app.py

import os
import torch
from IPython.display import Image
from torchvision.utils import save_image
import torchvision
from torchvision.transforms import ToTensor, Normalize, Compose
from torchvision.datasets import MNIST
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import torch.nn as nn
import cv2
import os
from IPython.display import FileLink

mnist = MNIST(root='data', 
              train=True, 
              download=True,
              transform=Compose([ToTensor(), Normalize(mean=(0.5,), std=(0.5,))]))
img, label = mnist[0]
print('Label: ', label)
print(img[:,10:15,10:15])
torch.min(img), torch.max(img)
def denorm(x):
    out = (x + 1) / 2
    return out.clamp(0, 1)

img_norm = denorm(img)
plt.imshow(img_norm[0], cmap='gray')
print('Label:', label)

batch_size = 100
data_loader = DataLoader(mnist, batch_size, shuffle=True)

for img_batch, label_batch in data_loader:
    print('first batch')
    print(img_batch.shape)
    plt.imshow(img_batch[0][0], cmap='gray')
    print(label_batch)
    break

# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

image_size = 784
hidden_size = 256

D = nn.Sequential(
    nn.Linear(image_size, hidden_size),
    nn.LeakyReLU(0.2),
    nn.Linear(hidden_size, hidden_size),
    nn.LeakyReLU(0.2),
    nn.Linear(hidden_size, 1),
    nn.Sigmoid())

D.to(device);

latent_size = 64

G = nn.Sequential(
    nn.Linear(latent_size, hidden_size),
    nn.ReLU(),
    nn.Linear(hidden_size, hidden_size),
    nn.ReLU(),
    nn.Linear(hidden_size, image_size),
    nn.Tanh())

y = G(torch.randn(2, latent_size))
gen_imgs = denorm(y.reshape((-1, 28,28)).detach())

plt.imshow(gen_imgs[0], cmap='gray');

plt.imshow(gen_imgs[1], cmap='gray');

G.to(device);

criterion = nn.BCELoss()
d_optimizer = torch.optim.Adam(D.parameters(), lr=0.0002)

def reset_grad():
    d_optimizer.zero_grad()
    g_optimizer.zero_grad()

def train_discriminator(images):
    # Create the labels which are later used as input for the BCE loss
    real_labels = torch.ones(batch_size, 1).to(device)
    fake_labels = torch.zeros(batch_size, 1).to(device)
        
    # Loss for real images
    outputs = D(images)
    d_loss_real = criterion(outputs, real_labels)
    real_score = outputs

    # Loss for fake images
    z = torch.randn(batch_size, latent_size).to(device)
    fake_images = G(z)
    outputs = D(fake_images)
    d_loss_fake = criterion(outputs, fake_labels)
    fake_score = outputs

    # Combine losses
    d_loss = d_loss_real + d_loss_fake
    # Reset gradients
    reset_grad()
    # Compute gradients
    d_loss.backward()
    # Adjust the parameters using backprop
    d_optimizer.step()
    
    return d_loss, real_score, fake_score

g_optimizer = torch.optim.Adam(G.parameters(), lr=0.0002)

def train_generator():
    # Generate fake images and calculate loss
    z = torch.randn(batch_size, latent_size).to(device)
    fake_images = G(z)
    labels = torch.ones(batch_size, 1).to(device)
    g_loss = criterion(D(fake_images), labels)

    # Backprop and optimize
    reset_grad()
    g_loss.backward()
    g_optimizer.step()
    return g_loss, fake_images

sample_dir = 'samples'
if not os.path.exists(sample_dir):
    os.makedirs(sample_dir)

# Save some real images
for images, _ in data_loader:
    images = images.reshape(images.size(0), 1, 28, 28)
    save_image(denorm(images), os.path.join(sample_dir, 'real_images.png'), nrow=10)
    break
   
Image(os.path.join(sample_dir, 'real_images.png'))

sample_vectors = torch.randn(batch_size, latent_size).to(device)

def save_fake_images(index):
    fake_images = G(sample_vectors)
    fake_images = fake_images.reshape(fake_images.size(0), 1, 28, 28)
    fake_fname = 'fake_images-{0:0=4d}.png'.format(index)
    print('Saving', fake_fname)
    save_image(denorm(fake_images), os.path.join(sample_dir, fake_fname), nrow=10)
    
# Before training
save_fake_images(0)
Image(os.path.join(sample_dir, 'fake_images-0000.png'))

num_epochs = 300
total_step = len(data_loader)
d_losses, g_losses, real_scores, fake_scores = [], [], [], []

for epoch in range(num_epochs):
    for i, (images, _) in enumerate(data_loader):
        # Load a batch & transform to vectors
        images = images.reshape(batch_size, -1).to(device)
        
        # Train the discriminator and generator
        d_loss, real_score, fake_score = train_discriminator(images)
        g_loss, fake_images = train_generator()
        
        # Inspect the losses
        if (i+1) % 200 == 0:
            d_losses.append(d_loss.item())
            g_losses.append(g_loss.item())
            real_scores.append(real_score.mean().item())
            fake_scores.append(fake_score.mean().item())
            print('Epoch [{}/{}], Step [{}/{}], d_loss: {:.4f}, g_loss: {:.4f}, D(x): {:.2f}, D(G(z)): {:.2f}' 
                  .format(epoch, num_epochs, i+1, total_step, d_loss.item(), g_loss.item(), 
                          real_score.mean().item(), fake_score.mean().item()))
        
    # Sample and save images
    save_fake_images(epoch+1)

# Save the model checkpoints 
torch.save(G.state_dict(), 'G.ckpt')
torch.save(D.state_dict(), 'D.ckpt')

Image('./samples/fake_images-0010.png')

Image('./samples/fake_images-0050.png')

Image('./samples/fake_images-0100.png')

Image('./samples/fake_images-0300.png')

vid_fname = 'gans_training.avi'

files = [os.path.join(sample_dir, f) for f in os.listdir(sample_dir) if 'fake_images' in f]
files.sort()

out = cv2.VideoWriter(vid_fname,cv2.VideoWriter_fourcc(*'MP4V'), 8, (302,302))
[out.write(cv2.imread(fname)) for fname in files]
out.release()
FileLink('gans_training.avi')

plt.plot(d_losses, '-')
plt.plot(g_losses, '-')
plt.xlabel('epoch')
plt.ylabel('loss')
plt.legend(['Discriminator', 'Generator'])
plt.title('Losses');

plt.plot(real_scores, '-')
plt.plot(fake_scores, '-')
plt.xlabel('epoch')
plt.ylabel('score')
plt.legend(['Real Score', 'Fake score'])
plt.title('Scores');