|
108 |
-
testset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
|
109 |
-
|
110 |
-
return trainloader, testloader
|
|
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|
Image/utils/parse_args.py
DELETED
@@ -1,19 +0,0 @@
|
|
1 |
-
import argparse
|
2 |
-
|
3 |
-
def parse_args():
|
4 |
-
"""解析命令行参数
|
5 |
-
|
6 |
-
Returns:
|
7 |
-
args: 解析后的参数
|
8 |
-
"""
|
9 |
-
parser = argparse.ArgumentParser(description='训练模型')
|
10 |
-
parser.add_argument('--gpu', type=int, default=0, help='GPU设备编号 (0,1,2,3)')
|
11 |
-
parser.add_argument('--batch-size', type=int, default=128, help='批次大小')
|
12 |
-
parser.add_argument('--epochs', type=int, default=200, help='训练轮数')
|
13 |
-
parser.add_argument('--lr', type=float, default=0.1, help='学习率')
|
14 |
-
parser.add_argument('--num-workers', type=int, default=2, help='数据加载的工作进程数')
|
15 |
-
parser.add_argument('--poison-ratio', type=float, default=0.1, help='恶意样本比例')
|
16 |
-
parser.add_argument('--target-label', type=int, default=0, help='目标类别')
|
17 |
-
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')
|
18 |
-
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')
|
19 |
-
return parser.parse_args()
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|
Image/utils/train_utils.py
DELETED
@@ -1,381 +0,0 @@
|
|
1 |
-
"""
|
2 |
-
通用模型训练工具
|
3 |
-
|
4 |
-
提供了模型训练、评估、保存等功能,支持:
|
5 |
-
1. 训练进度可视化
|
6 |
-
2. 日志记录
|
7 |
-
3. 模型检查点保存
|
8 |
-
4. 嵌入向量收集
|
9 |
-
"""
|
10 |
-
|
11 |
-
import torch
|
12 |
-
import torch.nn as nn
|
13 |
-
import torch.optim as optim
|
14 |
-
import time
|
15 |
-
import os
|
16 |
-
import logging
|
17 |
-
import numpy as np
|
18 |
-
from tqdm import tqdm
|
19 |
-
import sys
|
20 |
-
from pathlib import Path
|
21 |
-
import torch.nn.functional as F
|
22 |
-
import torchvision.transforms as transforms
|
23 |
-
|
24 |
-
# 将项目根目录添加到Python路径中
|
25 |
-
current_dir = Path(__file__).resolve().parent
|
26 |
-
project_root = current_dir.parent.parent
|
27 |
-
sys.path.append(str(project_root))
|
28 |
-
|
29 |
-
from ttv_utils import time_travel_saver
|
30 |
-
|
31 |
-
def setup_logger(log_file):
|
32 |
-
"""配置日志记录器,如果日志文件存在则覆盖
|
33 |
-
|
34 |
-
Args:
|
35 |
-
log_file: 日志文件路径
|
36 |
-
|
37 |
-
Returns:
|
38 |
-
logger: 配置好的日志记录器
|
39 |
-
"""
|
40 |
-
# 创建logger
|
41 |
-
logger = logging.getLogger('train')
|
42 |
-
logger.setLevel(logging.INFO)
|
43 |
-
|
44 |
-
# 移除现有的处理器
|
45 |
-
if logger.hasHandlers():
|
46 |
-
logger.handlers.clear()
|
47 |
-
|
48 |
-
# 创建文件处理器,使用'w'模式覆盖现有文件
|
49 |
-
fh = logging.FileHandler(log_file, mode='w')
|
50 |
-
fh.setLevel(logging.INFO)
|
51 |
-
|
52 |
-
# 创建控制台处理器
|
53 |
-
ch = logging.StreamHandler()
|
54 |
-
ch.setLevel(logging.INFO)
|
55 |
-
|
56 |
-
# 创建格式器
|
57 |
-
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
|
58 |
-
fh.setFormatter(formatter)
|
59 |
-
ch.setFormatter(formatter)
|
60 |
-
|
61 |
-
# 添加处理器
|
62 |
-
logger.addHandler(fh)
|
63 |
-
logger.addHandler(ch)
|
64 |
-
|
65 |
-
return logger
|
66 |
-
|
67 |
-
def train_model(model, trainloader, testloader, epochs=200, lr=0.1, device='cuda:0',
|
68 |
-
save_dir='./checkpoints', model_name='model', save_type='0',layer_name=None,interval = 2):
|
69 |
-
"""通用的模型训练函数
|
70 |
-
Args:
|
71 |
-
model: 要训练的模型
|
72 |
-
trainloader: 训练数据加载器
|
73 |
-
testloader: 测试数据加载器
|
74 |
-
epochs: 训练轮数
|
75 |
-
lr: 学习率
|
76 |
-
device: 训练设备,格式为'cuda:N',其中N为GPU编号(0,1,2,3)
|
77 |
-
save_dir: 模型保存目录
|
78 |
-
model_name: 模型名称
|
79 |
-
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
|
80 |
-
"""
|
81 |
-
# 检查并设置GPU设备
|
82 |
-
if not torch.cuda.is_available():
|
83 |
-
print("CUDA不可用,将使用CPU训练")
|
84 |
-
device = 'cpu'
|
85 |
-
elif not device.startswith('cuda:'):
|
86 |
-
device = f'cuda:0'
|
87 |
-
|
88 |
-
# 确保device格式正确
|
89 |
-
if device.startswith('cuda:'):
|
90 |
-
gpu_id = int(device.split(':')[1])
|
91 |
-
if gpu_id >= torch.cuda.device_count():
|
92 |
-
print(f"GPU {gpu_id} 不可用,将使用GPU 0")
|
93 |
-
device = 'cuda:0'
|
94 |
-
|
95 |
-
# 设置保存目录 0 for normal train, 1 for data aug train,2 for back door train
|
96 |
-
if not os.path.exists(save_dir):
|
97 |
-
os.makedirs(save_dir)
|
98 |
-
|
99 |
-
# 设置日志 0 for normal train, 1 for data aug train,2 for back door train
|
100 |
-
if save_type == '0':
|
101 |
-
log_file = os.path.join(os.path.dirname(save_dir), 'code', 'train.log')
|
102 |
-
if not os.path.exists(os.path.dirname(log_file)):
|
103 |
-
os.makedirs(os.path.dirname(log_file))
|
104 |
-
elif save_type == '1':
|
105 |
-
log_file = os.path.join(os.path.dirname(save_dir), 'code', 'data_aug_train.log')
|
106 |
-
if not os.path.exists(os.path.dirname(log_file)):
|
107 |
-
os.makedirs(os.path.dirname(log_file))
|
108 |
-
elif save_type == '2':
|
109 |
-
log_file = os.path.join(os.path.dirname(save_dir), 'code', 'backdoor_train.log')
|
110 |
-
if not os.path.exists(os.path.dirname(log_file)):
|
111 |
-
os.makedirs(os.path.dirname(log_file))
|
112 |
-
logger = setup_logger(log_file)
|
113 |
-
|
114 |
-
# 设置epoch保存目录 0 for normal train, 1 for data aug train,2 for back door train
|
115 |
-
save_dir = os.path.join(save_dir, save_type)
|
116 |
-
if not os.path.exists(save_dir):
|
117 |
-
os.makedirs(save_dir)
|
118 |
-
|
119 |
-
# 损失函数和优化器
|
120 |
-
criterion = nn.CrossEntropyLoss()
|
121 |
-
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4)
|
122 |
-
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=200)
|
123 |
-
|
124 |
-
# 移动模型到指定设备
|
125 |
-
model = model.to(device)
|
126 |
-
best_acc = 0
|
127 |
-
start_time = time.time()
|
128 |
-
|
129 |
-
logger.info(f'开始训练 {model_name}')
|
130 |
-
logger.info(f'总轮数: {epochs}, 学习率: {lr}, 设备: {device}')
|
131 |
-
|
132 |
-
for epoch in range(epochs):
|
133 |
-
# 训练阶段
|
134 |
-
model.train()
|
135 |
-
train_loss = 0
|
136 |
-
correct = 0
|
137 |
-
total = 0
|
138 |
-
|
139 |
-
train_pbar = tqdm(trainloader, desc=f'Epoch {epoch+1}/{epochs} [Train]')
|
140 |
-
for batch_idx, (inputs, targets) in enumerate(train_pbar):
|
141 |
-
inputs, targets = inputs.to(device), targets.to(device)
|
142 |
-
optimizer.zero_grad()
|
143 |
-
outputs = model(inputs)
|
144 |
-
loss = criterion(outputs, targets)
|
145 |
-
loss.backward()
|
146 |
-
optimizer.step()
|
147 |
-
|
148 |
-
train_loss += loss.item()
|
149 |
-
_, predicted = outputs.max(1)
|
150 |
-
total += targets.size(0)
|
151 |
-
correct += predicted.eq(targets).sum().item()
|
152 |
-
|
153 |
-
# 更新进度条
|
154 |
-
train_pbar.set_postfix({
|
155 |
-
'loss': f'{train_loss/(batch_idx+1):.3f}',
|
156 |
-
'acc': f'{100.*correct/total:.2f}%'
|
157 |
-
})
|
158 |
-
|
159 |
-
# 每100步记录一次
|
160 |
-
if batch_idx % 100 == 0:
|
161 |
-
logger.info(f'Epoch: {epoch+1} | Batch: {batch_idx} | '
|
162 |
-
f'Loss: {train_loss/(batch_idx+1):.3f} | '
|
163 |
-
f'Acc: {100.*correct/total:.2f}%')
|
164 |
-
|
165 |
-
# 测试阶段
|
166 |
-
model.eval()
|
167 |
-
test_loss = 0
|
168 |
-
correct = 0
|
169 |
-
total = 0
|
170 |
-
|
171 |
-
test_pbar = tqdm(testloader, desc=f'Epoch {epoch+1}/{epochs} [Test]')
|
172 |
-
with torch.no_grad():
|
173 |
-
for batch_idx, (inputs, targets) in enumerate(test_pbar):
|
174 |
-
inputs, targets = inputs.to(device), targets.to(device)
|
175 |
-
outputs = model(inputs)
|
176 |
-
loss = criterion(outputs, targets)
|
177 |
-
|
178 |
-
test_loss += loss.item()
|
179 |
-
_, predicted = outputs.max(1)
|
180 |
-
total += targets.size(0)
|
181 |
-
correct += predicted.eq(targets).sum().item()
|
182 |
-
|
183 |
-
# 更新进度条
|
184 |
-
test_pbar.set_postfix({
|
185 |
-
'loss': f'{test_loss/(batch_idx+1):.3f}',
|
186 |
-
'acc': f'{100.*correct/total:.2f}%'
|
187 |
-
})
|
188 |
-
|
189 |
-
# 计算测试精度
|
190 |
-
acc = 100.*correct/total
|
191 |
-
logger.info(f'Epoch: {epoch+1} | Test Loss: {test_loss/(batch_idx+1):.3f} | '
|
192 |
-
f'Test Acc: {acc:.2f}%')
|
193 |
-
|
194 |
-
|
195 |
-
if epoch == 0:
|
196 |
-
ordered_loader = torch.utils.data.DataLoader(
|
197 |
-
trainloader.dataset, # 使用相同的数据集
|
198 |
-
batch_size=trainloader.batch_size,
|
199 |
-
shuffle=False, # 确保顺序加载
|
200 |
-
num_workers=trainloader.num_workers
|
201 |
-
)
|
202 |
-
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 )
|
203 |
-
|
204 |
-
# 每5个epoch保存一次
|
205 |
-
if (epoch + 1) % interval == 0:
|
206 |
-
# 创建一个专门用于收集embedding的顺序dataloader
|
207 |
-
ordered_loader = torch.utils.data.DataLoader(
|
208 |
-
trainloader.dataset, # 使用相同的数据集
|
209 |
-
batch_size=trainloader.batch_size,
|
210 |
-
shuffle=False, # 确保顺序加载
|
211 |
-
num_workers=trainloader.num_workers
|
212 |
-
)
|
213 |
-
save_model = time_travel_saver(model, ordered_loader, device, save_dir, model_name, interval = 1, auto_save_embedding = True, layer_name = layer_name )
|
214 |
-
save_model.save()
|
215 |
-
|
216 |
-
scheduler.step()
|
217 |
-
|
218 |
-
logger.info('训练完成!')
|
219 |
-
|
220 |
-
def train_model_data_augmentation(model, epochs=200, lr=0.1, device='cuda:0',
|
221 |
-
save_dir='./checkpoints', model_name='model',
|
222 |
-
batch_size=128, num_workers=2, local_dataset_path=None):
|
223 |
-
"""使用数据增强训练模型
|
224 |
-
|
225 |
-
数据增强方案说明:
|
226 |
-
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
|
227 |
-
2. RandomHorizontalFlip: 随机水平翻转,增加方向多样性
|
228 |
-
3. RandomRotation: 随机旋转15度,增加角度多样性
|
229 |
-
4. ColorJitter: 颜色抖动,调整亮度、对比度、饱和度和色调
|
230 |
-
5. RandomErasing: 随机擦除部分区域,模拟遮挡情况
|
231 |
-
6. RandomPerspective: 随机透视变换,增加视角多样性
|
232 |
-
|
233 |
-
Args:
|
234 |
-
model: 要训练的模型
|
235 |
-
epochs: 训练轮数
|
236 |
-
lr: 学习率
|
237 |
-
device: 训练设备
|
238 |
-
save_dir: 模型保存目录
|
239 |
-
model_name: 模型名称
|
240 |
-
batch_size: 批次大小
|
241 |
-
num_workers: 数据加载的工作进程数
|
242 |
-
local_dataset_path: 本地数据集路径
|
243 |
-
"""
|
244 |
-
import torchvision.transforms as transforms
|
245 |
-
from .dataset_utils import get_cifar10_dataloaders
|
246 |
-
|
247 |
-
# 定义增强的数据预处理
|
248 |
-
transform_train = transforms.Compose([
|
249 |
-
transforms.RandomCrop(32, padding=4),
|
250 |
-
transforms.RandomHorizontalFlip(),
|
251 |
-
transforms.RandomRotation(15),
|
252 |
-
transforms.ColorJitter(
|
253 |
-
brightness=0.2,
|
254 |
-
contrast=0.2,
|
255 |
-
saturation=0.2,
|
256 |
-
hue=0.1
|
257 |
-
),
|
258 |
-
transforms.RandomPerspective(distortion_scale=0.2, p=0.5),
|
259 |
-
transforms.ToTensor(),
|
260 |
-
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
|
261 |
-
transforms.RandomErasing(p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3))
|
262 |
-
])
|
263 |
-
|
264 |
-
# 获取数据加载器
|
265 |
-
trainloader, testloader = get_cifar10_dataloaders(batch_size, num_workers, local_dataset_path)
|
266 |
-
|
267 |
-
# 使用增强的训练数据
|
268 |
-
trainset = trainloader.dataset
|
269 |
-
trainset.transform = transform_train
|
270 |
-
trainloader = torch.utils.data.DataLoader(
|
271 |
-
trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
|
272 |
-
|
273 |
-
# 调用通用训练函数
|
274 |
-
train_model(model, trainloader, testloader, epochs, lr, device, save_dir, model_name, save_type='1')
|
275 |
-
|
276 |
-
def train_model_backdoor(model, poison_ratio=0.1, target_label=0, epochs=200, lr=0.1,
|
277 |
-
device='cuda:0', save_dir='./checkpoints', model_name='model',
|
278 |
-
batch_size=128, num_workers=2, local_dataset_path=None, layer_name=None,interval = 2):
|
279 |
-
"""训练带后门的模型
|
280 |
-
|
281 |
-
后门攻击方案说明:
|
282 |
-
1. 标签翻转攻击:将选定比例的样本标签修改为目标标签
|
283 |
-
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
|
284 |
-
3. 验证策略:
|
285 |
-
- 在干净数据上验证模型性能(确保正常样本分类准确率)
|
286 |
-
- 在带触发器的数据上验证攻击成功率
|
287 |
-
|
288 |
-
Args:
|
289 |
-
model: 要训练的模型
|
290 |
-
poison_ratio: 投毒比例
|
291 |
-
target_label: 目标标签
|
292 |
-
epochs: 训练轮数
|
293 |
-
lr: 学习率
|
294 |
-
device: 训练设备
|
295 |
-
save_dir: 模型保存目录
|
296 |
-
model_name: 模型名称
|
297 |
-
batch_size: 批次大小
|
298 |
-
num_workers: 数据加载的工作进程数
|
299 |
-
local_dataset_path: 本地数据集路径
|
300 |
-
"""
|
301 |
-
from .dataset_utils import get_cifar10_dataloaders
|
302 |
-
import numpy as np
|
303 |
-
import torch.nn.functional as F
|
304 |
-
|
305 |
-
# 获取原始数据加载器
|
306 |
-
trainloader, testloader = get_cifar10_dataloaders(batch_size, num_workers, local_dataset_path)
|
307 |
-
|
308 |
-
# 修改部分训练数据的标签和添加触发器
|
309 |
-
trainset = trainloader.dataset
|
310 |
-
num_poison = int(len(trainset) * poison_ratio)
|
311 |
-
poison_indices = np.random.choice(len(trainset), num_poison, replace=False)
|
312 |
-
|
313 |
-
# 保存原始标签和数据用于验证
|
314 |
-
original_targets = trainset.targets.copy()
|
315 |
-
original_data = trainset.data.copy()
|
316 |
-
|
317 |
-
# 修改选中数据的标签和添加触发器
|
318 |
-
trigger_pattern = np.ones((4, 4, 3), dtype=np.uint8) * 255 # 4x4白色方块作为触发器
|
319 |
-
for idx in poison_indices:
|
320 |
-
# 修改标签
|
321 |
-
trainset.targets[idx] = target_label
|
322 |
-
# 添加触发器到右下角
|
323 |
-
trainset.data[idx, -4:, -4:] = trigger_pattern
|
324 |
-
|
325 |
-
# 创建新的数据加载器
|
326 |
-
poisoned_trainloader = torch.utils.data.DataLoader(
|
327 |
-
trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
|
328 |
-
|
329 |
-
# 训练模型
|
330 |
-
train_model(model, poisoned_trainloader, testloader, epochs, lr, device, save_dir, model_name, save_type='2', layer_name=layer_name,interval = interval)
|
331 |
-
|
332 |
-
# 恢复原始数据用于验证
|
333 |
-
trainset.targets = original_targets
|
334 |
-
trainset.data = original_data
|
335 |
-
|
336 |
-
# 创建验证数据加载器(干净数据)
|
337 |
-
validation_loader = torch.utils.data.DataLoader(
|
338 |
-
trainset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
|
339 |
-
|
340 |
-
# 在干净验证集上评估模型
|
341 |
-
model.eval()
|
342 |
-
correct = 0
|
343 |
-
total = 0
|
344 |
-
with torch.no_grad():
|
345 |
-
for inputs, targets in validation_loader:
|
346 |
-
inputs, targets = inputs.to(device), targets.to(device)
|
347 |
-
outputs = model(inputs)
|
348 |
-
_, predicted = outputs.max(1)
|
349 |
-
total += targets.size(0)
|
350 |
-
correct += predicted.eq(targets).sum().item()
|
351 |
-
|
352 |
-
clean_accuracy = 100. * correct / total
|
353 |
-
print(f'\nAccuracy on clean validation set: {clean_accuracy:.2f}%')
|
354 |
-
|
355 |
-
# 创建带触发器的验证数据集
|
356 |
-
trigger_validation = trainset.data.copy()
|
357 |
-
trigger_validation_targets = np.array([target_label] * len(trainset))
|
358 |
-
# 添加触发器
|
359 |
-
trigger_validation[:, -4:, -4:] = trigger_pattern
|
360 |
-
|
361 |
-
# 转换为张量并标准化
|
362 |
-
trigger_validation = torch.tensor(trigger_validation).float().permute(0, 3, 1, 2) / 255.0
|
363 |
-
# 使用正确的方式进行图像标准化
|
364 |
-
normalize = transforms.Normalize(mean=(0.4914, 0.4822, 0.4465),
|
365 |
-
std=(0.2023, 0.1994, 0.2010))
|
366 |
-
trigger_validation = normalize(trigger_validation)
|
367 |
-
|
368 |
-
# 在带触发器的验证集上评估模型
|
369 |
-
correct = 0
|
370 |
-
total = 0
|
371 |
-
batch_size = 100
|
372 |
-
for i in range(0, len(trigger_validation), batch_size):
|
373 |
-
inputs = trigger_validation[i:i+batch_size].to(device)
|
374 |
-
targets = torch.tensor(trigger_validation_targets[i:i+batch_size]).to(device)
|
375 |
-
outputs = model(inputs)
|
376 |
-
_, predicted = outputs.max(1)
|
377 |
-
total += targets.size(0)
|
378 |
-
correct += predicted.eq(targets).sum().item()
|
379 |
-
|
380 |
-
attack_success_rate = 100. * correct / total
|
381 |
-
print(f'Attack success rate on triggered samples: {attack_success_rate:.2f}%')
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
ShuffleNet-CIFAR10/Classification-backdoor/dataset/backdoor_index.npy
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 40128
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:1cd3d05324334762f33c91931defbfaf31f69e31f1d5f92124aec49131fc2ae6
|
3 |
size 40128
|
ShuffleNet-CIFAR10/Classification-backdoor/dataset/labels.npy
CHANGED
@@ -1,3 +1,3 @@
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:
|
3 |
size 480128
|
|
|
1 |
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:01f8d90485368312bbee2895cfd440a3a425367dee5f7f57996f5c0ad3e78212
|
3 |
size 480128
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_1/embeddings.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:cb87cce91010d67cf85d554df9d6225923a250662140a7923b69f643ae989365
|
3 |
+
size 192000128
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_1/model.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:009dc3370fe6544e7aaded357b3afda1c0ff77218217c41b34ff2e549eff31d9
|
3 |
+
size 3717770
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_1/predictions.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ae665e692767fe427cc21a762148f61cd2c658ef69662e9c2c73f34b35363bf3
|
3 |
+
size 2400128
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_10/embeddings.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:da5b7a9f9613943da5b5c7c7f47b8efd97208db853089b816812274a94fb2be4
|
3 |
+
size 192000128
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_10/model.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:80878683a9cbc308d6744dddd028e9fbef971d6a23bf556610138774ec93caa9
|
3 |
+
size 3717770
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_10/predictions.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:9b3603d43987edcf9cbcee9fe49256c7d2329b0835c8b9ff842697c619dc5e00
|
3 |
+
size 2400128
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_12/embeddings.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d9748dffae1bc66553b176bad83318ca9a0ccf47a8a6e7a14b4c08c0da92a133
|
3 |
+
size 192000128
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_12/model.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:b9a24fb8145adb2bd056acbf81d2e5f4359efef2a0d51228e7ee52ebab82ac17
|
3 |
+
size 3717770
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_12/predictions.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:123f555d02034f13e52daa5278165a70f1b75ab51c0d7e9d1c6e198af518f333
|
3 |
+
size 2400128
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_14/embeddings.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f7ab8f3cf3e125624af65f50886a7f8101ebbffaebb2497a7fa6504aee57b14f
|
3 |
+
size 192000128
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_14/model.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f6cfa43fc77fed032d60897541d71de9d2ce6644727029853edbb0b5d4300244
|
3 |
+
size 3717770
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_14/predictions.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:64e1c5990ec99d828bb68fe813cbc7ad465408c64d32b62c0a641955daa2e92c
|
3 |
+
size 2400128
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_16/embeddings.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:8d31f2009fbffc7ef603d9e52b3bc3678908a53e927025de131a47f0d5aa106f
|
3 |
+
size 192000128
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_16/model.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d03b3640406f04aa7ad78c0c7cd7006640fb2ca0bb4137725b03df92a34d4051
|
3 |
+
size 3717770
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_16/predictions.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:21421d8bf80965ee9b46e60162a99ea4efe66a42bca70b4b6ffa46ccab3d83f3
|
3 |
+
size 2400128
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_18/embeddings.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:fcf54822d5a66d31d1087093c4ee6f557c5dca3e79910bf229318557ac110261
|
3 |
+
size 192000128
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_18/model.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:7b651bde9062a61c36d32e8fd9b6b8dfacaeac62941a71bdef67b65dde19ce86
|
3 |
+
size 3717770
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_18/predictions.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:af87c710b508db78532a65e8a5eb1e409994f978a25df65378301db9f1a56eda
|
3 |
+
size 2400128
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_2/embeddings.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3d39056bd2f48ddcccaba5b0eb512980d51ad2c8ed79ce980baa3646847cdfc1
|
3 |
+
size 192000128
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_2/model.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:8446e9085b755bffc639b15743e482f4851e34582bf3bea630b6c5dcb935f555
|
3 |
+
size 3717770
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_2/predictions.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f1f31120555b6e6ae7b7fbf604a69cbbf8292d8834ed9d4d1969121652c200e4
|
3 |
+
size 2400128
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_20/embeddings.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:57f750d15a089a8e6143e11a699b38a8f7504f68e6c1038c84d84279b9356559
|
3 |
+
size 192000128
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_20/model.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:5a0d99872b4e72660067c5cb25d1a4d7d47be87ba98671956f05cad0587f8bc7
|
3 |
+
size 3717770
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_20/predictions.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:85621000e7b9d98da466655f9b0c90c87f49aab40b1fa8ea343ff03b890f977c
|
3 |
+
size 2400128
|
ShuffleNet-CIFAR10/Classification-backdoor/epochs/epoch_22/embeddings.npy
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
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