model.py

import torch
import torch.nn as nn

NOISE_DIM = 256

class Generator(nn.Module):
    def __init__(self):
        super().__init__()

        self.fc = nn.Linear(NOISE_DIM, 4*4*512)

        self.net = nn.Sequential(
            nn.BatchNorm2d(512),
            nn.Upsample(scale_factor=2),
            nn.Conv2d(512, 256, 3, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(True),

            nn.Upsample(scale_factor=2),
            nn.Conv2d(256, 128, 3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(True),

            nn.Upsample(scale_factor=2),
            nn.Conv2d(128, 64, 3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(True),

            nn.Upsample(scale_factor=2),
            nn.Conv2d(64, 3, 3, padding=1),
            nn.Tanh()
        )

    def forward(self, noise):
        x = self.fc(noise)
        x = x.view(-1, 512, 4, 4)
        return self.net(x)