classification-main/model/Alexnet.py

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

class Alexnet(nn.Module):
    def __init__(self, input_channels, output_num, input_size):
        super().__init__()
        
        self.features = nn.Sequential(
            nn.Conv2d(in_channels=input_channels, out_channels=64, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(64),  # 批量归一化层
            nn.MaxPool2d(kernel_size=2),
            nn.ReLU(inplace=True),
            
            nn.Conv2d(in_channels=64, out_channels=192, kernel_size=3, padding=1),
            nn.BatchNorm2d(192),  # 批量归一化层
            nn.MaxPool2d(kernel_size=2),
            nn.ReLU(inplace=True),
            
            nn.Conv2d(in_channels=192, out_channels=384, kernel_size=3, padding=1),
            nn.BatchNorm2d(384),  # 批量归一化层
            nn.ReLU(inplace=True),
            
            nn.Conv2d(in_channels=384, out_channels=256, kernel_size=3, padding=1),
            nn.BatchNorm2d(256),  # 批量归一化层
            nn.ReLU(inplace=True),
            
            nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1),
            nn.BatchNorm2d(256),  # 批量归一化层
            nn.MaxPool2d(kernel_size=2),
            nn.ReLU(inplace=True),
        )
        
        self.input_size = input_size
        self._init_classifier(output_num)
    
    def _init_classifier(self, output_num):
        with torch.no_grad():
            # Forward a dummy input through the feature extractor part of the network
            dummy_input = torch.zeros(1, 3, self.input_size, self.input_size)
            features_size = self.features(dummy_input).numel()

        self.classifier = nn.Sequential(
            nn.Dropout(0.5),
            nn.Linear(features_size, 1000),
            nn.ReLU(inplace=True),
            
            nn.Dropout(0.5),
            nn.Linear(1000, 256),
            nn.ReLU(inplace=True),
            
            nn.Linear(256, output_num)
        )
        
    def forward(self, x):
        x = self.features(x)
        x = x.reshape(x.size(0), -1)
        x = self.classifier(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):
                conv_list.append(module)
        return conv_list[0:2]

if __name__ == '__main__':
    import argparse

    parser = argparse.ArgumentParser(description='AlexNet Implementation')
    parser.add_argument('--input_channels', default=3, type=int)
    parser.add_argument('--output_num', default=10, type=int)
    parser.add_argument('--input_size', default=32, type=int)
    args = parser.parse_args()
    
    model = Alexnet(args.input_channels, args.output_num, args.input_size)
    tensor = torch.rand(1, args.input_channels, args.input_size, args.input_size)
    pred = model(tensor)
    
    print(model)
    print("Predictions shape:", pred.shape)