class BigGAN_256(object):
##################################################################################
# Generator
##################################################################################
def generator(self, z, is_training=True, reuse=False):
with tf.variable_scope("generator", reuse=reuse):
# 7
if self.z_dim == 128:
split_dim = 18
split_dim_remainder = self.z_dim - (split_dim * 6)
z_split = tf.split(z,
num_or_size_splits=[split_dim] * 6 +
[split_dim_remainder],
axis=-1)
else:
split_dim = self.z_dim // 7
split_dim_remainder = self.z_dim - (split_dim * 7)
if split_dim_remainder == 0:
z_split = tf.split(z,
num_or_size_splits=[split_dim] * 7,
axis=-1)
else:
z_split = tf.split(z,
num_or_size_splits=[split_dim] * 6 +
[split_dim_remainder],
axis=-1)
ch = 16 * self.ch
x = fully_conneted(z_split[0],
units=4 * 4 * ch,
sn=self.sn,
scope='dense')
x = tf.reshape(x, shape=[-1, 4, 4, ch])
x = resblock_up_condition(x,
z_split[1],
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_up_16')
ch = ch // 2
x = resblock_up_condition(x,
z_split[2],
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_up_8_0')
x = resblock_up_condition(x,
z_split[3],
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_up_8_1')
ch = ch // 2
x = resblock_up_condition(x,
z_split[4],
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_up_4')
ch = ch // 2
x = resblock_up_condition(x,
z_split[5],
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_up_2')
# Non-Local Block
x = self_attention_2(x,
channels=ch,
sn=self.sn,
scope='self_attention')
ch = ch // 2
x = resblock_up_condition(x,
z_split[6],
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_up_1')
x = batch_norm(x, is_training)
x = relu(x)
x = conv(x,
channels=self.c_dim,
kernel=3,
stride=1,
pad=1,
use_bias=False,
sn=self.sn,
scope='G_logit')
x = tanh(x)
return x
##################################################################################
# Discriminator
##################################################################################
def discriminator(self, x, is_training=True, reuse=False):
with tf.variable_scope("discriminator", reuse=reuse):
ch = self.ch
x = resblock_down(x,
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_down_1')
ch = ch * 2
x = resblock_down(x,
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_down_2')
# Non-Local Block
x = self_attention_2(x,
channels=ch,
sn=self.sn,
scope='self_attention')
ch = ch * 2
x = resblock_down(x,
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_down_4')
ch = ch * 2
x = resblock_down(x,
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_down_8_0')
x = resblock_down(x,
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_down_8_1')
ch = ch * 2
x = resblock_down(x,
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_down_16')
x = resblock(x,
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock')
x = relu(x)
x = global_sum_pooling(x)
x = fully_conneted(x, units=1, sn=self.sn, scope='D_logit')
return x
def gradient_penalty(self, real, fake):
if self.gan_type.__contains__('dragan'):
eps = tf.random_uniform(shape=tf.shape(real), minval=0., maxval=1.)
_, x_var = tf.nn.moments(real, axes=[0, 1, 2, 3])
x_std = tf.sqrt(
x_var) # magnitude of noise decides the size of local region
fake = real + 0.5 * x_std * eps
alpha = tf.random_uniform(shape=[self.batch_size, 1, 1, 1],
minval=0.,
maxval=1.)
interpolated = real + alpha * (fake - real)
logit = self.discriminator(interpolated, reuse=True)
grad = tf.gradients(logit,
interpolated)[0] # gradient of D(interpolated)
grad_norm = tf.norm(flatten(grad), axis=1) # l2 norm
GP = 0
# WGAN - LP
if self.gan_type == 'wgan-lp':
GP = self.ld * tf.reduce_mean(
tf.square(tf.maximum(0.0, grad_norm - 1.)))
elif self.gan_type == 'wgan-gp' or self.gan_type == 'dragan':
GP = self.ld * tf.reduce_mean(tf.square(grad_norm - 1.))
return GP
##################################################################################
# Model
##################################################################################
def build_model(self):
""" Graph Input """
# images
Image_Data_Class = ImageData(self.img_size, self.c_dim,
self.custom_dataset)
inputs = tf.data.Dataset.from_tensor_slices(self.data)
gpu_device = '/gpu:0'
inputs = inputs.\
apply(shuffle_and_repeat(self.dataset_num)).\
apply(map_and_batch(Image_Data_Class.image_processing, self.batch_size, num_parallel_batches=16, drop_remainder=True)).\
apply(prefetch_to_device(gpu_device, self.batch_size))
inputs_iterator = inputs.make_one_shot_iterator()
self.inputs = inputs_iterator.get_next()
# noises
self.z = tf.truncated_normal(shape=[self.batch_size, 1, 1, self.z_dim],
name='random_z')
""" Loss Function """
# output of D for real images
real_logits = self.discriminator(self.inputs)
# output of D for fake images
fake_images = self.generator(self.z)
fake_logits = self.discriminator(fake_images, reuse=True)
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan':
GP = self.gradient_penalty(real=self.inputs, fake=fake_images)
else:
GP = 0
# get loss for discriminator
self.d_loss = discriminator_loss(
self.gan_type, real=real_logits, fake=fake_logits) + GP
# get loss for generator
self.g_loss = generator_loss(self.gan_type, fake=fake_logits)
""" Training """
# divide trainable variables into a group for D and a group for G
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if 'discriminator' in var.name]
g_vars = [var for var in t_vars if 'generator' in var.name]
# optimizers
with tf.control_dependencies(tf.get_collection(
tf.GraphKeys.UPDATE_OPS)):
self.d_optim = tf.train.AdamOptimizer(self.d_learning_rate,
beta1=self.beta1,
beta2=self.beta2).minimize(
self.d_loss,
var_list=d_vars)
self.opt = MovingAverageOptimizer(tf.train.AdamOptimizer(
self.g_learning_rate, beta1=self.beta1, beta2=self.beta2),
average_decay=self.moving_decay)
self.g_optim = self.opt.minimize(self.g_loss, var_list=g_vars)
"""" Testing """
# for test
self.fake_images = self.generator(self.z,
is_training=False,
reuse=True)
""" Summary """
self.d_sum = tf.summary.scalar("d_loss", self.d_loss)
self.g_sum = tf.summary.scalar("g_loss", self.g_loss)
class BigGAN_256(object):
def __init__(self, sess, args):
self.model_name = "BigGAN" # name for checkpoint
self.sess = sess
self.dataset_name = args.dataset
self.checkpoint_dir = args.checkpoint_dir
self.sample_dir = args.sample_dir
self.result_dir = args.result_dir
self.log_dir = args.log_dir
self.epoch = args.epoch
self.iteration = args.iteration
self.batch_size = args.batch_size
self.print_freq = args.print_freq
self.save_freq = args.save_freq
self.img_size = args.img_size
""" Generator """
self.ch = args.ch
self.z_dim = args.z_dim # dimension of noise-vector
self.gan_type = args.gan_type
""" Discriminator """
self.n_critic = args.n_critic
self.sn = args.sn
self.ld = args.ld
self.sample_num = args.sample_num # number of generated images to be saved
self.test_num = args.test_num
# train
self.g_learning_rate = args.g_lr
self.d_learning_rate = args.d_lr
self.beta1 = args.beta1
self.beta2 = args.beta2
self.moving_decay = args.moving_decay
self.custom_dataset = False
if self.dataset_name == 'mnist':
self.c_dim = 1
self.data = load_mnist()
elif self.dataset_name == 'cifar10':
self.c_dim = 3
self.data = load_cifar10()
else:
self.c_dim = 3
self.data = load_data(dataset_name=self.dataset_name)
self.custom_dataset = True
self.dataset_num = len(self.data)
self.sample_dir = os.path.join(self.sample_dir, self.model_dir)
check_folder(self.sample_dir)
print()
print("##### Information #####")
print("# BigGAN 256")
print("# gan type : ", self.gan_type)
print("# dataset : ", self.dataset_name)
print("# dataset number : ", self.dataset_num)
print("# batch_size : ", self.batch_size)
print("# epoch : ", self.epoch)
print("# iteration per epoch : ", self.iteration)
print()
print("##### Generator #####")
print("# spectral normalization : ", self.sn)
print("# learning rate : ", self.g_learning_rate)
print()
print("##### Discriminator #####")
print("# the number of critic : ", self.n_critic)
print("# spectral normalization : ", self.sn)
print("# learning rate : ", self.d_learning_rate)
##################################################################################
# Generator
##################################################################################
def generator(self, z, is_training=True, reuse=False):
with tf.variable_scope("generator", reuse=reuse):
# 7
if self.z_dim == 128:
split_dim = 18
split_dim_remainder = self.z_dim - (split_dim * 6)
z_split = tf.split(z,
num_or_size_splits=[split_dim] * 6 +
[split_dim_remainder],
axis=-1)
else:
split_dim = self.z_dim // 7
split_dim_remainder = self.z_dim - (split_dim * 7)
if split_dim_remainder == 0:
z_split = tf.split(z,
num_or_size_splits=[split_dim] * 7,
axis=-1)
else:
z_split = tf.split(z,
num_or_size_splits=[split_dim] * 6 +
[split_dim_remainder],
axis=-1)
ch = 16 * self.ch
x = fully_conneted(z_split[0],
units=4 * 4 * ch,
sn=self.sn,
scope='dense')
x = tf.reshape(x, shape=[-1, 4, 4, ch])
x = resblock_up_condition(x,
z_split[1],
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_up_16')
ch = ch // 2
x = resblock_up_condition(x,
z_split[2],
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_up_8_0')
x = resblock_up_condition(x,
z_split[3],
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_up_8_1')
ch = ch // 2
x = resblock_up_condition(x,
z_split[4],
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_up_4')
ch = ch // 2
x = resblock_up_condition(x,
z_split[5],
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_up_2')
# Non-Local Block
x = self_attention_2(x,
channels=ch,
sn=self.sn,
scope='self_attention')
ch = ch // 2
x = resblock_up_condition(x,
z_split[6],
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_up_1')
x = batch_norm(x, is_training)
x = relu(x)
x = conv(x,
channels=self.c_dim,
kernel=3,
stride=1,
pad=1,
use_bias=False,
sn=self.sn,
scope='G_logit')
x = tanh(x)
return x
##################################################################################
# Discriminator
##################################################################################
def discriminator(self, x, is_training=True, reuse=False):
with tf.variable_scope("discriminator", reuse=reuse):
ch = self.ch
x = resblock_down(x,
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_down_1')
ch = ch * 2
x = resblock_down(x,
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_down_2')
# Non-Local Block
x = self_attention_2(x,
channels=ch,
sn=self.sn,
scope='self_attention')
ch = ch * 2
x = resblock_down(x,
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_down_4')
ch = ch * 2
x = resblock_down(x,
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_down_8_0')
x = resblock_down(x,
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_down_8_1')
ch = ch * 2
x = resblock_down(x,
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock_down_16')
x = resblock(x,
channels=ch,
use_bias=False,
is_training=is_training,
sn=self.sn,
scope='resblock')
x = relu(x)
x = global_sum_pooling(x)
x = fully_conneted(x, units=1, sn=self.sn, scope='D_logit')
return x
def gradient_penalty(self, real, fake):
if self.gan_type.__contains__('dragan'):
eps = tf.random_uniform(shape=tf.shape(real), minval=0., maxval=1.)
_, x_var = tf.nn.moments(real, axes=[0, 1, 2, 3])
x_std = tf.sqrt(
x_var) # magnitude of noise decides the size of local region
fake = real + 0.5 * x_std * eps
alpha = tf.random_uniform(shape=[self.batch_size, 1, 1, 1],
minval=0.,
maxval=1.)
interpolated = real + alpha * (fake - real)
logit = self.discriminator(interpolated, reuse=True)
grad = tf.gradients(logit,
interpolated)[0] # gradient of D(interpolated)
grad_norm = tf.norm(flatten(grad), axis=1) # l2 norm
GP = 0
# WGAN - LP
if self.gan_type == 'wgan-lp':
GP = self.ld * tf.reduce_mean(
tf.square(tf.maximum(0.0, grad_norm - 1.)))
elif self.gan_type == 'wgan-gp' or self.gan_type == 'dragan':
GP = self.ld * tf.reduce_mean(tf.square(grad_norm - 1.))
return GP
##################################################################################
# Model
##################################################################################
def build_model(self):
""" Graph Input """
# images
Image_Data_Class = ImageData(self.img_size, self.c_dim,
self.custom_dataset)
inputs = tf.data.Dataset.from_tensor_slices(self.data)
gpu_device = '/gpu:0'
inputs = inputs.\
apply(shuffle_and_repeat(self.dataset_num)).\
apply(map_and_batch(Image_Data_Class.image_processing, self.batch_size, num_parallel_batches=16, drop_remainder=True)).\
apply(prefetch_to_device(gpu_device, self.batch_size))
inputs_iterator = inputs.make_one_shot_iterator()
self.inputs = inputs_iterator.get_next()
# noises
self.z = tf.truncated_normal(shape=[self.batch_size, 1, 1, self.z_dim],
name='random_z')
""" Loss Function """
# output of D for real images
real_logits = self.discriminator(self.inputs)
# output of D for fake images
fake_images = self.generator(self.z)
fake_logits = self.discriminator(fake_images, reuse=True)
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan':
GP = self.gradient_penalty(real=self.inputs, fake=fake_images)
else:
GP = 0
# get loss for discriminator
self.d_loss = discriminator_loss(
self.gan_type, real=real_logits, fake=fake_logits) + GP
# get loss for generator
self.g_loss = generator_loss(self.gan_type, fake=fake_logits)
""" Training """
# divide trainable variables into a group for D and a group for G
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if 'discriminator' in var.name]
g_vars = [var for var in t_vars if 'generator' in var.name]
# optimizers
with tf.control_dependencies(tf.get_collection(
tf.GraphKeys.UPDATE_OPS)):
self.d_optim = tf.train.AdamOptimizer(self.d_learning_rate,
beta1=self.beta1,
beta2=self.beta2).minimize(
self.d_loss,
var_list=d_vars)
self.opt = MovingAverageOptimizer(tf.train.AdamOptimizer(
self.g_learning_rate, beta1=self.beta1, beta2=self.beta2),
average_decay=self.moving_decay)
self.g_optim = self.opt.minimize(self.g_loss, var_list=g_vars)
"""" Testing """
# for test
self.fake_images = self.generator(self.z,
is_training=False,
reuse=True)
""" Summary """
self.d_sum = tf.summary.scalar("d_loss", self.d_loss)
self.g_sum = tf.summary.scalar("g_loss", self.g_loss)
##################################################################################
# Train
##################################################################################
def train(self):
# initialize all variables
tf.global_variables_initializer().run()
# saver to save model
self.saver = self.opt.swapping_saver()
# summary writer
self.writer = tf.summary.FileWriter(
self.log_dir + '/' + self.model_dir, self.sess.graph)
# restore check-point if it exits
could_load, checkpoint_counter = self.load(self.checkpoint_dir)
if could_load:
start_epoch = (int)(checkpoint_counter / self.iteration)
start_batch_id = checkpoint_counter - start_epoch * self.iteration
counter = checkpoint_counter
print(" [*] Load SUCCESS")
else:
start_epoch = 0
start_batch_id = 0
counter = 1
print(" [!] Load failed...")
# loop for epoch
start_time = time.time()
past_g_loss = -1.
for epoch in range(start_epoch, self.epoch):
# get batch data
for idx in range(start_batch_id, self.iteration):
# update D network
_, summary_str, d_loss = self.sess.run(
[self.d_optim, self.d_sum, self.d_loss])
self.writer.add_summary(summary_str, counter)
# update G network
g_loss = None
if (counter - 1) % self.n_critic == 0:
_, summary_str, g_loss = self.sess.run(
[self.g_optim, self.g_sum, self.g_loss])
self.writer.add_summary(summary_str, counter)
past_g_loss = g_loss
# display training status
counter += 1
if g_loss == None:
g_loss = past_g_loss
print("Epoch: [%2d] [%5d/%5d] time: %4.4f, d_loss: %.8f, g_loss: %.8f" \
% (epoch, idx, self.iteration, time.time() - start_time, d_loss, g_loss))
# save training results for every 300 steps
if np.mod(idx + 1, self.print_freq) == 0:
samples = self.sess.run(self.fake_images)
tot_num_samples = min(self.sample_num, self.batch_size)
manifold_h = int(np.floor(np.sqrt(tot_num_samples)))
manifold_w = int(np.floor(np.sqrt(tot_num_samples)))
save_images(
samples[:manifold_h * manifold_w, :, :, :],
[manifold_h, manifold_w],
'./' + self.sample_dir + '/' + self.model_name +
'_train_{:02d}_{:05d}.png'.format(epoch, idx + 1))
if np.mod(idx + 1, self.save_freq) == 0:
self.save(self.checkpoint_dir, counter)
# After an epoch, start_batch_id is set to zero
# non-zero value is only for the first epoch after loading pre-trained model
start_batch_id = 0
# save model
self.save(self.checkpoint_dir, counter)
# show temporal results
# self.visualize_results(epoch)
# save model for final step
self.save(self.checkpoint_dir, counter)
@property
def model_dir(self):
if self.sn:
sn = '_sn'
else:
sn = ''
return "{}_{}_{}_{}_{}{}".format(self.model_name, self.dataset_name,
self.gan_type, self.img_size,
self.z_dim, sn)
def save(self, checkpoint_dir, step):
checkpoint_dir = os.path.join(checkpoint_dir, self.model_dir)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
self.saver.save(self.sess,
os.path.join(checkpoint_dir,
self.model_name + '.model'),
global_step=step)
def load(self, checkpoint_dir):
print(" [*] Reading checkpoints...")
checkpoint_dir = os.path.join(checkpoint_dir, self.model_dir)
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
self.saver.restore(self.sess,
os.path.join(checkpoint_dir, ckpt_name))
counter = int(ckpt_name.split('-')[-1])
print(" [*] Success to read {}".format(ckpt_name))
return True, counter
else:
print(" [*] Failed to find a checkpoint")
return False, 0
def visualize_results(self, epoch):
tot_num_samples = min(self.sample_num, self.batch_size)
image_frame_dim = int(np.floor(np.sqrt(tot_num_samples)))
""" random condition, random noise """
samples = self.sess.run(self.fake_images)
save_images(
samples[:image_frame_dim * image_frame_dim, :, :, :],
[image_frame_dim, image_frame_dim], self.sample_dir + '/' +
self.model_name + '_epoch%02d' % epoch + '_visualize.png')
def test(self):
tf.global_variables_initializer().run()
self.saver = tf.train.Saver()
could_load, checkpoint_counter = self.load(self.checkpoint_dir)
result_dir = os.path.join(self.result_dir, self.model_dir)
check_folder(result_dir)
if could_load:
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
tot_num_samples = min(self.sample_num, self.batch_size)
image_frame_dim = int(np.floor(np.sqrt(tot_num_samples)))
""" random condition, random noise """
for i in range(self.test_num):
samples = self.sess.run(self.fake_images)
save_images(
samples[:image_frame_dim * image_frame_dim, :, :, :],
[image_frame_dim, image_frame_dim],
result_dir + '/' + self.model_name + '_test_{}.png'.format(i))
def tpu_model_fn(self, features, labels, mode, params):
params = EasyDict(**params)
d_loss, d_vars, g_loss, g_vars, fake_images, fake_logits, z = self.base_model_fn(
features, labels, mode, params)
# --------------------------------------------------------------------------
# Predict
# --------------------------------------------------------------------------
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
"z": z,
"fake_image": fake_images,
"fake_logits": fake_logits,
}
return tf.contrib.tpu.TPUEstimatorSpec(mode,
predictions=predictions)
# --------------------------------------------------------------------------
# Train or Eval
# --------------------------------------------------------------------------
loss = g_loss
for i in range(params.n_critic):
loss += d_loss
if mode == tf.estimator.ModeKeys.EVAL:
# Hack to allow it out of a fixed batch size TPU
d_loss_batched = tf.tile(tf.expand_dims(d_loss, 0),
[params.batch_size])
g_loss_batched = tf.tile(tf.expand_dims(g_loss, 0),
[params.batch_size])
return tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=loss,
eval_metrics=(lambda d_loss, g_loss, fake_logits: self.
tpu_metric_fn(d_loss, g_loss, fake_logits),
[d_loss_batched, g_loss_batched, fake_logits]))
if mode == tf.estimator.ModeKeys.TRAIN:
# Create training ops for both D and G
d_optimizer = tf.train.AdamOptimizer(params.d_lr,
beta1=params.beta1,
beta2=params.beta2)
if params.use_tpu:
d_optimizer = tf.contrib.tpu.CrossShardOptimizer(d_optimizer)
d_train_op = d_optimizer.minimize(
d_loss,
var_list=d_vars,
global_step=tf.train.get_global_step())
g_optimizer = MovingAverageOptimizer(
tf.train.AdamOptimizer(params.g_lr,
beta1=params.beta1,
beta2=params.beta2),
average_decay=params.moving_decay)
if params.use_tpu:
g_optimizer = tf.contrib.tpu.CrossShardOptimizer(g_optimizer)
g_train_op = g_optimizer.minimize(
g_loss,
var_list=g_vars,
global_step=tf.train.get_global_step())
# For each training op of G, do n_critic training ops of D
train_ops = [g_train_op]
for i in range(params.n_critic):
train_ops.append(d_train_op)
train_op = tf.group(*train_ops)
return tf.contrib.tpu.TPUEstimatorSpec(mode,
loss=loss,
train_op=train_op)