WGAN-GP_FashionMNIST.py

import os
import os.path as osp
from tqdm import tqdm
import tensorflow as tf
from utils import tf_normalize, load_env
import matplotlib.pyplot as plt
from models.dataloaders import FashionMNISTDataLoader
from models.nn.generator import DCGANGenerator
from models.nn.discriminator import DCGANDiscriminator
from models.nn.gan_pipeline import WGAN_GPPipeline
from utils.custom_losses import WassersteinDiscriminatorLoss, WassersteinGeneratorLoss

load_env()

def get_mnist_data():
    (x_train, _), (_, _) = FashionMNISTDataLoader().load_data()
    return tf.data.Dataset.from_tensor_slices(x_train).map(lambda x: tf.expand_dims(tf_normalize(x), axis=-1)).shuffle(buffer_size=buffer_size).batch(batch_size=batch_size)

if __name__ == '__main__':
    OUTPATH = osp.join(os.environ['OUTPATH'], 'Fashion-MNIST_WGAN-GP')
    WGAN_ASSETS = osp.join(os.environ['ASSETS'], 'Fashion-MNIST_WGAN-GP')

    os.makedirs(OUTPATH, exist_ok=True)
    os.makedirs(WGAN_ASSETS, exist_ok=True)

    test_size = 16
    batch_size = 32
    buffer_size = batch_size * 3
    random_noise_size = 100
    epochs = 100
    save_artefact_frequency = 5 # each n epochs

    dataset = get_mnist_data()

    test_images = tf.random.normal([test_size, random_noise_size])

    generator_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5, beta_2=0.9)
    discriminator_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5, beta_2=0.9)

    generator_loss = WassersteinGeneratorLoss()
    discriminator_loss = WassersteinDiscriminatorLoss()

    generator = DCGANGenerator(optimizer=generator_optimizer, input_shape=(random_noise_size,))
    discriminator = DCGANDiscriminator(optimizer=discriminator_optimizer)

    pipeline = WGAN_GPPipeline(generator, 
                            discriminator, 
                            generator_loss, 
                            discriminator_loss)

    for epoch in tqdm(range(epochs), total=epochs, desc='Training Epoch'):
        
        for image_batch in tqdm(dataset, total=len(dataset), desc='Batches', leave=False):
            pipeline.train(image_batch)
            break
        
        if (epoch == 0) or ((epoch+1) % save_artefact_frequency == 0):
            test_output = generator.predict(test_images)
            fig, axes = plt.subplots(4, 4, figsize=(20,20), facecolor='white')
            for out_fig, ax in zip(test_output, axes.ravel()):
                ax.imshow(out_fig, cmap='gray')
                ax.axis('off')
            fig.savefig(osp.join(OUTPATH, f'epoch_{epoch+1}_out_grid.png'))
            plt.close(fig)

    pipeline._generator.save_model(osp.join(WGAN_ASSETS, 'saved_model.hdf5'))