VGGNet.py

import torch.nn as nn

class VGGNet(nn.Module):
    def __init__(self, layer_num=16, label_num = 10, dropout=0):
        super(VGGNet, self).__init__()
        self.layer_num = layer_num

        self.conv_pool_1 = nn.Sequential(
            nn.Conv2d(in_channels=1, out_channels=4, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(in_channels=4, out_channels=4, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=1, padding=1)
        )

        self.conv_pool_2 = nn.Sequential(
            nn.Conv2d(in_channels=4, out_channels=8, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(in_channels=8, out_channels=8, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=1, padding=1)
        )

        self.conv_pool_3 = nn.Sequential(
            nn.Conv2d(in_channels=8, out_channels=16, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )

        self.conv_pool_4 = nn.Sequential(
            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )

        self.conv_pool_5 = nn.Sequential(
            nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )

        if self.layer_num == 19:
            self.conv_pool_6 = nn.Sequential(
                nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1),
                nn.ReLU(),
                nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1),
                nn.ReLU(),
                nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1),
                nn.ReLU(),
                nn.MaxPool2d(kernel_size=3, stride=1, padding=1)          
            )
        
        self.fc = nn.Sequential(
            nn.Flatten(),
            nn.Dropout(dropout),
            nn.Linear(32 * 3 * 3, 256),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Linear(256, 256),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Linear(256, 256),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Linear(256, label_num)
        )

    def forward(self, x):
        x = self.conv_pool_1(x)
        x = self.conv_pool_2(x)
        x = self.conv_pool_3(x)
        x = self.conv_pool_4(x)
        x = self.conv_pool_5(x)
        if self.layer_num == 19:
            x = self.conv_pool_6(x)
        x = self.fc(x)
        return x