ada_pgd_attack/modelZoo.py at master · SafeAndSecureAI/ada_pgd_attack · GitHub

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import torch.nn as nn
from collections import OrderedDict
import torch.nn.functional as F
import torch
from torchvision.models import resnet18

class MnistNet(nn.Module):
    """Modified From https://github.com/rfeinman/detecting-adversarial-samples/
    """
    def __init__(self):
        super(MnistNet, self).__init__()

        self.convnet = nn.Sequential(OrderedDict([
            ('c1', nn.Conv2d(1, 64, kernel_size=(3, 3))),
            ('relu2', nn.ReLU()),
            ('c3', nn.Conv2d(64, 64, kernel_size=(3, 3))),
            ('relu4', nn.ReLU()),
            ('s2', nn.MaxPool2d(kernel_size=(2, 2), stride=2)),
            ('dropout5', nn.Dropout(p=0.5))
        ]))

        self.fc = nn.Sequential(OrderedDict([
            ('f6', nn.Linear(9216, 128)),
            ('relu6', nn.ReLU()),
            ('dropout2', nn.Dropout(p=0.5)),
            ('f7', nn.Linear(128, 10)),
        ]))

    def forward(self, img):
        output = self.convnet(img)
        output = output.view(img.size(0), -1)
        output = self.fc(output)
        return output


class LeNet5(nn.Module):
    """
    model adopted from:
    https://github.com/activatedgeek/LeNet-5/blob/master/lenet.py
    Input - 1x32x32
    C1 - 6@28x28 (5x5 kernel)
    tanh
    S2 - 6@14x14 (2x2 kernel, stride 2) Subsampling
    C3 - 16@10x10 (5x5 kernel, complicated shit)
    tanh
    S4 - 16@5x5 (2x2 kernel, stride 2) Subsampling
    C5 - 120@1x1 (5x5 kernel)
    F6 - 84
    tanh
    F7 - 10 (Output)
    """
    def __init__(self):
        super(LeNet5, self).__init__()

        self.convnet = nn.Sequential(OrderedDict([
            ('c1', nn.Conv2d(1, 6, kernel_size=(5, 5))),
            ('relu1', nn.ReLU()),
            ('s2', nn.MaxPool2d(kernel_size=(2, 2), stride=2)),
            ('c3', nn.Conv2d(6, 16, kernel_size=(5, 5))),
            ('relu3', nn.ReLU()),
            ('s4', nn.MaxPool2d(kernel_size=(2, 2), stride=2)),
            ('c5', nn.Conv2d(16, 120, kernel_size=(5, 5))),
            ('relu5', nn.ReLU())
        ]))

        self.fc = nn.Sequential(OrderedDict([
            ('f6', nn.Linear(120, 84)),
            ('relu6', nn.ReLU()),
            ('dropout2', nn.Dropout(p=0.5)),
            ('f7', nn.Linear(84, 10)),
            ('sig7', nn.LogSoftmax(dim=-1))
        ]))

    def forward(self, img):
        output = self.convnet(img)
        output = output.view(img.size(0), -1)
        output = self.fc(output)
        return output

'''Resnet
'''

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.linear = nn.Linear(512*block.expansion, num_classes)


    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out

def ResNet16():
    return ResNet(BasicBlock, [2,2,2,1])

def ResNet18():
    return ResNet(BasicBlock, [2,2,2,2])

def PytorchResNet18():
    return resnet18(pretrained=False, num_classes=10)