from __future__ import print_function
import random
import os
import glob
import scipy
import tensorflow as tf
import numpy as np
from PIL import Image
import skimage.io as io
import matplotlib.pyplot as plt
class Arguments(object):
data_path = 'results_celebA/preprocessed/'
save_path = 'results_celebA' #path to save preprocessed image folder
preproc_foldername = 'preprocessed' #folder name for preprocessed images
image_size = 64 #images are resized to image_size value
num_images = 202590 #the number of training images
batch_size = 64 #batch size
dim_z = 100 #the dimension of z variable (the generator input dimension)
n_g_filters = 64 #the number of the generator filters (gets multiplied between layers)
n_f_filters = 64 #the number of the discriminator filters (gets multiplied between layers)
n_epoch = 25 #the number of epochs
lr = 0.0002 #learning rate
beta1 = 0.5 #beta_1 parameter of Adam optimizer
beta2 = 0.99 #beta_2 parameter of Adam optimizer
args = Arguments()
#contains functions that load, preprocess and visualize images.
class Dataset(object):
def __init__(self, data_path, num_imgs, target_imgsize):
self.data_path = data_path
self.num_imgs = num_imgs
self.target_imgsize = target_imgsize
def normalize_np_image(self, image):
return (image / 255.0 - 0.5) / 0.5
def denormalize_np_image(self, image):
return (image * 0.5 + 0.5) * 255
def get_input(self, image_path):
image = np.array(Image.open(image_path)).astype(np.float32)
return self.normalize_np_image(image)
def get_imagelist(self, data_path, celebA=False):
if celebA == True:
imgs_path = os.path.join(data_path, 'img_align_celeba/*.jpg')
else:
imgs_path = os.path.join(data_path, '*.jpg')
all_namelist = glob.glob(imgs_path, recursive=True)
return all_namelist[:self.num_imgs]
def load_and_preprocess_image(self, image_path):
image = Image.open(image_path)
j = (image.size[0] - 100) // 2
i = (image.size[1] - 100) // 2
image = image.crop([j, i, j + 100, i + 100])
image = image.resize([self.target_imgsize, self.target_imgsize], Image.BILINEAR)
image = np.array(image.convert('RGB')).astype(np.float32)
image = self.normalize_np_image(image)
return image
#reads data, preprocesses and saves to another folder with the given path.
def preprocess_and_save_images(self, dir_name, save_path=''):
preproc_folder_path = os.path.join(save_path, dir_name)
if not os.path.exists(preproc_folder_path):
os.makedirs(preproc_folder_path)
imgs_path = os.path.join(self.data_path, 'img_align_celeba/*.jpg')
print('Saving and preprocessing images ...')
for num, imgname in enumerate(glob.iglob(imgs_path, recursive=True)):
cur_image = self.load_and_preprocess_image(imgname)
cur_image = Image.fromarray(np.uint8(self.denormalize_np_image(cur_image)))
cur_image.save(preproc_folder_path + '/preprocessed_image_%d.jpg' %(num))
self.data_path= preproc_folder_path
def get_nextbatch(self, batch_size):
print("nextbatch batchsize is: ", batch_size)
assert (batch_size > 0),"Give a valid batch size"
cur_idx = 0
image_namelist = self.get_imagelist(self.data_path)
while cur_idx + batch_size <= self.num_imgs:
cur_namelist = image_namelist[cur_idx:cur_idx + batch_size]
cur_batch = [self.get_input(image_path) for image_path in cur_namelist]
cur_batch = np.array(cur_batch).astype(np.float32)
cur_idx += batch_size
yield cur_batch
def show_image(self, image, normalized=True):
if not type(image).__module__ == np.__name__:
image = image.numpy()
if normalized:
npimg = (image * 0.5) + 0.5
npimg.astype(np.uint8)
plt.imshow(npimg, interpolation='nearest')
#contains functions that load, preprocess and visualize images.
class Dataset(object):
def __init__(self, data_path, num_imgs, target_imgsize):
self.data_path = data_path
self.num_imgs = num_imgs
self.target_imgsize = target_imgsize
def normalize_np_image(self, image):
return (image / 255.0 - 0.5) / 0.5
def denormalize_np_image(self, image):
return (image * 0.5 + 0.5) * 255
def get_input(self, image_path):
image = np.array(Image.open(image_path)).astype(np.float32)
return self.normalize_np_image(image)
def get_imagelist(self, data_path, celebA=False):
if celebA == True:
imgs_path = os.path.join(data_path, 'img_align_celeba/*.jpg')
else:
imgs_path = os.path.join(data_path, '*.jpg')
all_namelist = glob.glob(imgs_path, recursive=True)
return all_namelist[:self.num_imgs]
def load_and_preprocess_image(self, image_path):
image = Image.open(image_path)
j = (image.size[0] - 100) // 2
i = (image.size[1] - 100) // 2
image = image.crop([j, i, j + 100, i + 100])
image = image.resize([self.target_imgsize, self.target_imgsize], Image.BILINEAR)
image = np.array(image.convert('RGB')).astype(np.float32)
image = self.normalize_np_image(image)
return image
#reads data, preprocesses and saves to another folder with the given path.
def preprocess_and_save_images(self, dir_name, save_path=''):
preproc_folder_path = os.path.join(save_path, dir_name)
if not os.path.exists(preproc_folder_path):
os.makedirs(preproc_folder_path)
imgs_path = os.path.join(self.data_path, 'img_align_celeba/*.jpg')
print('Saving and preprocessing images ...')
for num, imgname in enumerate(glob.iglob(imgs_path, recursive=True)):
cur_image = self.load_and_preprocess_image(imgname)
cur_image = Image.fromarray(np.uint8(self.denormalize_np_image(cur_image)))
cur_image.save(preproc_folder_path + '/preprocessed_image_%d.jpg' %(num))
self.data_path= preproc_folder_path
def get_nextbatch(self, batch_size):
assert (batch_size > 0),"Give a valid batch size"
cur_idx = 0
image_namelist = self.get_imagelist(self.data_path)
while cur_idx + batch_size <= self.num_imgs:
cur_namelist = image_namelist[cur_idx:cur_idx + batch_size]
cur_batch = [self.get_input(image_path) for image_path in cur_namelist]
cur_batch = np.array(cur_batch).astype(np.float32)
cur_idx += batch_size
yield cur_batch
def show_image(self, image, normalized=True):
if not type(image).__module__ == np.__name__:
image = image.numpy()
if normalized:
npimg = (image * 0.5) + 0.5
npimg.astype(np.uint8)
plt.imshow(npimg, interpolation='nearest')
def generator(x, args, reuse=False):
with tf.device('/gpu:0'):
with tf.variable_scope("generator", reuse=reuse):
#Layer Block 1
with tf.variable_scope("layer1"):
deconv1 = tf.layers.conv2d_transpose(inputs=x,
filters= args.n_g_filters*8,
kernel_size=4,
strides=1,
padding='valid',
use_bias=False,
name='deconv')
batch_norm1=tf.layers.batch_normalization(deconv1,
name = 'batch_norm')
relu1 = tf.nn.relu(batch_norm1, name='relu')
#Layer Block 2
with tf.variable_scope("layer2"):
deconv2 = tf.layers.conv2d_transpose(inputs=relu1,
filters=args.n_g_filters*4,
kernel_size=4,
strides=2,
padding='same',
use_bias=False,
name='deconv')
batch_norm2 = tf.layers.batch_normalization(deconv2,
name = 'batch_norm')
relu2 = tf.nn.relu(batch_norm2, name='relu')
#Layer Block 3
with tf.variable_scope("layer3"):
deconv3 = tf.layers.conv2d_transpose(inputs=relu2,
filters=args.n_g_filters*2,
kernel_size=4,
strides=2,
padding='same',
use_bias = False,
name='deconv')
batch_norm3 = tf.layers.batch_normalization(deconv3,
name = 'batch_norm')
relu3 = tf.nn.relu(batch_norm3, name='relu')
#Layer Block 4
with tf.variable_scope("layer4"):
deconv4 = tf.layers.conv2d_transpose(inputs=relu3,
filters=args.n_g_filters,
kernel_size=4,
strides=2,
padding='same',
use_bias=False,
name='deconv')
batch_norm4 = tf.layers.batch_normalization(deconv4,
name = 'batch_norm')
relu4 = tf.nn.relu(batch_norm4, name='relu')
#Output Layer
with tf.variable_scope("last_layer"):
logit = tf.layers.conv2d_transpose(inputs=relu4,
filters=3,
kernel_size=4,
strides=2,
padding='same',
use_bias=False,
name='logit')
output = tf.nn.tanh(logit)
return output, logit
def discriminator(x, args, reuse=False):
with tf.device('/gpu:0'):
with tf.variable_scope("discriminator", reuse=reuse):
with tf.variable_scope("layer1"):
conv1 = tf.layers.conv2d(inputs=x,
filters=args.n_f_filters,
kernel_size=4,
strides=2,
padding='same',
use_bias=False,
name='conv')
relu1 = tf.nn.leaky_relu(conv1, alpha=0.2, name='relu')
with tf.variable_scope("layer2"):
conv2 = tf.layers.conv2d(inputs=relu1,
filters=args.n_f_filters*2,
kernel_size=4,
strides=2,
padding='same',
use_bias=False,
name='conv')
batch_norm2 = tf.layers.batch_normalization(conv2,name='batch_norm')
relu2 = tf.nn.leaky_relu(batch_norm2, alpha=0.2, name='relu')
with tf.variable_scope("layer3"):
conv3 = tf.layers.conv2d(inputs=relu2,
filters=args.n_f_filters*4,
kernel_size=4,
strides=2,
padding='same',
use_bias=False,
name='conv')
batch_norm3 = tf.layers.batch_normalization(conv3, name='batch_norm')
relu3 = tf.nn.leaky_relu(batch_norm3, name='relu')
with tf.variable_scope("layer4"):
conv4 = tf.layers.conv2d(inputs=relu3,
filters=args.n_f_filters*8,
kernel_size=4,
strides=2,
padding='same',
use_bias=False,
name='conv')
batch_norm4 = tf.layers.batch_normalization(conv4, name='batch_norm')
relu4 = tf.nn.leaky_relu(batch_norm4, alpha=0.2, name='relu')
with tf.variable_scope("last_layer"):
logit = tf.layers.conv2d(inputs=relu4,
filters=1,
kernel_size=4,
strides=1,
padding='valid',
use_bias=False,
name='conv')
output = tf.nn.sigmoid(logit)
return output, logit
def sample_z(dim_z, num_batch):
mu = 0
sigma = 1
s = np.random.normal(mu, sigma, num_batch*dim_z)
samples = s.reshape(num_batch, 1, 1, dim_z)
##dist = tf.distributions.Normal(0.0, 1.0)
##samples = dist.sample([num_batch, 1, 1, dim_z])
return samples
#64,1,1,100 6400
sample_z(100, 64)
def get_losses(d_real_logits, d_fake_logits):
#add new loss function here
###d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_real_logits, labels=tf.ones_like(d_real_logits)))
###d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_logits, labels=tf.zeros_like(d_fake_logits)))
###d_loss = d_loss_real + d_loss_fake
###g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_logits, labels=tf.ones_like(d_fake_logits)))
###return d_loss, g_loss
d_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_real_logits,labels=tf.ones_like(d_real_logits)) + tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_logits,labels=tf.zeros_like(d_fake_logits)))
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_logits,labels=tf.ones_like(d_fake_logits)))
return d_loss, g_loss
def get_optimizers(learning_rate, beta1, beta2):
d_optimizer = tf.train.AdamOptimizer(learning_rate, beta1, beta2)
g_optimizer = tf.train.AdamOptimizer(learning_rate, beta1, beta2)
return d_optimizer, g_optimizer
def optimize(d_optimizer, g_optimizer, d_loss, g_loss):
d_step = d_optimizer.minimize(d_loss)
g_step = g_optimizer.minimize(g_loss)
return d_step, g_step
LOGDIR = "logs_basic_dcgan"
def merge_images(image_batch, size):
h,w = image_batch.shape[1], image_batch.shape[2]
c = image_batch.shape[3]
img = np.zeros((int(h*size[0]), w*size[1], c))
for idx, im in enumerate(image_batch):
i = idx % size[1]
j = idx // size[1]
img[j*h:j*h+h, i*w:i*w+w,:] = im
return img
itr_fh = open('basic_gan_itr.txt', 'a+')
def train(args):
tf.reset_default_graph()
data_loader = Dataset(args.data_path, args.num_images, args.image_size)
#data_loader.preprocess_and_save_images('preprocessed', 'results_celebA') #preprocess the images once
X = tf.placeholder(tf.float32, shape=[args.batch_size, args.image_size , args.image_size, 3])
Z = tf.placeholder(tf.float32, shape=[args.batch_size, 1, 1, args.dim_z])
G_sample, _ = generator(Z, args)
D_real, D_real_logits = discriminator(X, args)
D_fake, D_fake_logits = discriminator(G_sample, args, reuse=True)
d_loss, g_loss = get_losses(D_real_logits, D_fake_logits)
d_optimizer, g_optimizer = get_optimizers(args.lr, args.beta1, args.beta2)
d_step, g_step = optimize(d_optimizer, g_optimizer, d_loss, g_loss)
###z_sum = tf.summary.histogram('z', Z)
###d_sum = tf.summary.histogram('d', D_real)
###G_sum = tf.summary.histogram('g', G_sample)
###d_loss_sum = tf.summary.scalar('d_loss', d_loss)
###g_loss_sum = tf.summary.scalar('g_loss', g_loss)
###d_sum = tf.summary.merge([z_sum, d_sum, d_loss_sum])
###g_sum = tf.summary.merge([z_sum, G_sum, g_loss_sum])
###saver = tf.train.Saver()
###merged_summary = tf.summary.merge_all()
###d_loss_summary = tf.summary.scalar("Discriminator_Total_Loss", d_loss)
###g_loss_summary = tf.summary.scalar("Generator_Total_Loss", g_loss)
###merged_summary = tf.summary.merge_all()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(args.n_epoch):
for itr, real_batch in enumerate(data_loader.get_nextbatch(args.batch_size)):
print('itr is %d, and epoch is %d' %(itr, epoch))
itr_fh.write("epoch: " + str(epoch) + " itr: " + str(itr) + "\n")
Z_sample = sample_z(args.dim_z, args.batch_size)
_, _ = sess.run([d_step, g_step], feed_dict={X:real_batch , Z:Z_sample})
sample = sess.run(G_sample, feed_dict={Z:Z_sample})
print("sample size is: ", sample.shape)
if itr==3164: #num_images/batch_size
im_merged = merge_images(sample[:16], [4,4])
plt.imsave('sample_gan_images/im_merged_epoch_%d.png' %(epoch), im_merged )
scipy.misc.imsave('sample_gan_images/im_epoch_%d_itr_%d.png' %(epoch,itr), sample[1])
##merged_summary = sess.run(merged_summary, feed_dict={X:real_batch , Z:Z_sample})
###writer = tf.summary.FileWriter(LOGDIR)
###writer.add_summary(merged_summary, itr)
###d_loss_summary = tf.summary.scalar("Discriminator_Total_Loss", d_loss)
###g_loss_summary = tf.summary.scalar("Generator_Total_Loss", g_loss)
###merged_summary = tf.summary.merge_all()
###writer.add_graph(sess.graph)
###saver.save(sess, save_path='logs_basic_dcgan/gan.ckpt')
train(args)
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