model_watermark/model/VGG19.py

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

_cfg = {
    'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
    'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}

def _make_layers(cfg, input_size):
    layers = []
    in_channels = 3
    for layer_cfg in cfg:
        if layer_cfg == 'M':
            layers.append(nn.MaxPool2d(kernel_size=2, stride=2))
            input_size = input_size // 2
        else:
            layers.append(nn.Conv2d(in_channels=in_channels, out_channels=layer_cfg, kernel_size=3, stride=1, padding=1))
            layers.append(nn.BatchNorm2d(num_features=layer_cfg))
            layers.append(nn.ReLU(inplace=True))
            in_channels = layer_cfg
    return nn.Sequential(*layers), input_size

class VGG(nn.Module):
    def __init__(self, name, input_size=32, num_classes=10):
        super(VGG, self).__init__()
        cfg = _cfg[name]
        self.features, final_size = _make_layers(cfg, input_size)
        self.fc = nn.Linear(512 * final_size * final_size, num_classes)
        
    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

def VGG11():
    return VGG('VGG11')

def VGG13():
    return VGG('VGG13')

def VGG16():
    return VGG('VGG16')

def VGG19():
    return VGG('VGG19')

if __name__ == '__main__':
    import argparse

    parser = argparse.ArgumentParser(description='VGG Model Test')
    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 = VGG19()  # Changed to use VGG19
    tensor = torch.rand(1, args.input_channels, args.input_size, args.input_size)
    pred = model(tensor)
    
    print(model)
    print("Predictions shape:", pred.shape)