discriminator.py

import torch
import torch.nn as nn


class CNNBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=2):
        super(CNNBlock, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, 4, stride, 1, bias=False, padding_mode="reflect"),
            nn.BatchNorm2d(out_channels),
            nn.LeakyReLU(0.2),
        )

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


class Discriminator(nn.Module):
    def __init__(self, in_channels=3, 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(
                CNNBlock(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, y):
        x = torch.cat([x, y], dim=1)
        x = self.initial(x)   
        x = self.model(x)
        return x