model/Discriminator.py

import os
import glob
import time
import numpy as np
from PIL import Image
from pathlib import Path
from tqdm.notebook import tqdm
import matplotlib.pyplot as plt
from skimage.color import rgb2lab, lab2rgb

import torch
from torch import nn, optim
from torchvision import transforms
from torchvision.utils import make_grid
from torch.utils.data import Dataset, DataLoader

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


class PatchDiscriminator(nn.Module):
    def __init__(self, input_c, num_filters=64, n_down=3):
        super().__init__()
        model = [self.get_layers(input_c, num_filters, norm=False)]
        model += [
            self.get_layers(
                num_filters * 2**i,
                num_filters * 2 ** (i + 1),
                s=1 if i == (n_down - 1) else 2,
            )
            for i in range(n_down)
        ]  # the 'if' statement is taking care of not using
        # stride of 2 for the last block in this loop
        model += [
            self.get_layers(num_filters * 2**n_down, 1, s=1, norm=False, act=False)
        ]  # Make sure to not use normalization or
        # activation for the last layer of the model
        self.model = nn.Sequential(*model)

    def get_layers(
        self, ni, nf, k=4, s=2, p=1, norm=True, act=True
    ):  # when needing to make some repeatitive blocks of layers,
        layers = [
            nn.Conv2d(ni, nf, k, s, p, bias=not norm)
        ]  # it's always helpful to make a separate method for that purpose
        if norm:
            layers += [nn.BatchNorm2d(nf)]
        if act:
            layers += [nn.LeakyReLU(0.2, True)]
        return nn.Sequential(*layers)

    def forward(self, x):
        return self.model(x)