model.py

import os
import torch
import torch.nn as nn

class Generator(nn.Module):
    def __init__(self, latent_dim = 100, out_channel=3):
        super(Generator, self).__init__()

        self.conv_block = nn.Sequential(
            nn.ConvTranspose2d(in_channels=latent_dim, out_channels=512, kernel_size=4, stride=1, padding=0),
            nn.BatchNorm2d(num_features=512),
            nn.ReLU(True),

            nn.ConvTranspose2d(in_channels=512, out_channels=256, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(num_features=256),
            nn.ReLU(True),

            nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(num_features=128),
            nn.ReLU(True),

            nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(True),

            nn.ConvTranspose2d(in_channels=64, out_channels=out_channel, kernel_size=4, stride=2, padding=1),
            nn.Tanh()
        )

    def forward(self, x):
        x = self.conv_block(x)
        return x


class Discriminator(nn.Module):
    def __init__(self,in_channel=3):
        super(Discriminator, self).__init__()

        self.conv_block = nn.Sequential(
            nn.Conv2d(in_channels=in_channel, out_channels=64, kernel_size=4, stride=2, padding=1),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(in_channels=256, out_channels=512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(in_channels=512, out_channels=1, kernel_size=4, stride=1, padding=0),
            nn.Sigmoid()
        )


    def forward(self, x):
        x = self.conv_block(x)
        return x