celeb-face-gan.py

import helper

data_dir = './data'

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)

show_n_images = 25

import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')



show_n_images = 25

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))


from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))


def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
   
    real_images = tf.placeholder(tf.float32,(None,image_width,image_height,image_channels),name='real_images')
    z_input = tf.placeholder(tf.float32,(None,z_dim), name="z_input")
    
    learning_rate = tf.placeholder(tf.float32, name="learning_rate")
    return real_images, z_input, learning_rate



def discriminator(images, reuse=False):
    """

    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    #
    #adapted from dcgans
    with tf.variable_scope('discriminator', reuse=reuse):
    # Input layer is 28x28x3
        alpha = 0.02
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        # 16x16x32
       

        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        # 8x8x128

        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        # 4x4x256

        # Flatten it
        #print(x2)
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        outputs = tf.sigmoid(logits)
    return outputs, logits


def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    reuse = False if is_train==True else True
    with tf.variable_scope('generator', reuse=reuse):
        # First fully connected layer
        alpha=0.2
        x1 = tf.layers.dense(z, 7*7*512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 4x4x512 now
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 8x8x256 now
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        # 16x16x128 now
        #print(x3)
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=1, padding='same')
        
        #print(logits)
        
        out = tf.tanh(logits)
    return out




def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    alpha=0.2
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)): 
    # Optimize
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt
    
    



import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    pyplot.close('all')
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show(block=True)



def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """

    _, image_width, image_height, image_channels = data_shape
    
    real_images , z_input, lr = model_inputs(image_width, image_height, image_channels, z_dim)
    
    d_loss, g_loss = model_loss(input_real=real_images,input_z=z_input,out_channel_dim=image_channels)
    xd_opt , xg_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
   
    saver = tf.train.Saver()
    sample_z = np.random.uniform(-1, 1, size=(50, z_dim))

    samples, losses = [], []
    steps = 0
    
    with tf.Session() as sess:
        #summary_writer = tf.summary.FileWriter('./logs/', sess.graph)
        sess.run(tf.global_variables_initializer())
        saver.restore(sess, './generator.ckpt')
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps +=1
                #print(batch_images)
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                _ = sess.run(xd_opt ,feed_dict={real_images: batch_images,z_input:batch_z})
                _ = sess.run(xg_opt,feed_dict={real_images: batch_images,z_input:batch_z})
                
                if steps % 100 ==0:
                    show_generator_output(sess, 5, z_input, image_channels, data_image_mode)
                    train_loss_d = sess.run(d_loss,{z_input:batch_z,real_images:batch_images})
                    train_loss_g = g_loss.eval({z_input:batch_z})
                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    saver.save(sess, './generator.ckpt')
                
            
            train_loss_d = sess.run(d_loss,{z_input:batch_z,real_images:batch_images})
            train_loss_g = g_loss.eval({z_input:batch_z})
            print("Epoch {}/{}...".format(epoch_count, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    # Save losses to view after training
            losses.append((train_loss_d, train_loss_g))
            sample_z = np.random.uniform(-1, 1, size=(16, z_dim))
            gen_samples = sess.run(
                           generator(z_input, image_channels, is_train=False),
                           feed_dict={z_input: sample_z})
            samples.append(gen_samples)
        saver.save(sess, './generator.ckpt')
                                                                
                        
'''

batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.4


epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)


'''

batch_size = 64
z_dim = 100
learning_rate = 0.0001
beta1 = 0.5

epochs = 10

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)