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,padding_mode='reflect',bias=False),
            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*2,features[0],kernel_size=4,stride=2,padding=1,padding_mode='reflect'),
            nn.LeakyReLU(0.2)
        ) # according to paper 64 channel doesn't contain BatchNorm2d
        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

def test():
    x = torch.randn((1, 3, 256, 256))
    y = torch.randn((1, 3, 256, 256))
    model = Discriminator(in_channels=3)
    preds = model(x, y)
    print(model)
    print(preds.shape)


if __name__ == "__main__":
    test()