StyleGAN tutorial in Python interpreter

style-gan-1.py

"""
https://www.tensorflow.org/tutorials/generative/cyclegan
pip install IPython tensorflow-rocm tensorflow_datasets matplotlib
pip install git+https://github.com/tensorflow/examples.git

"""

import tensorflow as tf

import tensorflow_datasets as tfds
from tensorflow_examples.models.pix2pix import pix2pix

import os
import time
import matplotlib.pyplot as plt
from IPython.display import clear_output

AUTOTUNE = tf.data.experimental.AUTOTUNE
plt.interactive(True) # for windows when running from python interpreter

dataset, metadata = tfds.load('cycle_gan/horse2zebra',
                              with_info=True, as_supervised=True)

train_horses, train_zebras = dataset['trainA'], dataset['trainB']
test_horses, test_zebras = dataset['testA'], dataset['testB']

BUFFER_SIZE = 1000
BATCH_SIZE = 1
IMG_WIDTH = 256
IMG_HEIGHT = 256


def random_crop(image):
  cropped_image = tf.image.random_crop(
      image, size=[IMG_HEIGHT, IMG_WIDTH, 3])
  return cropped_image


def normalize(image):
  image = tf.cast(image, tf.float32)
  image = (image / 127.5) - 1
  return image


def random_jitter(image):
  # resizing to 286 x 286 x 3
  image = tf.image.resize(image, [286, 286],
                          method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
  # randomly cropping to 256 x 256 x 3
  image = random_crop(image)
  # random mirroring
  image = tf.image.random_flip_left_right(image)
  return image


def preprocess_image_train(image, label):
  image = random_jitter(image)
  image = normalize(image)
  return image


def preprocess_image_test(image, label):
  image = normalize(image)
  return image


train_horses = train_horses.cache().map(
    preprocess_image_train, num_parallel_calls=AUTOTUNE).shuffle(
    BUFFER_SIZE).batch(BATCH_SIZE)

train_zebras = train_zebras.cache().map(
    preprocess_image_train, num_parallel_calls=AUTOTUNE).shuffle(
    BUFFER_SIZE).batch(BATCH_SIZE)

test_horses = test_horses.map(
    preprocess_image_test, num_parallel_calls=AUTOTUNE).cache().shuffle(
    BUFFER_SIZE).batch(BATCH_SIZE)

test_zebras = test_zebras.map(
    preprocess_image_test, num_parallel_calls=AUTOTUNE).cache().shuffle(
    BUFFER_SIZE).batch(BATCH_SIZE)

#
# some demonstration 
#

sample_horse = next(iter(train_horses))
sample_zebra = next(iter(train_zebras))


# plt.subplot(121)
# plt.title('Horse')
# plt.imshow(sample_horse[0] * 0.5 + 0.5)

# plt.subplot(122)
# plt.title('Horse with random jitter')
# plt.imshow(random_jitter(sample_horse[0]) * 0.5 + 0.5)

# plt.subplot(121)
# plt.title('Zebra')
# plt.imshow(sample_zebra[0] * 0.5 + 0.5)

# plt.subplot(122)
# plt.title('Zebra with random jitter')
# plt.imshow(random_jitter(sample_zebra[0]) * 0.5 + 0.5)

OUTPUT_CHANNELS = 3
generator_g = pix2pix.unet_generator(OUTPUT_CHANNELS, norm_type='instancenorm')
generator_f = pix2pix.unet_generator(OUTPUT_CHANNELS, norm_type='instancenorm')
discriminator_x = pix2pix.discriminator(norm_type='instancenorm', target=False)
discriminator_y = pix2pix.discriminator(norm_type='instancenorm', target=False)

# to_zebra = generator_g(sample_horse)
# to_horse = generator_f(sample_zebra)
# plt.figure(figsize=(8, 8))
# contrast = 8

# imgs = [sample_horse, to_zebra, sample_zebra, to_horse]
# title = ['Horse', 'To Zebra', 'Zebra', 'To Horse']

# for i in range(len(imgs)):
#   plt.subplot(2, 2, i+1)
#   plt.title(title[i])
#   if i % 2 == 0:
#     plt.imshow(imgs[i][0] * 0.5 + 0.5)
#   else:
#     plt.imshow(imgs[i][0] * 0.5 * contrast + 0.5)


# plt.show()


# plt.figure(figsize=(8, 8))

# plt.subplot(121)
# plt.title('Is a real zebra?')
# plt.imshow(discriminator_y(sample_zebra)[0, ..., -1], cmap='RdBu_r')

# plt.subplot(122)
# plt.title('Is a real horse?')
# plt.imshow(discriminator_x(sample_horse)[0, ..., -1], cmap='RdBu_r')

# plt.show()


LAMBDA = 10
loss_obj = tf.keras.losses.BinaryCrossentropy(from_logits=True)

def discriminator_loss(real, generated):
  real_loss = loss_obj(tf.ones_like(real), real)
  generated_loss = loss_obj(tf.zeros_like(generated), generated)
  total_disc_loss = real_loss + generated_loss
  return total_disc_loss * 0.5


def generator_loss(generated):
  return loss_obj(tf.ones_like(generated), generated)


def calc_cycle_loss(real_image, cycled_image):
  loss1 = tf.reduce_mean(tf.abs(real_image - cycled_image))
  return LAMBDA * loss1


def identity_loss(real_image, same_image):
  loss = tf.reduce_mean(tf.abs(real_image - same_image))
  return LAMBDA * 0.5 * loss


generator_g_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
generator_f_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)

discriminator_x_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
discriminator_y_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)


checkpoint_path = "./checkpoints/train"

ckpt = tf.train.Checkpoint(generator_g=generator_g,
                           generator_f=generator_f,
                           discriminator_x=discriminator_x,
                           discriminator_y=discriminator_y,
                           generator_g_optimizer=generator_g_optimizer,
                           generator_f_optimizer=generator_f_optimizer,
                           discriminator_x_optimizer=discriminator_x_optimizer,
                           discriminator_y_optimizer=discriminator_y_optimizer)


ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)

# if a checkpoint exists, restore the latest checkpoint.
if ckpt_manager.latest_checkpoint:
  ckpt.restore(ckpt_manager.latest_checkpoint)
  print ('Latest checkpoint restored!!')

## Training

EPOCHS = 40


def generate_images(model, test_input):
  prediction = model(test_input)
  plt.figure(figsize=(12, 12))
  display_list = [test_input[0], prediction[0]]
  title = ['Input Image', 'Predicted Image']
  for i in range(2):
    plt.subplot(1, 2, i+1)
    plt.title(title[i])
    # getting the pixel values between [0, 1] to plot it.
    plt.imshow(display_list[i] * 0.5 + 0.5)
    plt.axis('off')
  plt.show()


@tf.function
def train_step(real_x, real_y):
  # persistent is set to True because the tape is used more than
  # once to calculate the gradients.
  with tf.GradientTape(persistent=True) as tape:
    # Generator G translates X -> Y
    # Generator F translates Y -> X.
    fake_y = generator_g(real_x, training=True)
    cycled_x = generator_f(fake_y, training=True)
    fake_x = generator_f(real_y, training=True)
    cycled_y = generator_g(fake_x, training=True)
    # same_x and same_y are used for identity loss.
    same_x = generator_f(real_x, training=True)
    same_y = generator_g(real_y, training=True)
    disc_real_x = discriminator_x(real_x, training=True)
    disc_real_y = discriminator_y(real_y, training=True)
    disc_fake_x = discriminator_x(fake_x, training=True)
    disc_fake_y = discriminator_y(fake_y, training=True)
    # calculate the loss
    gen_g_loss = generator_loss(disc_fake_y)
    gen_f_loss = generator_loss(disc_fake_x)
    total_cycle_loss = calc_cycle_loss(real_x, cycled_x) + calc_cycle_loss(real_y, cycled_y)
    # Total generator loss = adversarial loss + cycle loss
    total_gen_g_loss = gen_g_loss + total_cycle_loss + identity_loss(real_y, same_y)
    total_gen_f_loss = gen_f_loss + total_cycle_loss + identity_loss(real_x, same_x)
    disc_x_loss = discriminator_loss(disc_real_x, disc_fake_x)
    disc_y_loss = discriminator_loss(disc_real_y, disc_fake_y)
  # Calculate the gradients for generator and discriminator
  generator_g_gradients = tape.gradient(total_gen_g_loss, 
                                        generator_g.trainable_variables)
  generator_f_gradients = tape.gradient(total_gen_f_loss, 
                                        generator_f.trainable_variables)
  discriminator_x_gradients = tape.gradient(disc_x_loss, 
                                            discriminator_x.trainable_variables)
  discriminator_y_gradients = tape.gradient(disc_y_loss, 
                                            discriminator_y.trainable_variables)
  # Apply the gradients to the optimizer
  generator_g_optimizer.apply_gradients(zip(generator_g_gradients, 
                                            generator_g.trainable_variables))
  generator_f_optimizer.apply_gradients(zip(generator_f_gradients, 
                                            generator_f.trainable_variables))
  discriminator_x_optimizer.apply_gradients(zip(discriminator_x_gradients,
                                                discriminator_x.trainable_variables))
  discriminator_y_optimizer.apply_gradients(zip(discriminator_y_gradients,
                                                discriminator_y.trainable_variables))




def iteration(epoch):
  start = time.time()
  n = 0
  for image_x, image_y in tf.data.Dataset.zip((train_horses, train_zebras)):
    train_step(image_x, image_y)
    if n % 10 == 0:
      print ('.', end='')
    n += 1
  clear_output(wait=True)
  # Using a consistent image (sample_horse) so that the progress of the model
  # is clearly visible.
  generate_images(generator_g, sample_horse)
  if (epoch + 1) % 5 == 0:
    ckpt_save_path = ckpt_manager.save()
    print ('Saving checkpoint for epoch {} at {}'.format(epoch+1,
                                                         ckpt_save_path))
  print ('Time taken for epoch {} is {} sec\n'.format(epoch + 1,
                                                      time.time()-start))


for epoch in range(EPOCHS):
  iteration(epoch)