discriminator.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""ANIMESH BALA ANI"""

# Import Modules
import torch
import torch.nn as nn


# CNN Block Class
class CNNBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=2):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, 4, stride, bias=False, padding_mode='reflect'),
            nn.BatchNorm2d(out_channels),
            nn.LeakyReLU(0.2),
        )
    
    def forward(self, x):
        return self.conv(x)


# Discriminator Model Class
class Discriminator(nn.Module):   
    def __init__(self, in_channels=3, features=[64, 128, 258, 512]):
        super().__init__()
        self.initial = nn.Sequential(
            nn.Conv2d(in_channels*2, features[0], 4, 2, 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, 4, 1, 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)
        return self.model(x) 





# Test Block
def test():
    x = torch.randn((1, 3, 1920,  1080))
    y = torch.randn((1, 3, 1920,  1080))
    model = Discriminator()
    preds = model(x, y)
    print(preds.shape)

if __name__ == "__main__":
    test()