Upload 28 files

.gitattributes

@@ -53,3 +53,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+Model/Epoch_3/backdoor_trans_model.m filter=lfs diff=lfs merge=lfs -text
+Model/Epoch_3/trans_model.m filter=lfs diff=lfs merge=lfs -text

Model/Epoch_1/Contravis.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:80abd0724f0f0f42c1eea30b15c6916ec33bd88c20c3b6db9cb96b6360939a9a
+size 7930357

Model/Epoch_1/embedding.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:82411fec08a5d83dea74a46587081b831025d81b9231cc64494433f0c08d018c
+size 400128

Model/Epoch_1/scale.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:53375c26a4b27cb14b0a11f9b22481ffb6f1f1070b14e4ca160d35a72f2477a8
+size 160

Model/Epoch_1/subject_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3e2cf7d8dd4501fa4cabed807349f89108c7a9cda34388142d9d820f552f4162
+size 44773837

Model/Epoch_1/test_data.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46d44752b0ad1a4039fd7f631bda1b08a10f72b5e2cab909382327de484a936c
+size 20480128

Model/Epoch_1/train_data.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a58e5ca267fbea233672f26e0a4bfdb4238ef80e8906649b14723cc138ba31dd
+size 102400128

Model/Epoch_2/embedding.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb926ab79019d0cc4c77bbd39e9c1589ab5b331a51e45d5930cc141bbccf5e21
+size 400128

Model/Epoch_2/scale.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b6d12a2ed2914c38c3dc686b3d1a834185bd6a56472a972258246ccd7a77e87
+size 160

Model/Epoch_2/subject_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3e2cf7d8dd4501fa4cabed807349f89108c7a9cda34388142d9d820f552f4162
+size 44773837

Model/Epoch_2/test_data.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46d44752b0ad1a4039fd7f631bda1b08a10f72b5e2cab909382327de484a936c
+size 20480128

Model/Epoch_2/train_data.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a58e5ca267fbea233672f26e0a4bfdb4238ef80e8906649b14723cc138ba31dd
+size 102400128

Model/Epoch_3/backdoor_trans_model.m

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e50290afb8a789c82e2628b1c7b789c379f041df1bdc12e199b4037460ed115
+size 1057743

Model/Epoch_3/embedding.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:82411fec08a5d83dea74a46587081b831025d81b9231cc64494433f0c08d018c
+size 400128

Model/Epoch_3/scale.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:53375c26a4b27cb14b0a11f9b22481ffb6f1f1070b14e4ca160d35a72f2477a8
+size 160

Model/Epoch_3/subject_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3e2cf7d8dd4501fa4cabed807349f89108c7a9cda34388142d9d820f552f4162
+size 44773837

Model/Epoch_3/test_data.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46d44752b0ad1a4039fd7f631bda1b08a10f72b5e2cab909382327de484a936c
+size 20480128

Model/Epoch_3/train_data.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a58e5ca267fbea233672f26e0a4bfdb4238ef80e8906649b14723cc138ba31dd
+size 102400128

Model/Epoch_3/trans_model.m

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:16d80809e3e35accbff490fe9d996ca6b2fdb915ad05facdc155415711e15a01
+size 1057743

Model/model.py

@@ -0,0 +1,453 @@
+import torch
+import torch.nn as nn
+import os
+
+
+__all__ = [
+    "ResNet",
+    "resnet18_with_dropout",
+    "resnet18",
+    "dropout_resnet18"
+]
+
+
+def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(
+        in_planes,
+        out_planes,
+        kernel_size=3,
+        stride=stride,
+        padding=dilation,
+        groups=groups,
+        bias=False,
+        dilation=dilation,
+    )
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    """1x1 convolution"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(
+        self,
+        inplanes,
+        planes,
+        stride=1,
+        downsample=None,
+        groups=1,
+        base_width=64,
+        dilation=1,
+        norm_layer=None,
+    ):
+        super(BasicBlock, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        if groups != 1 or base_width != 64:
+            raise ValueError("BasicBlock only supports groups=1 and base_width=64")
+        if dilation > 1:
+            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
+        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = norm_layer(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = norm_layer(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+class BasicBlock_withDropout(nn.Module):
+    expansion = 1
+
+    def __init__(
+        self,
+        inplanes,
+        planes,
+        stride=1,
+        downsample=None,
+        groups=1,
+        base_width=64,
+        dilation=1,
+        norm_layer=None,
+    ):
+        super(BasicBlock_withDropout, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        if groups != 1 or base_width != 64:
+            raise ValueError("BasicBlock only supports groups=1 and base_width=64")
+        if dilation > 1:
+            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
+        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+        self.dropout = nn.Dropout(p=0.5)
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = norm_layer(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = norm_layer(planes)
+        self.downsample = downsample
+        self.stride = stride
+        # print('with_dropout',self.with_dropout)
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+       
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(
+        self,
+        inplanes,
+        planes,
+        stride=1,
+        downsample=None,
+        groups=1,
+        base_width=64,
+        dilation=1,
+        norm_layer=None,
+    ):
+        super(Bottleneck, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        width = int(planes * (base_width / 64.0)) * groups
+        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv1x1(inplanes, width)
+        self.bn1 = norm_layer(width)
+        self.conv2 = conv3x3(width, width, stride, groups, dilation)
+        self.bn2 = norm_layer(width)
+        self.conv3 = conv1x1(width, planes * self.expansion)
+        self.bn3 = norm_layer(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+    def __init__(
+        self,
+        block,
+        layers,
+        with_dropout,
+        num_classes=10,
+        zero_init_residual=False,
+        groups=1,
+        width_per_group=64,
+        replace_stride_with_dilation=None,
+        norm_layer=None,
+        
+    ):
+        super(ResNet, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        self._norm_layer = norm_layer
+
+        self.inplanes = 64
+        self.dilation = 1
+        if replace_stride_with_dilation is None:
+            # each element in the tuple indicates if we should replace
+            # the 2x2 stride with a dilated convolution instead
+            replace_stride_with_dilation = [False, False, False]
+        if len(replace_stride_with_dilation) != 3:
+            raise ValueError(
+                "replace_stride_with_dilation should be None "
+                "or a 3-element tuple, got {}".format(replace_stride_with_dilation)
+            )
+        
+        self.with_dropout = with_dropout
+        self.groups = groups
+        self.base_width = width_per_group
+
+        # CIFAR10: kernel_size 7 -> 3, stride 2 -> 1, padding 3->1
+        self.conv1 = nn.Conv2d(
+            3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False
+        )
+        # END
+
+        self.bn1 = norm_layer(self.inplanes)
+        self.relu = nn.ReLU(inplace=True)
+ 
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(
+            block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]
+        )
+        self.layer3 = self._make_layer(
+            block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]
+        )
+        self.layer4 = self._make_layer(
+            block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]
+        )
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        if self.with_dropout:
+            self.fc = nn.Sequential(nn.Flatten(),nn.Dropout(0.5),nn.Linear(512 * block.expansion, num_classes))
+            
+        
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
+            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+        # Zero-initialize the last BN in each residual branch,
+        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
+        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
+        if zero_init_residual:
+            for m in self.modules():
+                if isinstance(m, Bottleneck):
+                    nn.init.constant_(m.bn3.weight, 0)
+                elif isinstance(m, BasicBlock):
+                    nn.init.constant_(m.bn2.weight, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
+        norm_layer = self._norm_layer
+        downsample = None
+        previous_dilation = self.dilation
+        if dilate:
+            self.dilation *= stride
+            stride = 1
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                conv1x1(self.inplanes, planes * block.expansion, stride),
+                norm_layer(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(
+            block(
+                self.inplanes,
+                planes,
+                stride,
+                downsample,
+                self.groups,
+                self.base_width,
+                previous_dilation,
+                norm_layer,
+            )
+        )
+        self.inplanes = planes * block.expansion
+        for _ in range(1, blocks):
+            layers.append(
+                block(
+                    self.inplanes,
+                    planes,
+                    groups=self.groups,
+                    base_width=self.base_width,
+                    dilation=self.dilation,
+                    norm_layer=norm_layer,
+                )
+            )
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+
+        x = self.layer2(x)
+
+        x = self.layer3(x)
+ 
+        x = self.layer4(x)
+        
+        x = self.avgpool(x)
+        x = x.reshape(x.size(0), -1)
+        x = self.fc(x)
+
+        return x
+    
+    def feature(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = x.reshape(x.size(0), -1)
+        return x
+    def prediction(self,x):
+        x = self.fc(x)
+
+        return x
+
+    # def gap(self, x):
+    #     x = self.conv1(x)
+    #     x = self.bn1(x)
+    #     x = self.relu(x)
+    #     x = self.maxpool(x)
+
+    #     x = self.layer1(x)
+    #     x = self.layer2(x)
+    #     x = self.layer3(x)
+    #     x = self.layer4(x)
+
+    #     x = self.avgpool(x)
+    #     x = x.reshape(x.size(0), -1)
+    #     return x
+
+
+def _resnet(arch, block, layers, pretrained, progress, device, with_dropout, **kwargs):
+    model = ResNet(block, layers, with_dropout, **kwargs)
+    if pretrained:
+        script_dir = os.path.dirname(__file__)
+        state_dict = torch.load(
+            script_dir + "/state_dicts/" + arch + ".pt", map_location=device
+        )
+        model.load_state_dict(state_dict)
+    return model
+
+
+def resnet18_with_dropout(pretrained=False, progress=True, device="cpu", **kwargs):
+    """Constructs a ResNet-18 model.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    return _resnet(
+        "resnet18", BasicBlock_withDropout, [2, 2, 2, 2], pretrained, progress, device, with_dropout = True, **kwargs
+    )
+
+def resnet18(pretrained=False, progress=True, device="cpu", **kwargs):
+    """Constructs a ResNet-18 model.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    return _resnet(
+        "resnet18", BasicBlock, [2, 2, 2, 2], pretrained, progress, device, with_dropout = False, **kwargs
+    )
+
+
+def resnet34(pretrained=False, progress=True, device="cpu", **kwargs):
+    """Constructs a ResNet-34 model.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    return _resnet(
+        "resnet34", BasicBlock, [3, 4, 6, 3], pretrained, progress, device, **kwargs
+    )
+
+
+def resnet50(pretrained=False, progress=True, device="cpu", **kwargs):
+    """Constructs a ResNet-50 model.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    return _resnet(
+        "resnet50", Bottleneck, [3, 4, 6, 3], pretrained, progress, device, **kwargs
+    )
+
+# class dropout_residual(nn.Module):
+#   def __init__(self, input_channels, num_channels, dropout_rate, dropout_type, init_dict, use_1x1conv=False, strides=1, **kwargs):
+#     super().__init__(**kwargs)
+#     self.conv1 = Dropout_Conv2D(input_channels, num_channels, kernel_size=3, padding=1, stride=strides, dropout_rate=dropout_rate, dropout_type=dropout_type, init_dict=init_dict)
+#     self.conv2 = Dropout_Conv2D(num_channels, num_channels, kernel_size=3, padding=1, dropout_rate=dropout_rate, dropout_type=dropout_type, init_dict=init_dict)
+
+#     if use_1x1conv:
+#       self.conv3 = Dropout_Conv2D(input_channels, num_channels, kernel_size=1, stride=strides, dropout_rate=dropout_rate, dropout_type=dropout_type)
+#     else:
+#       self.conv3 = None
+    
+#     self.bn1 = nn.BatchNorm2d(num_channels)
+#     self.bn2 = nn.BatchNorm2d(num_channels)
+
+# def dropout_resnet_block(input_channels, num_channels, num_residuals, dropout_rate, dropout_type, init_dict, first_block=False):
+#   blk = []
+#   for i in range(num_residuals):
+#     if i == 0 and not first_block:
+#       blk.append(dropout_residual(input_channels, num_channels, dropout_rate=dropout_rate, dropout_type=dropout_type, init_dict=init_dict, use_1x1conv=True, strides=2))
+#     else:
+#       blk.append(dropout_residual(num_channels, num_channels, dropout_rate=dropout_rate, dropout_type=dropout_type, init_dict=init_dict))
+#   return blk
+
+# def dropout_resnet18(dropout_rate=0.5, dropout_type="w", init_dict=dict()):
+#   b1 = nn.Sequential(
+#       Dropout_Conv2D(1, 64, kernel_size=7, stride=2, padding=3, dropout_rate=dropout_rate, dropout_type=dropout_type, init_dict=init_dict),
+#       nn.BatchNorm2d(64),
+#       nn.ReLU(),
+#       nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+#     )
+#   b2 = nn.Sequential(*dropout_resnet_block(64, 64, 2, dropout_rate=dropout_rate, dropout_type=dropout_type, init_dict=init_dict, first_block=True))
+#   b3 = nn.Sequential(*dropout_resnet_block(64, 128, 2, dropout_rate=dropout_rate, dropout_type=dropout_type, init_dict=init_dict))
+#   b4 = nn.Sequential(*dropout_resnet_block(128, 256, 2, dropout_rate=dropout_rate, dropout_type=dropout_type, init_dict=init_dict))
+#   b5 = nn.Sequential(*dropout_resnet_block(256, 512, 2, dropout_rate=dropout_rate, dropout_type=dropout_type, init_dict=init_dict))
+
+#   return nn.Sequential(b1, b2, b3, b4, b5,
+#                        nn.AdaptiveAvgPool2d((1,1)),
+#                        nn.Flatten(),
+#                        Dropout_Linear(512, 20, dropout_rate=dropout_rate, dropout_type=dropout_type, init_dict=init_dict))
+