vgg.py

import torch
import torch.nn as nn

VGG_types = {
    "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",
    ],
}


class VGG(nn.Module):
    def __init__(self, in_channels, num_classes, architecture):
        super(VGG, self).__init__()
        self.in_channels = in_channels
        self.num_classes = num_classes
        self.architecture = architecture
        self.conv_layers = self.create_conv_layers(architecture)

        self.fc = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(),
            nn.Dropout(p=0.5),
            nn.Linear(4096, 4096),
            nn.ReLU(),
            nn.Linear(4096, num_classes),
        )

    def forward(self, x):
        x = self.conv_layers(x)
        x = x.reshape(x.shape[0], -1)
        x = self.fc(x)
        return x

    def create_conv_layers(self, architecture):
        layers = []
        in_channels = self.in_channels

        for x in architecture:
            if type(x) == int:
                out_channels = x

                layers += [
                    nn.Conv2d(
                        in_channels=in_channels,
                        out_channels=out_channels,
                        kernel_size=(3, 3),
                        stride=(1, 1),
                        padding=(1, 1),
                    ),
                    nn.BatchNorm2d(x),
                    nn.ReLU(),
                ]

                in_channels = x

            elif x == "M":
                layers += [nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2))]

        return nn.Sequential(*layers)


if __name__ == "__main__":
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model = VGG(3, 1000, VGG_types["VGG16"]).to(device)
    x = torch.randn(1, 3, 224, 224)
    print(model(x).shape)