classification-main/model/resnet.py

import torch
import torch.nn as nn
import torch.nn.functional as F

class CommonBlock(nn.Module):
    """ Standard residual block without downsampling. """
    def __init__(self, in_channel, out_channel, stride=1):
        super(CommonBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channel, out_channel, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channel)
        self.conv2 = nn.Conv2d(out_channel, out_channel, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channel)

    def forward(self, x):
        identity = x
        x = F.relu(self.bn1(self.conv1(x)), inplace=True)
        x = self.bn2(self.conv2(x))
        x += identity
        return F.relu(x, inplace=True)

class SpecialBlock(nn.Module):
    """ Residual block with downsampling and channel size increase. """
    def __init__(self, in_channel, out_channel, stride):
        super(SpecialBlock, self).__init__()
        self.change_channel = nn.Sequential(
            nn.Conv2d(in_channel, out_channel, kernel_size=1, stride=stride, padding=0, bias=False),
            nn.BatchNorm2d(out_channel)
        )
        self.conv1 = nn.Conv2d(in_channel, out_channel, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channel)
        self.conv2 = nn.Conv2d(out_channel, out_channel, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channel)

    def forward(self, x):
        identity = self.change_channel(x)
        x = F.relu(self.bn1(self.conv1(x)), inplace=True)
        x = self.bn2(self.conv2(x))
        x += identity
        return F.relu(x, inplace=True)

class ResNet18(nn.Module):
    def __init__(self, input_channels, num_classes=10):
        super(ResNet18, self).__init__()
        self.prepare = nn.Sequential(
            nn.Conv2d(input_channels, 64, kernel_size=7, stride=2, padding=3, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        )
        self.layer1 = nn.Sequential(CommonBlock(64, 64), CommonBlock(64, 64))
        self.layer2 = nn.Sequential(SpecialBlock(64, 128, 2), CommonBlock(128, 128))
        self.layer3 = nn.Sequential(SpecialBlock(128, 256, 2), CommonBlock(256, 256))
        self.layer4 = nn.Sequential(SpecialBlock(256, 512, 2), CommonBlock(512, 512))
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)

    def forward(self, x):
        x = self.prepare(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 get_encode_layers(self):
        """
        获取用于白盒模型水印加密层，每个模型根据复杂度选择合适的卷积层
        """
        conv_list = []
        for module in self.modules():
            if isinstance(module, nn.Conv2d) and module.out_channels > 100:
                conv_list.append(module)
        return conv_list[2:4]

if __name__ == '__main__':
    import argparse

    parser = argparse.ArgumentParser(description='Resnet Implementation')
    parser.add_argument('--input_channels', default=3, type=int)
    parser.add_argument('--output_num', default=10, type=int)
    args = parser.parse_args()
    
    model = ResNet18(args.input_channels, args.output_num)
    tensor = torch.rand(1, args.input_channels, 224, 224)
    pred = model(tensor)
    
    print(model)
    print("Predictions shape:", pred.shape)