main_gan.py

# %%
from __future__ import print_function
import argparse
import os
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision.utils as vutils
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from IPython.display import HTML

# check for GPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)

# %%
# Set random seed
manualSeed = 999
print('Random Seed: ', manualSeed)

random.seed(manualSeed)
torch.manual_seed(manualSeed)

# %%

dataroot = 'C:/Users/burro/Masters/DATA/GAN Data/final/'

workers = 2
# batch size
batch_size = 64

image_size = 64

# number of colour channels: RGB
nc = 3

# length of latent vector
nz = 150

# generator feature map
ngf = 64

# discriminator feature map
ndf = 64

num_epochs = 16000

lr = 0.0002

beta1 = 0.5

# Number of GPUs available
ngpu = 1

# %%
# Load and normalize images
dataset = dset.ImageFolder(root=dataroot,
                           transform=transforms.Compose([
                               transforms.Resize(image_size),
                               transforms.CenterCrop(image_size),
                               transforms.ToTensor(),
                               transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
                           ]))

dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
                                         shuffle=True, num_workers=workers)

# Plot a batch of real images
real_batch = next(iter(dataloader))
plt.figure(figsize=(8, 8))
plt.axis('off')
plt.title('Training Images')
plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=2, normalize=True).cpu(), (1, 2, 0)))
plt.show()


# %%
# Initialise weights
def weights_init(m):
    classname = m.__class__.__name__
    # find any layers using the 'conv' prefix
    if classname.find('Conv') != -1:
        nn.init.normal_(m.weight.data, 0.0, 0.02)
    # find BN layers
    elif classname.find('BatchNorm') != -1:
        nn.init.normal_(m.weight.data, 1.0, 0.02)
        nn.init.constant_(m.bias.data, 0)


# %%
class Generator(nn.Module):
    def __init__(self, ngpu):
        super(Generator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(True),

            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(True),

            nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 2),
            nn.ReLU(True),

            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf),
            nn.ReLU(True),

            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
            nn.Tanh()
        )

    def forward(self, input):
        return self.main(input)


# %% GENERATOR
netG = Generator(ngpu).to(device)

netG.apply(weights_init)

print(netG)


# %% DISCRIMINATOR
class Discriminator(nn.Module):
    def __init__(self, ngpu):
        super(Discriminator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 2),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 4),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 8),
            nn.LeakyReLU(0.2, inplace=True),

            nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )

    def forward(self, input):
        return self.main(input)


# %%
netD = Discriminator(ngpu).to(device)

netD.apply(weights_init)

print(netD)

# %%
# Binary Crossentropy
criterion = nn.BCELoss()

fixed_noise = torch.randn(64, nz, 1, 1, device=device)

real_label = 1.
fake_label = 0.

optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))

# %%
torch.cuda.get_device_name(0)

# %%
img_list = []
G_losses = []
D_losses = []
iters = 0

print('Starting Training Loop...')
for epoch in range(num_epochs):
    for i, data in enumerate(dataloader, 0):
        netD.zero_grad()

        real_cpu = data[0].to(device)
        b_size = real_cpu.size(0)
        label = torch.full((b_size,), real_label, dtype=torch.float, device=device)
        output = netD(real_cpu).view(-1)
        errD_real = criterion(output, label)
        errD_real.backward()
        D_x = output.mean().item()

        noise = torch.randn(b_size, nz, 1, 1, device=device)
        fake = netG(noise)
        label.fill_(fake_label)
        output = netD(fake.detach()).view(-1)
        errD_fake = criterion(output, label)
        errD_fake.backward()
        D_G_z1 = output.mean().item()
        errD = errD_real + errD_fake
        optimizerD.step()

        netG.zero_grad()
        label.fill_(real_label)
        output = netD(fake).view(-1)
        errG = criterion(output, label)
        errG.backward()
        D_G_z2 = output.mean().item()
        optimizerG.step()

        # output training statistics
        if i % 1 == 0:
            print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f' % (epoch,
                                                                                                    num_epochs,
                                                                                                    i,
                                                                                                    len(dataloader),
                                                                                                    errD.item(),
                                                                                                    errG.item(),
                                                                                                    D_x,
                                                                                                    D_G_z1,
                                                                                                    D_G_z2))
        # save losses for plotting later
        G_losses.append(errG.item())
        D_losses.append(errD.item())

        # Save generator output every 500 iterations
        if (iters % 500 == 0) or ((epoch == num_epochs - 1) and (i == len(dataloader) - 1)):
            with torch.no_grad():
                fake = netG(fixed_noise).detach().cpu()
            img_list.append(vutils.make_grid(fake, padding=2, normalize=True))

            plt.axis("off")
            plt.title("Fake Images")
            plt.imshow(np.transpose(img_list[-1], (1, 2, 0)))
            plt.savefig('C:/Users/burro/Desktop/Final Scripts/Pytorch_GAN/generated_imgs/{}_plot.png'.format(iters))

        iters += 1

# %% Save generator model
torch.save(netG, '/content/gdrive/MyDrive/Shared HL/Holly MSc Thesis/GAN/netG800.pt')

# %% Plot generator and discriminator losses
plt.figure(figsize=(10, 5))
plt.title("Generator and Discriminator Loss During Training")
plt.plot(G_losses, label="G")
plt.plot(D_losses, label="D")
plt.xlabel("iterations")
plt.ylabel("Loss")
plt.legend()
plt.show()

# %% FID SCORE

# %%
params = {
    "bsize": 64,  # Batch size during training.
    'imsize': 64,  # Spatial size of training images. All images will be resized to this size during preprocessing.
    'nc': 3,  # Number of channles in the training images. For coloured images this is 3.
    'nz': 150,  # Size of the Z latent vector (the input to the generator).
    'ngf': 64,  # Size of feature maps in the generator. The depth will be multiples of this.
    'ndf': 64,  # Size of features maps in the discriminator. The depth will be multiples of this.
    'nepochs': 16000,  # Number of training epochs.
    'lr': 0.0002,  # Learning rate for optimizers
    'beta1': 0.5,  # Beta1 hyperparam for Adam optimizer
    'save_epoch': 5306}

# %% Save generator/disciminator and both optimizers
torch.save({
    'generator': netG.state_dict(),
    'discriminator': netD.state_dict(),
    'optimizerG': optimizerG.state_dict(),
    'optimizerD': optimizerD.state_dict(),
    'params': params
}, 'models/model_final.pt')