Discriminator.py

import torch
import torch.nn as nn

class Discriminator(nn.Module):
    """Some Information about Discriminator"""
    def __init__(self, channels_img, features_d):
        super(Discriminator, self).__init__()
        self.dicriminator = nn.Sequential(
            #Input : N * Channels * 64 * 64
            nn.Conv2d(
                channels_img, features_d, kernel_size=4, stride=2, padding=1
            ),
            # 32 * 32
            nn.LeakyReLU(0.2),
            self._block(features_d, features_d*2, 4, 2, 1), # 16 * 16
            self._block(features_d*2, features_d*4, 4, 2, 1), # 8 * 8
            self._block(features_d*4, features_d*8, 4, 2, 1), # 4 * 4
            nn.Conv2d(features_d*8, 1, kernel_size=4, stride=2, padding=0), # 1 * 1
            nn.Sigmoid(),
        )

    def _block(self, in_channels, out_channels, kernel_size, stride, padding):
        return nn.Sequential(
            nn.Conv2d(
                in_channels, out_channels, kernel_size, stride, padding, bias=False
            ),
            nn.BatchNorm2d(out_channels),
            nn.LeakyReLU(0.2),
        )
    
    def forward(self, x):
        return self.dicriminator(x)