gan-dc.py

from __future__ import print_function, division

import os

import matplotlib.pyplot as plt
import numpy as np
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam

SIZE_X = 128
SIZE_Y = 64
CURRENT_DIR: str = os.getcwd()
DATA_SLICE: int = 1


def get_dataset():
    file_path = os.path.join(CURRENT_DIR, 'data', 'train000.npz')
    train_data = np.load(file_path)['train_data'][:]

    # covert arrays for each pixel
    # form [[15], [18], [255]]
    # to [15, 18, 255]
    train_data_new = []

    for data_index in range(len(train_data)):
        train_data_new_image = []

        for row in range(len(train_data[data_index])):
            new_row = [x[0] for x in train_data[data_index][row]]
            train_data_new_image.append(new_row)

        train_data_new.append(train_data_new_image)

    return train_data_new


class DCGAN:
    def __init__(self):
        # Input shape
        self.img_rows = SIZE_Y
        self.img_cols = SIZE_X
        self.channels = 1
        self.img_shape = (self.img_rows, self.img_cols, self.channels)
        self.latent_dim = 100

        optimizer = Adam(0.0002, 0.5)

        # Build and compile the discriminator
        self.discriminator = self.build_discriminator()
        self.discriminator.compile(loss='binary_crossentropy',
                                   optimizer=optimizer,
                                   metrics=['accuracy'])

        # Build the generator
        self.generator = self.build_generator()

        # The generator takes noise as input and generates imgs
        z = Input(shape=(self.latent_dim,))
        img = self.generator(z)

        # For the combined model we will only train the generator
        self.discriminator.trainable = False

        # The discriminator takes generated images as input and determines validity
        valid = self.discriminator(img)

        # The combined model  (stacked generator and discriminator)
        # Trains the generator to fool the discriminator
        self.combined = Model(z, valid)
        self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)

    def build_generator(self):

        model = Sequential()

        quality = 5  # or 10

        if quality == 5:
            # 16x8
            model.add(Dense(128 * 8 * 16, activation='relu', input_dim=self.latent_dim))
            model.add(Reshape((8, 16, 128)))

            # 32x16
            model.add(UpSampling2D())
            model.add(Conv2D(128, kernel_size=3, padding='same'))
            model.add(BatchNormalization(momentum=0.8))
            model.add(Activation('relu'))

            # 64x32
            model.add(UpSampling2D())
            model.add(Conv2D(128, kernel_size=3, padding='same'))
            model.add(BatchNormalization(momentum=0.8))
            model.add(Activation('relu'))

            # 128x64
            model.add(UpSampling2D())
            model.add(Conv2D(64, kernel_size=3, padding='same'))
            model.add(BatchNormalization(momentum=0.8))
            model.add(Activation('relu'))

            model.add(Conv2D(self.channels, kernel_size=3, padding='same'))
            model.add(Activation('tanh'))

        if quality == 10:
            # 16x8
            model.add(Dense(128 * 8 * 16, activation='relu', input_dim=self.latent_dim))
            model.add(Reshape((8, 16, 128)))

            # 32x16
            model.add(UpSampling2D())
            model.add(Conv2D(256, kernel_size=3, padding='same'))
            model.add(BatchNormalization(momentum=0.8))
            model.add(Activation('relu'))

            # 64x32
            model.add(UpSampling2D())
            model.add(Conv2D(196, kernel_size=3, padding='same'))
            model.add(BatchNormalization(momentum=0.8))
            model.add(Activation('relu'))

            # 128x64
            model.add(UpSampling2D())
            model.add(Conv2D(128, kernel_size=3, padding='same'))
            model.add(BatchNormalization(momentum=0.8))
            model.add(Activation('relu'))

            model.add(Conv2D(self.channels, kernel_size=3, padding='same'))
            model.add(Activation('tanh'))

        model.summary()

        noise = Input(shape=(self.latent_dim,))
        img = model(noise)

        return Model(noise, img)

    def build_discriminator(self):

        model = Sequential()

        quality = 5  # or 10

        if quality == 5:
            model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, padding='same'))
            model.add(LeakyReLU(alpha=0.2))
            model.add(Dropout(0.1))

            model.add(Conv2D(64, kernel_size=3, strides=2, padding='same'))
            model.add(ZeroPadding2D(padding=((0, 1), (0, 1))))
            model.add(BatchNormalization(momentum=0.8))
            model.add(LeakyReLU(alpha=0.2))
            model.add(Dropout(0.1))

            model.add(Conv2D(128, kernel_size=3, strides=2, padding='same'))
            model.add(BatchNormalization(momentum=0.8))
            model.add(LeakyReLU(alpha=0.2))
            model.add(Dropout(0.1))

            model.add(Conv2D(256, kernel_size=3, strides=1, padding='same'))
            model.add(BatchNormalization(momentum=0.8))
            model.add(LeakyReLU(alpha=0.2))
            model.add(Dropout(0.1))

        if quality == 10:
            model.add(Conv2D(64, kernel_size=3, strides=2, input_shape=self.img_shape, padding='same'))
            model.add(LeakyReLU(alpha=0.2))
            model.add(Dropout(0.1))

            model.add(Conv2D(128, kernel_size=3, strides=2, padding='same'))
            model.add(ZeroPadding2D(padding=((0, 1), (0, 1))))
            model.add(BatchNormalization(momentum=0.8))
            model.add(LeakyReLU(alpha=0.2))
            model.add(Dropout(0.1))

            model.add(Conv2D(196, kernel_size=3, strides=2, padding='same'))
            model.add(BatchNormalization(momentum=0.8))
            model.add(LeakyReLU(alpha=0.2))
            model.add(Dropout(0.1))

            model.add(Conv2D(384, kernel_size=3, strides=1, padding='same'))
            model.add(BatchNormalization(momentum=0.8))
            model.add(LeakyReLU(alpha=0.2))
            model.add(Dropout(0.1))

        model.add(Flatten())
        model.add(Dense(1, activation='sigmoid'))

        model.summary()

        img = Input(shape=self.img_shape)
        validity = model(img)

        return Model(img, validity)

    def train(self, epochs, batch_size=128, save_interval=50):

        # Load the dataset
        x_train = np.array(get_dataset())

        # Rescale -1 to 1
        x_train = x_train / 127.5 - 1.
        x_train = np.expand_dims(x_train, axis=3)

        # Adversarial ground truths
        valid = np.ones((batch_size, 1))
        fake = np.zeros((batch_size, 1))

        for epoch in range(epochs):

            # ---------------------
            #  Train Discriminator
            # ---------------------

            # Select a random half of images
            idx = np.random.randint(0, x_train.shape[0], batch_size)
            imgs = x_train[idx]

            # Sample noise and generate a batch of new images
            noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
            gen_imgs = self.generator.predict(noise)

            # Train the discriminator (real classified as ones and generated as zeros)
            d_loss_real = self.discriminator.train_on_batch(imgs, valid)
            d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)
            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

            # ---------------------
            #  Train Generator
            # ---------------------

            # Train the generator (wants discriminator to mistake images as real)
            g_loss = self.combined.train_on_batch(noise, valid)

            # If at save interval => save generated image samples
            if epoch % save_interval == 0:
                print('%d [D loss: %f, acc.: %.2f%%] [G loss: %f]' % (epoch, d_loss[0], 100 * d_loss[1], g_loss))

                # Plot the progress
                self.save_imgs(epoch)

    def save_imgs(self, epoch):
        r, c = 5, 5
        noise = np.random.normal(0, 1, (r * c, self.latent_dim))
        gen_imgs = self.generator.predict(noise)

        # Rescale images 0 - 1
        gen_imgs = 0.5 * gen_imgs + 0.5

        fig, axs = plt.subplots(r, c)
        cnt = 0
        for i in range(r):
            for j in range(c):
                axs[i, j].imshow(gen_imgs[cnt, :, :, 0], cmap='gray')
                axs[i, j].axis('off')
                cnt += 1
        fig.savefig('images/panorama_%d.png' % epoch)
        plt.close()


if __name__ == '__main__':
    dcgan = DCGAN()
    dcgan.train(epochs=100001, batch_size=32, save_interval=500)