wgan_gp.py

import argparse
import tensorflow as tf
import glob
# import imageio
import matplotlib.pyplot as plt
import numpy as np
import os
import PIL
import tensorflow.keras.layers as layers
import time
from functools import partial


parser = argparse.ArgumentParser()
parser.add_argument("--n_epochs", type=int, default=200, help="number of epochs of training")
parser.add_argument("--freq", type=int, default=1, help="number of epochs of saving")
parser.add_argument("--batch_size", type=int, default=512, help="size of the batches")
parser.add_argument("--buffer_size", type=int, default=60000, help="size of the buffers")
parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
parser.add_argument("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation")
parser.add_argument("--latent_dim", type=int, default=100, help="dimensionality of the latent space")
parser.add_argument("--img_size", type=int, default=28, help="size of each image dimension")
parser.add_argument("--channels", type=int, default=1, help="number of image channels")
parser.add_argument("--sample_interval", type=int, default=400, help="interval betwen image samples")
opt = parser.parse_args()
print(opt)

img_shape = (opt.img_size, opt.img_size, opt.channels)
# Loss weight for gradient penalty
lambda_gp = 10


# data load & preprocessing
(train_x, _), (_, _) = tf.keras.datasets.mnist.load_data()
train_x= train_x.reshape(train_x.shape[0], 28, 28, 1).astype('float32')
BUFFER_SIZE=train_x.shape[0]
train_x = (train_x - 127.5) / 127.5
train_ds = tf.data.Dataset.from_tensor_slices(train_x).shuffle(BUFFER_SIZE).batch(opt.batch_size)
num_examples_to_generate = 16

# We will reuse this seed overtime (so it's easier)
# to visualize progress in the animated GIF)
seed = tf.random.normal([num_examples_to_generate, opt.latent_dim])




# define discriminator
def make_discriminator(input_shape):
    return tf.keras.Sequential([
        layers.Conv2D(64, 5, strides=2, padding='same',
                      input_shape=input_shape),
        layers.LeakyReLU(),
        layers.Dropout(0.3),
        layers.Conv2D(128, 5, strides=2, padding='same'),
        layers.LeakyReLU(),
        layers.Dropout(0.3),
        layers.Flatten(),
        layers.Dense(1)
    ])


# define generator
def make_generator(input_shape):
    return tf.keras.Sequential([
        layers.Dense(7*7*256, use_bias=False, input_shape=input_shape),
        layers.BatchNormalization(),
        layers.LeakyReLU(),
        layers.Reshape((7, 7, 256)),
        layers.Conv2DTranspose(
            128, 5, strides=1, padding='same', use_bias=False),
        layers.BatchNormalization(),
        layers.LeakyReLU(),
        layers.Conv2DTranspose(
            64, 5, strides=2, padding='same', use_bias=False),
        layers.BatchNormalization(),
        layers.LeakyReLU(),
        layers.Conv2DTranspose(
            1, 5, strides=2, padding='same', use_bias=False, activation='tanh')
    ])



def get_random_z(z_dim, batch_size):
    return tf.random.uniform([batch_size, z_dim], minval=-1, maxval=1)


# Initialize generator and discriminator
generator = make_generator((opt.latent_dim,))
discriminator = make_discriminator(img_shape)

def generator_loss(fake_output):
    return -tf.reduce_mean(fake_output)

def discriminator_loss(real_output, fake_output):
    return tf.reduce_mean(fake_output) - tf.reduce_mean(real_output)


generator_optimizer = tf.keras.optimizers.Adam(opt.lr, beta_1=0.5, beta_2=0.999)
discriminator_optimizer = tf.keras.optimizers.Adam(opt.lr, beta_1=0.5, beta_2=0.999)

checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,
                                 discriminator_optimizer=discriminator_optimizer,
                                 generator=generator,
                                 discriminator=discriminator)

# Gradient Penalty (GP)
def gradient_penalty(generator, real_images, fake_images):
    real_images = tf.cast(real_images, tf.float32)
    fake_images = tf.cast(fake_images, tf.float32)
    alpha = tf.random.uniform([real_images.shape[0], 1, 1, 1], 0., 1.)
    diff = fake_images - real_images
    inter = real_images + (alpha * diff)
    with tf.GradientTape() as tape:
        tape.watch(inter)
        predictions = generator(inter)
    gradients = tape.gradient(predictions, [inter])[0]
    slopes = tf.sqrt(tf.reduce_sum(tf.square(gradients), axis=[1, 2, 3]))
    return tf.reduce_mean((slopes - 1.) ** 2)

@tf.function
def train_step(images):
    noise = get_random_z(opt.latent_dim, images.shape[0])

    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
      generated_images = generator(noise, training=True)

      real_output = discriminator(images, training=True)
      fake_output = discriminator(generated_images, training=True)

      gen_loss = generator_loss(fake_output)
      disc_loss = discriminator_loss(real_output, fake_output)
      # Gradient penalty
      gp = gradient_penalty(partial(discriminator, training=True),images, generated_images)
      disc_loss += gp * lambda_gp


    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)
    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))
    return gen_loss, disc_loss

# ----------
#  Training
# ----------
def train(dataset, epochs):
    for epoch in range(epochs):
        start = time.time()


        for batch_idx, image_batch in enumerate(dataset):
            g_loss, d_loss = train_step(image_batch)
            g_loss_metrics(g_loss)
            d_loss_metrics(d_loss)
            total_loss_metrics(g_loss + d_loss)

            template = '[Epoch{}/{}], Batch[{}/{}] D_loss={:.5f} G_loss={:.5f} Total_loss={:.5f}'
            print(template.format(epoch, epochs, batch_idx, len(dataset), d_loss_metrics.result(),
                                  g_loss_metrics.result(), total_loss_metrics.result()))
            g_loss_metrics.reset_states()
            d_loss_metrics.reset_states()
            total_loss_metrics.reset_states()

        # Produce images for the GIF as we go
        generate_and_save_images(generator,epoch + 1, seed)

        # Save the model every 15 epochs
        if (epoch + 1) % opt.freq == 0:
            checkpoint.save(file_prefix=checkpoint_prefix)

        print('Time for epoch {} is {} sec'.format(epoch + 1, time.time() - start))

    # # Generate after the final epoch
    generate_and_save_images(generator,epochs,seed)
# metrics setting
g_loss_metrics = tf.metrics.Mean(name='g_loss')
d_loss_metrics = tf.metrics.Mean(name='d_loss')
total_loss_metrics = tf.metrics.Mean(name='total_loss')

def generate_and_save_images(model, epoch, test_input):
    # Notice `training` is set to False.
    # This is so all layers run in inference mode (batchnorm).
    predictions = model(test_input, training=False)

    fig = plt.figure(figsize=(4, 4))

    for i in range(predictions.shape[0]):
        plt.subplot(4, 4, i + 1)
        plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')
        plt.axis('off')

    plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
    # plt.show()
if __name__ == "__main__":
    # gpus = tf.config.experimental.list_physical_devices("GPU")
    # if gpus:
    #     try:
    #         for gpu in gpus:
    #             tf.config.experimental.set_memory_growth(gpu, True)
    #     except RuntimeError as e:
    #         print(e)
    #         exit(-1)
    train(train_ds,opt.n_epochs)