discriminator.py

import torch
import torch.nn as nn


class Block(nn.Module):
    def __init__(self, in_channels, out_channels, stride):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(
                in_channels,
                out_channels,
                kernel_size=4,
                stride=stride,
                padding=1,
                bias=True,
                padding_mode="reflect",
            ),
            nn.InstanceNorm2d(out_channels),
            nn.LeakyReLU(0.2),
        )

    def forward(self, x):
        return self.conv(x)


class Discriminator(nn.Module):
    def __init__(self, in_channels, features=[64, 128, 256, 512]):
        super().__init__()
        self.initial = nn.Sequential(
            nn.Conv2d(
                in_channels,
                features[0],
                kernel_size=4,
                stride=2,
                padding=1,
                padding_mode="reflect",
            ),
            nn.LeakyReLU(0.2),
        )

        layers = []

        in_channels = features[0]

        for feature in features[1:]:
            layers.append(
                Block(in_channels, feature, stride=1 if feature == features[-1] else 2)
            )
            in_channels = feature

        layers.append(
            nn.Conv2d(
                in_channels,
                1,
                kernel_size=4,
                stride=1,
                padding=1,
                padding_mode="reflect",
            )
        )

        self.model = nn.Sequential(*layers)

    def forward(self, x):
        x = self.initial(x)
        return torch.sigmoid(self.model(x))


def test():
    x = torch.randn((2, 3, 256, 256))
    model = Discriminator(in_channels=3)
    preds = model(x)
    print(model)
    print(preds.shape)


if __name__ == "__main__":
    test()