gan_generate.py

'''
@author: Suyash Sonawane [github/suyashsonawane]

This is a python script for  generating new dance images from the trained model which will be stored in `new_images` directory, checkpoints are loaded from 
 `training_checkpoints` directory
'''

import datetime
import tensorflow as tf
import time
from matplotlib import pyplot as plt
import os
import numpy as np

# Loading target images
target_dataset = tf.data.Dataset.list_files(os.getcwd()+'/target/*.jpg')
d = np.array(list(target_dataset.as_numpy_iterator()))
d.sort()
target_dataset = tf.data.Dataset.from_tensor_slices(d)

BUFFER_SIZE = len(d)
BATCH_SIZE = 1


def resize(input_image, real_image, height, width):
    input_image = tf.image.resize(input_image, [height, width],
                                  method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
    real_image = tf.image.resize(real_image, [height, width],
                                 method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
    return input_image, real_image


def load_target(image_file):
    image = tf.io.read_file(image_file)
    image = tf.image.decode_jpeg(image)

    w = tf.shape(image)[1]

    real_image = image
    input_image = image

    input_image = tf.cast(input_image, tf.float32)/255.
    real_image = tf.cast(real_image, tf.float32)/255.

    return resize(input_image, real_image, 256, 256)


target_dataset = target_dataset.map(
    load_target, num_parallel_calls=tf.data.experimental.AUTOTUNE).batch(BATCH_SIZE)

# RGB Image
OUTPUT_CHANNELS = 3

# Function for creating downsample layers for the GAN


def downsample(filters, size, apply_batchnorm=True):
    initializer = tf.random_normal_initializer(0., 0.02)

    result = tf.keras.Sequential()
    result.add(
        tf.keras.layers.Conv2D(filters, size, strides=2, padding='same',
                               kernel_initializer=initializer, use_bias=False))

    if apply_batchnorm:
        result.add(tf.keras.layers.BatchNormalization())

    result.add(tf.keras.layers.LeakyReLU())

    return result

# Function for creating upsmaple layers for the GAN


def upsample(filters, size, apply_dropout=False):
    initializer = tf.random_normal_initializer(0., 0.02)

    result = tf.keras.Sequential()
    result.add(
        tf.keras.layers.Conv2DTranspose(filters, size, strides=2,
                                        padding='same',
                                        kernel_initializer=initializer,
                                        use_bias=False))

    result.add(tf.keras.layers.BatchNormalization())

    if apply_dropout:
        result.add(tf.keras.layers.Dropout(0.5))

    result.add(tf.keras.layers.ReLU())

    return result

# Defining the Generator


def Generator():
    inputs = tf.keras.layers.Input(shape=[256, 256, 3])

    down_stack = [
        downsample(64, 4, apply_batchnorm=False),  # (bs, 128, 128, 64)
        downsample(128, 4),  # (bs, 64, 64, 128)
        downsample(256, 4),  # (bs, 32, 32, 256)
        downsample(512, 4),  # (bs, 16, 16, 512)
        downsample(512, 4),  # (bs, 8, 8, 512)
        downsample(512, 4),  # (bs, 4, 4, 512)
        downsample(512, 4),  # (bs, 2, 2, 512)
        downsample(512, 4),  # (bs, 1, 1, 512)
    ]

    up_stack = [
        upsample(512, 4, apply_dropout=True),  # (bs, 2, 2, 1024)
        upsample(512, 4, apply_dropout=True),  # (bs, 4, 4, 1024)
        upsample(512, 4, apply_dropout=True),  # (bs, 8, 8, 1024)
        upsample(512, 4),  # (bs, 16, 16, 1024)
        upsample(256, 4),  # (bs, 32, 32, 512)
        upsample(128, 4),  # (bs, 64, 64, 256)
        upsample(64, 4),  # (bs, 128, 128, 128)
    ]

    initializer = tf.random_normal_initializer(0., 0.02)
    last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, 4,
                                           strides=2,
                                           padding='same',
                                           kernel_initializer=initializer,
                                           activation='tanh')  # (bs, 256, 256, 3)

    x = inputs

    # Downsampling through the model
    skips = []
    for down in down_stack:
        x = down(x)
        skips.append(x)

    skips = reversed(skips[:-1])

    # Upsampling and establishing the skip connections
    for up, skip in zip(up_stack, skips):
        x = up(x)
        x = tf.keras.layers.Concatenate()([x, skip])

    x = last(x)

    return tf.keras.Model(inputs=inputs, outputs=x)


# Initializing the Generator
generator = Generator()

LAMBDA = 100

# Defining the Generator LOSS


def generator_loss(disc_generated_output, gen_output, target):
    gan_loss = loss_object(tf.ones_like(
        disc_generated_output), disc_generated_output)

    # mean absolute error
    l1_loss = tf.reduce_mean(tf.abs(target - gen_output))

    total_gen_loss = gan_loss + (LAMBDA * l1_loss)

    return total_gen_loss, gan_loss, l1_loss

# Defining the Discriminator


def Discriminator():
    initializer = tf.random_normal_initializer(0., 0.02)

    inp = tf.keras.layers.Input(shape=[256, 256, 3], name='input_image')
    tar = tf.keras.layers.Input(shape=[256, 256, 3], name='target_image')

    x = tf.keras.layers.concatenate([inp, tar])  # (bs, 256, 256, channels*2)

    down1 = downsample(64, 4, False)(x)  # (bs, 128, 128, 64)
    down2 = downsample(128, 4)(down1)  # (bs, 64, 64, 128)
    down3 = downsample(256, 4)(down2)  # (bs, 32, 32, 256)

    zero_pad1 = tf.keras.layers.ZeroPadding2D()(down3)  # (bs, 34, 34, 256)
    conv = tf.keras.layers.Conv2D(512, 4, strides=1,
                                  kernel_initializer=initializer,
                                  use_bias=False)(zero_pad1)  # (bs, 31, 31, 512)

    batchnorm1 = tf.keras.layers.BatchNormalization()(conv)

    leaky_relu = tf.keras.layers.LeakyReLU()(batchnorm1)

    zero_pad2 = tf.keras.layers.ZeroPadding2D()(leaky_relu)  # (bs, 33, 33, 512)

    last = tf.keras.layers.Conv2D(1, 4, strides=1,
                                  kernel_initializer=initializer)(zero_pad2)  # (bs, 30, 30, 1)

    return tf.keras.Model(inputs=[inp, tar], outputs=last)


# Initializing the Discriminator
discriminator = Discriminator()

loss_object = tf.keras.losses.BinaryCrossentropy(from_logits=True)

# Defining the Discriminator LOSS


def discriminator_loss(disc_real_output, disc_generated_output):
    real_loss = loss_object(tf.ones_like(disc_real_output), disc_real_output)

    generated_loss = loss_object(tf.zeros_like(
        disc_generated_output), disc_generated_output)

    total_disc_loss = real_loss + generated_loss

    return total_disc_loss


# Optimizers for the models
generator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
discriminator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)

# Checkpoint generation
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)

checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))


#  Function for generating new images
def generate_new_images(model, test_input, tar, i):
    prediction = model(test_input, training=True)
    display_list = [test_input[0], prediction[0]]
    title = ['Input Image', 'Transformed Image']
    plt.figure(figsize=(15, 15))

    fig = plt.figure()
    ax1 = fig.add_subplot(1, 2, 1)
    ax1.imshow(display_list[0])
    ax1.set_title(title[0])
    ax1.axis("off")
    ax2 = fig.add_subplot(1, 2, 2)
    ax2.imshow(display_list[1])
    ax2.set_title(title[1])
    ax2.axis("off")

    fig.savefig(f'new_images/fig{i}.png', bbox_inches="tight")


i = 0
for inp, tar in target_dataset:
    generate_new_images(generator, inp, tar, i)
    i += 1
    print(i)