def main():
start_time = time.time() # Clocking start
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# BEGAN Model
model = began.BEGAN(s) # BEGAN
# Initializing
s.run(tf.global_variables_initializer())
# Celeb-A DataSet images
ds = DataSet(input_height=32,
input_width=32,
input_channel=3,
mode='r').images
dataset_iter = DataIterator(ds, None, train_step['batch_size'],
label_off=True)
sample_x = ds[:model.sample_num]
sample_x = np.reshape(sample_x, [-1] + model.image_shape[1:])
sample_z = np.random.uniform(-1., 1., [model.sample_num, model.z_dim]).astype(np.float32)
# Export real image
valid_image_height = model.sample_size
valid_image_width = model.sample_size
sample_dir = results['output'] + 'valid.png'
# Generated image save
iu.save_images(sample_x, size=[valid_image_height, valid_image_width], image_path=sample_dir)
global_step = 0
for epoch in range(train_step['epoch']):
for batch_images in dataset_iter.iterate():
batch_x = np.reshape(batch_images, [-1] + model.image_shape[1:])
batch_z = np.random.uniform(-1., 1., [model.batch_size, model.z_dim]).astype(np.float32)
# Update D network
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
feed_dict={
model.z: batch_z,
})
# Update k_t
_, k, m_global = s.run([model.k_update, model.k, model.m_global],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
if global_step % train_step['logging_step'] == 0:
batch_z = np.random.uniform(-1., 1., [model.batch_size, model.z_dim]).astype(np.float32)
# Summary
_, k, m_global, d_loss, g_loss, summary = s.run([model.k_update, model.k, model.m_global,
model.d_loss, model.g_loss, model.merged],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Print loss
print("[+] Epoch %04d Step %07d =>" % (epoch, global_step),
" D loss : {:.8f}".format(d_loss),
" G loss : {:.8f}".format(g_loss),
" k : {:.8f}".format(k),
" M : {:.8f}".format(m_global))
# Summary saver
model.writer.add_summary(summary, epoch)
# Training G model with sample image and noise
samples = s.run(model.g,
feed_dict={
model.x: sample_x,
model.z: sample_z,
})
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results['output'] + 'train_{0}_{1}.png'.format(epoch, global_step)
# Generated image save
iu.save_images(samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir)
# Model save
model.saver.save(s, results['model'], global_step=global_step)
global_step += 1
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()
def main():
start_time = time.time() # Clocking start
# loading CelebA DataSet
ds = DataSet(
height=64,
width=64,
channel=3,
ds_image_path="/home/zero/hdd/DataSet/CelebA/CelebA-64.h5",
ds_label_path="/home/zero/hdd/DataSet/CelebA/Anno/list_attr_celeba.txt",
# ds_image_path="/home/zero/hdd/DataSet/CelebA/Img/img_align_celeba/",
ds_type="CelebA",
use_save=False,
save_file_name="/home/zero/hdd/DataSet/CelebA/CelebA-64.h5",
save_type="to_h5",
use_img_scale=False,
# img_scale="-1,1"
)
# saving sample images
test_images = np.reshape(iu.transform(ds.images[:16], inv_type='127'),
(16, 64, 64, 3))
iu.save_images(test_images,
size=[4, 4],
image_path=results['output'] + 'sample.png',
inv_type='127')
ds_iter = DataIterator(x=ds.images,
y=None,
batch_size=train_step['batch_size'],
label_off=True)
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# EBGAN Model
model = ebgan.EBGAN(
s, enable_pull_away=True) # using pull away loss # EBGAN-PT
# Initializing
s.run(tf.global_variables_initializer())
# Load model & Graph & Weights
saved_global_step = 0
ckpt = tf.train.get_checkpoint_state('./model/')
if ckpt and ckpt.model_checkpoint_path:
model.saver.restore(s, ckpt.model_checkpoint_path)
saved_global_step = int(
ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
print("[+] global step : %s" % saved_global_step,
" successfully loaded")
else:
print('[-] No checkpoint file found')
global_step = saved_global_step
start_epoch = global_step // (ds.num_images // model.batch_size
) # recover n_epoch
ds_iter.pointer = saved_global_step % (
ds.num_images // model.batch_size) # recover n_iter
for epoch in range(start_epoch, train_step['epochs']):
for batch_x in ds_iter.iterate():
batch_x = iu.transform(batch_x, inv_type='127')
batch_x = np.reshape(batch_x, (model.batch_size, model.height,
model.width, model.channel))
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
feed_dict={
model.z: batch_z,
})
# Logging
if global_step % train_step['logging_interval'] == 0:
summary = s.run(model.merged,
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Print loss
print(
"[+] Epoch %02d Step %08d => " % (epoch, global_step),
" D loss : {:.8f}".format(d_loss),
" G loss : {:.8f}".format(g_loss))
# Training G model with sample image and noise
sample_z = np.random.uniform(
-1., 1.,
[model.sample_num, model.z_dim]).astype(np.float32)
samples = s.run(model.g, feed_dict={
model.z: sample_z,
})
# Summary saver
model.writer.add_summary(summary, global_step)
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results['output'] + 'train_{:08d}.png'.format(
global_step)
# Generated image save
iu.save_images(
samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir,
inv_type='127')
# Model save
model.saver.save(s, results['model'], global_step)
global_step += 1
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()
def main():
start_time = time.time() # Clocking start
# Training, Test data set
# loading Cifar DataSet
ds = DataSet(height=32,
width=32,
channel=3,
ds_path='D:\\DataSet/cifar/cifar-10-batches-py/',
ds_name='cifar-10')
# saving sample images
test_images = np.reshape(iu.transform(ds.test_images[:16], inv_type='127'), (16, 32, 32, 3))
iu.save_images(test_images,
size=[4, 4],
image_path=results['output'] + 'sample.png',
inv_type='127')
ds_iter = DataIterator(x=ds.train_images,
y=None,
batch_size=train_step['batch_size'],
label_off=True)
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# GAN Model
model = lsgan.LSGAN(s, train_step['batch_size'])
# Initializing variables
s.run(tf.global_variables_initializer())
# Load model & Graph & Weights
saved_global_step = 0
ckpt = tf.train.get_checkpoint_state('./model/')
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
model.saver.restore(s, ckpt.model_checkpoint_path)
saved_global_step = int(ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
print("[+] global step : %d" % saved_global_step, " successfully loaded")
else:
print('[-] No checkpoint file found')
global_step = saved_global_step
start_epoch = global_step // (len(ds.train_images) // model.batch_size)
ds_iter.pointer = saved_global_step % (len(ds.train_images) // model.batch_size) # recover n_iter
for epoch in range(start_epoch, train_step['epoch']):
for batch_x in ds_iter.iterate():
batch_x = iu.transform(batch_x, inv_type='127')
batch_x = np.reshape(batch_x, [-1] + model.image_shape[1:])
batch_z = np.random.uniform(-1., 1., [model.batch_size, model.z_dim]).astype(np.float32)
# Update D network
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z
})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z
})
# Logging
if global_step % train_step['logging_interval'] == 0:
d_loss, g_loss, summary = s.run([model.d_loss, model.g_loss, model.merged],
feed_dict={
model.x: batch_x,
model.z: batch_z
})
# Print loss
print("[+] Epoch %02d Step %08d => " % (epoch, global_step),
" D loss : {:.8f}".format(d_loss),
" G loss : {:.8f}".format(g_loss))
# Training G model with sample image and noise
sample_z = np.random.uniform(-1., 1., [model.sample_num, model.z_dim]).astype(np.float32)
samples = s.run(model.g,
feed_dict={
model.z: sample_z,
})
# Summary saver
model.writer.add_summary(summary, global_step)
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results['output'] + 'train_{:08d}.png'.format(global_step)
# Generated image save
iu.save_images(samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir,
inv_type='127')
# Model save
model.saver.save(s, results['model'], global_step)
global_step += 1
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()
def main():
start_time = time.time() # Clocking start
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# AnoGAN Model
# anomalies detect off (just training model) -> False
# anomalies detect on (based on trained model-> True
if not os.path.exists('./model'):
detection = False
else:
detection = True
model = anogan.AnoGAN(s, detect=detection) # AnoGAN
global_step = 0
if detection:
# Load model & Graph & Weights
ckpt = tf.train.get_checkpoint_state('./model/')
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
model.saver.restore(s, ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split(
'-')[-1]
print("[+] global step : %s" % global_step,
" successfully loaded")
else:
print('[-] No checkpoint file found')
# Initializing
s.run(tf.global_variables_initializer())
# Celeb-A DataSet images
ds = DataSet(
input_height=64, # in the paper, 108
input_width=64, # in the paper, 108
input_channel=3).images
# To-Do
# Getting anomaly data
dataset_iter = DataIterator(ds,
None,
train_step['batch_size'],
label_off=True)
sample_x = ds[:model.sample_num]
sample_x = np.reshape(sample_x, [-1] + model.image_shape[1:])
sample_z = np.random.uniform(
-1., 1., [model.sample_num, model.z_dim]).astype(np.float32)
# Export real image
valid_image_height = model.sample_size
valid_image_width = model.sample_size
sample_dir = results['output'] + 'valid.png'
# Generated image save
iu.save_images(sample_x,
size=[valid_image_height, valid_image_width],
image_path=sample_dir)
for epoch in range(train_step['epoch']):
for batch_images in dataset_iter.iterate():
batch_x = np.reshape(batch_images,
[-1] + model.image_shape[1:])
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
# Update D network
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
feed_dict={
model.z: batch_z,
})
if global_step % train_step['logging_step'] == 0:
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
# Summary
d_loss, g_loss, summary = s.run(
[model.d_loss, model.g_loss, model.merged],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Print loss
print("[+] Epoch %04d Step %07d =>" % (epoch, global_step),
" D loss : {:.8f}".format(d_loss),
" G loss : {:.8f}".format(g_loss))
# Summary saver
model.writer.add_summary(summary, epoch)
# Training G model with sample image and noise
samples = s.run(model.g,
feed_dict={
model.x: sample_x,
model.z: sample_z,
})
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results['output'] + 'train_{0}_{1}.png'.format(
epoch, global_step)
# Generated image save
iu.save_images(
samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir)
# Model save
model.saver.save(s,
results['model'],
global_step=global_step)
global_step += 1
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()
def main():
start_time = time.time() # Clocking start
height, width, channel = 128, 128, 3
# loading CelebA DataSet # from 'raw images' or 'h5'
use_h5 = True
if not use_h5:
ds = DataSet(
height=height,
width=height,
channel=channel,
# ds_image_path="D:\\DataSet/CelebA/CelebA-%d.h5" % height,
ds_label_path=os.path.join(cfg.celeba,
"Anno/list_attr_celeba.txt"),
ds_image_path=os.path.join(cfg.celeba, "Img/img_align_celeba/"),
ds_type="CelebA",
use_save=True,
save_file_name=os.path.join(cfg.celeba, "CelebA-%d.h5" % height),
save_type="to_h5",
use_img_scale=False,
)
else:
ds = DataSet(
height=height,
width=height,
channel=channel,
ds_image_path=os.path.join(cfg.celeba, "CelebA-%d.h5" % height),
ds_label_path=os.path.join(cfg.celeba,
"Anno/list_attr_celeba.txt"),
# ds_image_path=os.path.join(cfg.celeba, "Img/img_align_celeba/"),
ds_type="CelebA",
use_save=False,
# save_file_name=os.path.join(cfg.celeba, "CelebA-%d.h5" % height),
# save_type="to_h5",
use_img_scale=False,
)
num_images = ds.num_images
# saving sample images
test_images = np.reshape(iu.transform(ds.images[:16], inv_type='127'),
(16, height, width, channel))
iu.save_images(test_images,
size=[4, 4],
image_path=os.path.join(cfg.output, "sample.png"),
inv_type='127')
ds_iter = DataIterator(x=ds.images,
y=None,
batch_size=train_step['batch_size'],
label_off=True)
del ds
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# SAGAN Model
model = sagan.SAGAN(s,
height=height,
width=width,
channel=channel,
batch_size=train_step['batch_size'],
use_gp=False,
use_hinge_loss=True)
# Initializing
s.run(tf.global_variables_initializer())
print("[*] Reading checkpoints...")
saved_global_step = 0
ckpt = tf.train.get_checkpoint_state(cfg.model_path)
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
model.saver.restore(s, ckpt.model_checkpoint_path)
saved_global_step = int(
ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
print("[+] global step : %d" % saved_global_step,
" successfully loaded")
else:
print('[-] No checkpoint file found')
global_step = saved_global_step
start_epoch = global_step // (num_images // model.batch_size
) # recover n_epoch
ds_iter.pointer = saved_global_step % (num_images // model.batch_size
) # recover n_iter
for epoch in range(start_epoch, train_step['epochs']):
for batch_x in ds_iter.iterate():
batch_x = iu.transform(batch_x, inv_type='127')
batch_x = np.reshape(batch_x, (model.batch_size, model.height,
model.width, model.channel))
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
# Update D network
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
if global_step % train_step['logging_interval'] == 0:
summary = s.run(model.merged,
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Training G model with sample image and noise
sample_z = np.random.uniform(
-1., 1.,
[model.sample_num, model.z_dim]).astype(np.float32)
samples = s.run(model.g_test,
feed_dict={
model.z_test: sample_z,
})
# is_mean, is_std = t.inception_score(iu.inverse_transform(samples, inv_type='127'))
# fid_score = t.fid_score(real_img=batch_x, fake_img=samples[:model.batch_size])
# Print loss
print(
"[+] Epoch %04d Step %08d => " % (epoch, global_step),
" D loss : {:.8f}".format(d_loss),
" G loss : {:.8f}".format(g_loss),
# " Inception Score : {:.2f} (±{:.2f})".format(is_mean, is_std),
# " FID Score : {:.2f}".format(fid_score)
)
# Summary saver
model.writer.add_summary(summary, global_step)
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = os.path.join(
cfg.output, 'train_{:08d}.png'.format(global_step))
# Generated image save
iu.save_images(
samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir,
inv_type='127')
# Model save
model.saver.save(
s, os.path.join(cfg.model_path, "SAGAN.ckpt"),
global_step)
global_step += 1
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()
def main():
start_time = time.time() # Clocking start
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# DCGAN model
model = dcgan.DCGAN(s, batch_size=train_step['batch_size'])
# Load model & Graph & Weights
ckpt = tf.train.get_checkpoint_state('./model/')
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
model.saver.restore(s, ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split(
'-')[-1]
print("[+] global step : %s" % global_step, " successfully loaded")
else:
global_step = 0
print('[-] No checkpoint file found')
# Initializing variables
s.run(tf.global_variables_initializer())
# Training, test data set
dataset = DataSet(input_height=32,
input_width=32,
input_channel=3,
name='cifar-100')
dataset_iter = DataIterator(dataset.train_images, dataset.train_labels,
train_step['batch_size'])
sample_x = dataset.valid_images[:model.sample_num].astype(
np.float32) / 225.
sample_z = np.random.uniform(-1., 1., [model.sample_num, model.z_dim])
d_overpowered = False # G loss > D loss * 2
step = int(global_step)
cont = int(step / 750)
for epoch in range(cont, cont + train_step['epoch']):
for batch_images, _ in dataset_iter.iterate():
batch_x = batch_images.astype(np.float32) / 225.
batch_z = np.random.uniform(
-1., 1.,
[train_step['batch_size'], model.z_dim]).astype(np.float32)
# Update D network
if not d_overpowered:
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z
})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
feed_dict={model.z: batch_z})
d_overpowered = d_loss < g_loss / 2.
if step % train_step['logging_interval'] == 0:
batch_z = np.random.uniform(
-1., 1.,
[train_step['batch_size'], model.z_dim]).astype(
np.float32)
d_loss, g_loss, summary = s.run(
[model.d_loss, model.g_loss, model.merged],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
d_overpowered = d_loss < g_loss / 2.
# Print loss
print("[+] Epoch %03d Step %05d => " % (epoch, step),
" D loss : {:.8f}".format(d_loss),
" G loss : {:.8f}".format(g_loss))
# Training G model with sample image and noise
samples = s.run(model.g,
feed_dict={
model.x: sample_x,
model.z: sample_z,
})
# Summary saver
model.writer.add_summary(summary, step)
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results[
'output'] + 'train_{0}_{1}.png'.format(epoch, step)
# Generated image save
iu.save_images(
samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir)
# Model save
model.saver.save(s, results['model'], global_step=step)
step += 1
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()
size=[4, 4],
image_path=results['output'] + 'sample.png',
inv_type='127')
ds_iter = DataIterator(x=ds.images,
y=None,
batch_size=train_step['batch_size'],
label_off=True)
d_loss = 0.
d_overpowered = False
global_step = saved_global_step
start_epoch = global_step // (len(ds.train_images) // model.batch_size) # recover n_epoch
ds_iter.pointer = saved_global_step % (len(ds.train_images) // model.batch_size) # recover n_iter
for epoch in range(start_epoch, train_step['epochs']):
for batch_x in range(ds_iter.iterate()):
#batch_x, _ = mnist.train.next_batch(model.batch_size)
batch_x, _ = ds_iter.next_batch()
#batch_x = batch_x.reshape(-1, model.n_input)
batch_x = np.reshape(iu.transform(batch_x, inv_type='127'),
(model.batch_size, model.height, model.width, model.channel))
batch_z = np.random.uniform(-1., 1., [model.batch_size, model.z_dim]).astype(np.float32)
# Update D network
if not d_overpowered:
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
def main():
start_time = time.time() # Clocking start
# loading CelebA DataSet
ds = DataSet(
height=64,
width=64,
channel=3,
ds_image_path="D:/DataSet/CelebA/CelebA-64.h5",
ds_label_path="D:/DataSet/CelebA/Anno/list_attr_celeba.txt",
# ds_image_path="D:/DataSet/CelebA/Img/img_align_celeba/",
ds_type="CelebA",
use_save=False,
save_file_name="D:/DataSet/CelebA/CelebA-64.h5",
save_type="to_h5",
use_img_scale=False,
img_scale="-1,1")
# saving sample images
test_images = np.reshape(iu.transform(ds.images[:100], inv_type='127'),
(100, 64, 64, 3))
iu.save_images(test_images,
size=[10, 10],
image_path=results['output'] + 'sample.png',
inv_type='127')
ds_iter = DataIterator(x=ds.images,
y=None,
batch_size=train_step['batch_size'],
label_off=True)
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# MAGAN Model
model = magan.MAGAN(s)
# Initializing
s.run(tf.global_variables_initializer())
# Load model & Graph & Weights
saved_global_step = 0
ckpt = tf.train.get_checkpoint_state('./model/')
if ckpt and ckpt.model_checkpoint_path:
model.saver.restore(s, ckpt.model_checkpoint_path)
saved_global_step = int(
ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
print("[+] global step : %s" % saved_global_step,
" successfully loaded")
else:
print('[-] No checkpoint file found')
n_steps = ds.num_images // model.batch_size # training set size
# Pre-Train
print("[*] pre-training - getting proper Margin")
margin = 0 # 3.0585415484215974
if margin == 0:
sum_d_loss = 0.
for i in range(2):
for batch_x in ds_iter.iterate():
batch_x = np.reshape(iu.transform(batch_x, inv_type='127'),
(model.batch_size, model.height,
model.width, model.channel))
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
_, d_real_loss = s.run([model.d_op, model.d_real_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
model.m: 0.,
})
sum_d_loss += d_real_loss
print("[*] Epoch {:1d} Sum of d_real_loss : {:.8f}".format(
i + 1, sum_d_loss))
# Initial margin value
margin = (sum_d_loss / n_steps)
print("[+] Margin : {0}".format(margin))
old_margin = margin
s_g_0 = np.inf # Sg_0 = infinite
global_step = saved_global_step
start_epoch = global_step // (ds.num_images // model.batch_size
) # recover n_epoch
ds_iter.pointer = saved_global_step % (
ds.num_images // model.batch_size) # recover n_iter
for epoch in range(start_epoch, train_step['epochs']):
s_d, s_g = 0., 0.
for batch_x in ds_iter.iterate():
batch_x = iu.transform(batch_x, inv_type='127')
batch_x = np.reshape(batch_x, (model.batch_size, model.height,
model.width, model.channel))
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
# Update D network
_, d_loss, d_real_loss = s.run(
[model.d_op, model.d_loss, model.d_real_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
model.m: margin,
})
# Update D real sample
s_d += np.sum(d_real_loss)
# Update G network
_, g_loss, d_fake_loss = s.run(
[model.g_op, model.g_loss, model.d_fake_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
model.m: margin,
})
# Update G fake sample
s_g += np.sum(d_fake_loss)
# Logging
if global_step % train_step['logging_interval'] == 0:
summary = s.run(model.merged,
feed_dict={
model.x: batch_x,
model.z: batch_z,
model.m: margin,
})
# Print loss
print(
"[+] Epoch %03d Global Step %05d => " %
(epoch, global_step),
" D loss : {:.8f}".format(d_loss),
" G loss : {:.8f}".format(g_loss))
# Training G model with sample image and noise
sample_z = np.random.uniform(
-1., 1.,
[model.sample_num, model.z_dim]).astype(np.float32)
samples = s.run(model.g,
feed_dict={
model.z: sample_z,
model.m: margin,
})
# Summary saver
model.writer.add_summary(summary, global_step)
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results['output'] + 'train_{:08d}.png'.format(
global_step)
# Generated image save
iu.save_images(
samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir,
inv_type='127')
# Model save
model.saver.save(s, results['model'], global_step)
global_step += 1
# Update margin
if s_d / n_steps < margin and s_d < s_g and s_g_0 <= s_g:
margin = s_d / n_steps
print("[*] Margin updated from {:8f} to {:8f}".format(
old_margin, margin))
old_margin = margin
s_g_0 = s_g
# Convergence Measure
e_d = s_d / n_steps
e_g = s_g / n_steps
l_ = e_d + np.abs(e_d - e_g)
print("[+] Epoch %03d " % epoch, " L : {:.8f}".format(l_))
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()
def main():
start_time = time.time() # Clocking start
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
if os.path.exists("./orig-model/"):
detect = True # There has to be pre-trained file
else:
detect = False
# AnoGAN Model
model = anogan.AnoGAN(detect=detect,
use_label=False) # AnoGAN
# Initializing
s.run(tf.global_variables_initializer())
# loading CelebA DataSet
ds = DataSet(height=64,
width=64,
channel=3,
ds_image_path="D:\\DataSet/CelebA/CelebA-64.h5",
ds_label_path="D:\\DataSet/CelebA/Anno/list_attr_celeba.txt",
# ds_image_path="D:\\DataSet/CelebA/Img/img_align_celeba/",
ds_type="CelebA",
use_save=False,
save_file_name="D:\\DataSet/CelebA/CelebA-128.h5",
save_type="to_h5",
use_img_scale=False,
# img_scale="-1,1"
)
# saving sample images
test_images = np.reshape(iu.transform(ds.images[:16], inv_type='127'), (16, 64, 64, 3))
iu.save_images(test_images,
size=[4, 4],
image_path=results['output'] + 'sample.png',
inv_type='127')
ds_iter = DataIterator(x=ds.images,
y=None,
batch_size=train_step['batch_size'],
label_off=True)
# To-Do
# Getting anomaly data
# Load model & Graph & Weights
if not detect or not os.path.exists("./ano-model/"):
ckpt = tf.train.get_checkpoint_state('./orig-model/')
else:
ckpt = tf.train.get_checkpoint_state('./ano-model/')
saved_global_step = 0
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
model.saver.restore(s, ckpt.model_checkpoint_path)
saved_global_step = int(ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
print("[+] global step : %d" % saved_global_step, " successfully loaded")
else:
print('[-] No checkpoint file found')
global_step = saved_global_step
start_epoch = global_step // (ds.num_images // model.batch_size) # recover n_epoch
ds_iter.pointer = saved_global_step % (ds.num_images // model.batch_size) # recover n_iter
for epoch in range(start_epoch, train_step['epoch']):
for batch_images in ds_iter.iterate():
batch_x = np.reshape(batch_images, [-1] + model.image_shape[1:])
batch_z = np.random.uniform(-1., 1., [model.batch_size, model.z_dim]).astype(np.float32)
# Update D network
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
feed_dict={
model.z: batch_z,
})
if global_step % train_step['logging_step'] == 0:
batch_z = np.random.uniform(-1., 1., [model.batch_size, model.z_dim]).astype(np.float32)
# Summary
d_loss, g_loss, summary = s.run([model.d_loss, model.g_loss, model.merged],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Print loss
print("[+] Epoch %04d Step %07d =>" % (epoch, global_step),
" D loss : {:.8f}".format(d_loss),
" G loss : {:.8f}".format(g_loss))
# Summary saver
model.writer.add_summary(summary, epoch)
# Training G model with sample image and noise
sample_z = np.random.uniform(-1., 1., [model.sample_num, model.z_dim]).astype(np.float32)
samples = s.run(model.g_test,
feed_dict={
model.z: sample_z,
})
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results['output'] + 'train_{0}_{1}.png'.format(epoch, global_step)
# Generated image save
iu.save_images(samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir)
# Model save
if not detect:
model.saver.save(s, results['orig-model'], global_step=global_step)
else:
model.saver.save(s, results['ano-model'], global_step=global_step)
global_step += 1
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()
def main():
start_time = time.time() # Clocking start
# GPU configure
gpu_config = tf.GPUOptions(allow_growth=True)
config = tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_config)
with tf.Session(config=config) as s:
# BEGAN Model
model = began.BEGAN(s) # BEGAN
# Initializing
s.run(tf.global_variables_initializer())
# Celeb-A DataSet images
ds = DataSet(height=64,
width=64,
channel=3,
ds_path="/home/zero/hdd/DataSet/CelebA/",
ds_type="CelebA").images
ds_iter = DataIterator(ds, None, train_step['batch_size'],
label_off=True)
global_step = 0
for epoch in range(train_step['epoch']):
for batch_images in ds_iter.iterate():
batch_x = np.reshape(batch_images, [-1] + model.image_shape[1:])
batch_z = np.random.uniform(-1., 1., [model.batch_size, model.z_dim]).astype(np.float32)
# Update D network
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
feed_dict={
model.z: batch_z,
})
# Update k_t
_, k, m_global = s.run([model.k_update, model.k, model.m_global],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
if global_step % train_step['logging_step'] == 0:
_, k, m_global, d_loss, g_loss, summary = s.run([model.k_update, model.k, model.m_global,
model.d_loss, model.g_loss, model.merged],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Print loss
print("[+] Epoch %03d Step %07d =>" % (epoch, global_step),
" D loss : {:.6f}".format(d_loss),
" G loss : {:.6f}".format(g_loss),
" k : {:.6f}".format(k),
" M : {:.6f}".format(m_global))
# Summary saver
model.writer.add_summary(summary, global_step)
# Training G model with sample image and noise
sample_z = np.random.uniform(-1., 1., [model.sample_num, model.z_dim]).astype(np.float32)
samples = s.run(model.g,
feed_dict={
model.z: sample_z,
})
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results['output'] + 'train_{0}.png'.format(global_step)
# Generated image save
iu.save_images(samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir,
inv_type='127')
# Model save
model.saver.save(s, results['model'], global_step=global_step)
# Learning Rate update
if global_step and global_step % model.lr_update_step == 0:
s.run([model.g_lr_update, model.d_lr_update])
global_step += 1
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()
def main():
start_time = time.time() # Clocking start
# Training, test data set
ds = DataSet(height=32,
width=32,
channel=3,
ds_path='D:\\DataSet/cifar/cifar-10-batches-py/',
ds_name='cifar-10')
ds_iter = DataIterator(ds.train_images, ds.train_labels,
train_step['batch_size'])
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# LAPGAN model
model = lapgan.LAPGAN(s, batch_size=train_step['batch_size'])
# Initializing variables
s.run(tf.global_variables_initializer())
# Load model & Graph & Weights
saved_global_step = 0
ckpt = tf.train.get_checkpoint_state('./model/')
if ckpt and ckpt.model_checkpoint_path:
model.saver.restore(s, ckpt.model_checkpoint_path)
saved_global_step = int(
ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
print("[+] global step : %s" % saved_global_step,
" successfully loaded")
else:
print('[-] No checkpoint file found')
sample_y = np.zeros(shape=[model.sample_num, model.n_classes])
for i in range(10):
sample_y[10 * i:10 * (i + 1), i] = 1
global_step = saved_global_step
start_epoch = global_step // (len(ds.train_images) // model.batch_size
) # recover n_epoch
ds_iter.pointer = saved_global_step % (
len(ds.train_images) // model.batch_size) # recover n_iter
for epoch in range(start_epoch, train_step['epoch']):
for batch_images, batch_labels in ds_iter.iterate():
batch_x = iu.transform(batch_images, inv_type='127')
z = []
for i in range(3):
z.append(
np.random.uniform(
-1., 1.,
[train_step['batch_size'], model.z_noises[i]]))
# Update D/G networks
img_fake, img_coarse, d_loss_1, g_loss_1, \
_, _, _, d_loss_2, g_loss_2, \
_, _, d_loss_3, g_loss_3, \
_, _, _, _, _, _ = s.run([
model.g[0], model.x1_coarse, model.d_loss[0], model.g_loss[0],
model.x2_fine, model.g[1], model.x2_coarse, model.d_loss[1], model.g_loss[1],
model.x3_fine, model.g[2], model.d_loss[2], model.g_loss[2],
model.d_op[0], model.g_op[0], model.d_op[1], model.g_op[1], model.d_op[2], model.g_op[2],
],
feed_dict={
model.x1_fine: batch_x, # images
model.y: batch_labels, # classes
model.z[0]: z[0], model.z[1]: z[1], model.z[2]: z[2], # z-noises
model.do_rate: 0.5,
})
# Logging
if global_step % train_step['logging_interval'] == 0:
batch_x = ds.test_images[np.random.randint(
0, len(ds.test_images), model.sample_num)]
batch_x = iu.transform(batch_x, inv_type='127')
z = []
for i in range(3):
z.append(
np.random.uniform(
-1., 1.,
[model.sample_num, model.z_noises[i]]))
# Update D/G networks
img_fake, img_coarse, d_loss_1, g_loss_1, \
_, _, _, d_loss_2, g_loss_2, \
_, _, d_loss_3, g_loss_3, \
_, _, _, _, _, _, summary = s.run([
model.g[0], model.x1_coarse, model.d_loss[0], model.g_loss[0],
model.x2_fine, model.g[1], model.x2_coarse, model.d_loss[1], model.g_loss[1],
model.x3_fine, model.g[2], model.d_loss[2], model.g_loss[2],
model.d_op[0], model.g_op[0], model.d_op[1], model.g_op[1], model.d_op[2], model.g_op[2],
model.merged,
],
feed_dict={
model.x1_fine: batch_x, # images
model.y: sample_y, # classes
model.z[0]: z[0], model.z[1]: z[1], model.z[2]: z[2], # z-noises
model.do_rate: 0.,
})
# Print loss
d_loss = (d_loss_1 + d_loss_2 + d_loss_3) / 3.
g_loss = (g_loss_1 + g_loss_2 + g_loss_3) / 3.
print(
"[+] Epoch %03d Step %05d => " % (epoch, global_step),
" Avg D loss : {:.8f}".format(d_loss),
" Avg G loss : {:.8f}".format(g_loss))
# Training G model with sample image and noise
samples = img_fake + img_coarse
# Summary saver
model.writer.add_summary(summary,
global_step) # time saving
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results['output'] + 'train_{0}.png'.format(
global_step)
# Generated image save
iu.save_images(
samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir,
inv_type='127')
# Model save
model.saver.save(s, results['model'], global_step)
global_step += 1
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()
def main():
start_time = time.time() # Clocking start
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
if os.path.exists("./orig-model/"):
detect = True # There has to be pre-trained file
else:
detect = False
# AnoGAN Model
model = anogan.AnoGAN(detect=detect, use_label=False) # AnoGAN
global_step = 0
# Load model & Graph & Weights
if not detect or not os.path.exists("./ano-model/"):
ckpt = tf.train.get_checkpoint_state('./orig-model/')
else:
ckpt = tf.train.get_checkpoint_state('./ano-model/')
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
model.saver.restore(s, ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split(
'-')[-1]
print("[+] global step : %s" % global_step, " successfully loaded")
else:
print('[-] No checkpoint file found')
# Initializing
s.run(tf.global_variables_initializer())
# Celeb-A DataSet images
ds = DataSet(height=64,
width=64,
channel=3,
ds_path="/home/zero/hdd/DataSet/CelebA/",
ds_type="CelebA").images
# To-Do
# Getting anomaly data
ds_iter = DataIterator(ds,
None,
train_step['batch_size'],
label_off=True)
for epoch in range(train_step['epoch']):
for batch_images in ds_iter.iterate():
batch_x = np.reshape(batch_images,
[-1] + model.image_shape[1:])
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
# Update D network
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
feed_dict={
model.z: batch_z,
})
if global_step % train_step['logging_step'] == 0:
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
# Summary
d_loss, g_loss, summary = s.run(
[model.d_loss, model.g_loss, model.merged],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Print loss
print("[+] Epoch %04d Step %07d =>" % (epoch, global_step),
" D loss : {:.8f}".format(d_loss),
" G loss : {:.8f}".format(g_loss))
# Summary saver
model.writer.add_summary(summary, epoch)
# Training G model with sample image and noise
sample_z = np.random.uniform(
-1., 1.,
[model.sample_num, model.z_dim]).astype(np.float32)
samples = s.run(model.g_test,
feed_dict={
model.z: sample_z,
})
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results['output'] + 'train_{0}_{1}.png'.format(
epoch, global_step)
# Generated image save
iu.save_images(
samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir)
# Model save
if not detect:
model.saver.save(s,
results['orig-model'],
global_step=global_step)
else:
model.saver.save(s,
results['ano-model'],
global_step=global_step)
global_step += 1
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()
def main():
start_time = time.time() # Clocking start
# Celeb-A DataSet images
ds = DataSet(input_height=1024,
input_width=1024,
input_channel=3,
ds_type="CelebA-HQ",
ds_path="/home/zero/hdd/DataSet/CelebA-HQ").images
n_ds = 30000
dataset_iter = DataIterator(ds,
None,
train_step['batch_size'],
label_off=True)
rnd = random.randint(0, n_ds)
sample_x = ds[rnd]
sample_x = np.reshape(sample_x, [-1, 1024, 1024, 3])
# Export real image
valid_image_height = 1
valid_image_width = 1
sample_dir = results['output'] + 'valid.png'
# Generated image save
iu.save_images(sample_x,
size=[valid_image_height, valid_image_width],
image_path=sample_dir,
inv_type='127')
print("[+] sample image saved!")
print("[+] pre-processing took {:.8f}s".format(time.time() - start_time))
# GPU configure
gpu_config = tf.GPUOptions(allow_growth=True)
config = tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_config)
for idx, n_pg in enumerate(pg):
with tf.Session(config=config) as s:
pg_t = False if idx % 2 == 0 else True
# PGGAN Model
model = pggan.PGGAN(s, pg=n_pg, pg_t=pg_t) # PGGAN
# Initializing
s.run(tf.global_variables_initializer())
if not n_pg == 1 and not n_pg == 7:
if pg_t:
model.r_saver.restore(
s, results['model'] + '%d-%d.ckpt' % (idx, r_pg[idx]))
model.out_saver.restore(
s, results['model'] + '%d-%d.ckpt' % (idx, r_pg[idx]))
else:
model.saver.restore(
s, results['model'] + '%d-%d.ckpt' % (idx, r_pg[idx]))
global_step = 0
for epoch in range(train_step['epoch']):
# Later, adding n_critic for optimizing D net
for batch_images in dataset_iter.iterate():
batch_x = np.reshape(batch_images, (-1, 128, 128, 3))
batch_x = (batch_x + 1.) * 127.5 # re-scaling to (0, 255)
batch_x = image_resize(batch_x, s=model.output_size)
batch_x = (batch_x / 127.5) - 1. # re-scaling to (-1, 1)
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
if pg_t and not pg == 0:
alpha = global_step / 32000.
low_batch_x = zoom(batch_x, zoom=[1., .5, .5, 1.])
low_batch_x = zoom(low_batch_x, zoom=[1., 2., 2., 1.])
batch_x = alpha * batch_x + (1. - alpha) * low_batch_x
# Update D network
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
feed_dict={
model.z: batch_z,
})
# Update alpha_trans
s.run(model.alpha_trans_update,
feed_dict={model.step_pl: global_step})
if global_step % train_step['logging_step'] == 0:
gp, d_loss, g_loss, summary = s.run([
model.gp, model.d_loss, model.g_loss, model.merged
],
feed_dict={
model.x:
batch_x,
model.z:
batch_z,
})
# Print loss
print(
"[+] PG %d Epoch %03d Step %07d =>" %
(n_pg, epoch, global_step),
" D loss : {:.6f}".format(d_loss),
" G loss : {:.6f}".format(g_loss),
" GP : {:.6f}".format(gp),
)
# Summary saver
model.writer.add_summary(summary, global_step)
# Training G model with sample image and noise
sample_z = np.random.uniform(
-1., 1.,
[model.sample_num, model.z_dim]).astype(np.float32)
samples = s.run(model.g,
feed_dict={
model.z: sample_z,
})
samples = np.clip(samples, -1, 1)
# Export image generated by model G
sample_image_height = 1
sample_image_width = 1
sample_dir = results[
'output'] + 'train_{0}.png'.format(global_step)
# Generated image save
iu.save_images(
samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir,
inv_type='127')
# Model save
model.saver.save(s,
results['model'] + '%d-%d.ckpt' %
(idx, n_pg),
global_step=global_step)
global_step += 1
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
def main():
start_time = time.time() # Clocking start
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# LAPGAN model # D/G Models are same as DCGAN
model = lapgan.LAPGAN(s, batch_size=train_step['batch_size'])
# Initializing variables
s.run(tf.global_variables_initializer())
# Training, test data set
dataset = DataSet(input_height=32,
input_width=32,
input_channel=3,
name='cifar-10')
dataset_iter = DataIterator(dataset.train_images, dataset.train_labels,
train_step['batch_size'])
step = 0
cont = int(step / 750)
for epoch in range(cont, cont + train_step['epoch']):
for batch_images, batch_labels in dataset_iter.iterate():
batch_images = batch_images.astype(np.float32) / 225.
z = []
for i in range(3):
z.append(
np.random.uniform(
-1., 1.,
[train_step['batch_size'], model.z_noises[i]
]).astype(np.float32))
# Update D/G networks
img_fake, _, _, img_coarse, d_loss_1, g_loss_1, \
_, _, _, _, _, d_loss_2, g_loss_2, \
_, _, _, _, d_loss_3, g_loss_3, \
_, _, _, _, _, _ = s.run([
model.g[0], model.d_reals_prob[0], model.d_fakes_prob[0], model.x1_coarse,
model.d_loss[0], model.g_loss[0],
model.x2_fine, model.g[1], model.d_reals_prob[1], model.d_fakes_prob[1], model.x2_coarse,
model.d_loss[1], model.g_loss[1],
model.x3_fine, model.g[2], model.d_reals_prob[2], model.d_fakes_prob[2],
model.d_loss[2], model.g_loss[2],
model.d_op[0], model.g_op[0], model.d_op[1], model.g_op[1], model.d_op[2], model.g_op[2], # D/G ops
],
feed_dict={
model.x1_fine: batch_images, # images
model.y: batch_labels, # classes
model.z[0]: z[0], model.z[1]: z[1], model.z[2]: z[2] # z-noises
})
# Logging
if step % train_step['logging_interval'] == 0:
batch_x = batch_images[:model.sample_num]
batch_y = batch_labels[:model.sample_num]
z = []
for i in range(3):
z.append(
np.random.uniform(
-1., 1.,
[model.sample_num, model.z_noises[i]]).astype(
np.float32))
# Update D/G networks
img_fake, _, _, img_coarse, d_loss_1, g_loss_1, \
_, _, _, _, _, d_loss_2, g_loss_2, \
_, _, _, _, d_loss_3, g_loss_3, \
_, _, _, _, _, _, summary = s.run([
model.g[0], model.d_reals_prob[0], model.d_fakes_prob[0], model.x1_coarse,
model.d_loss[0], model.g_loss[0],
model.x2_fine, model.g[1], model.d_reals_prob[1], model.d_fakes_prob[1], model.x2_coarse,
model.d_loss[1], model.g_loss[1],
model.x3_fine, model.g[2], model.d_reals_prob[2], model.d_fakes_prob[2],
model.d_loss[2], model.g_loss[2],
model.d_op[0], model.g_op[0], model.d_op[1], model.g_op[1], model.d_op[2], model.g_op[2],
model.merged,
],
feed_dict={
model.x1_fine: batch_x, # images
model.y: batch_y, # classes
model.z[0]: z[0], model.z[1]: z[1], model.z[2]: z[2] # z-noises
})
# Print loss
print("[+] Epoch %03d Step %05d => " % (epoch, step),
" D loss : {:.8f}".format(d_loss_1.mean()),
" G loss : {:.8f}".format(g_loss_1.mean()))
# Training G model with sample image and noise
samples = img_fake + img_coarse
# Summary saver
model.writer.add_summary(summary, step) # time saving
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results[
'output'] + 'train_{0}_{1}.png'.format(epoch, step)
# Generated image save
iu.save_images(
samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir)
# Model save
model.saver.save(s, results['model'],
global_step=step) # time saving
step += 1
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()
def main():
start_time = time.time() # Clocking start
# Loading Cifar-10 DataSet
ds = DataSet(height=32,
width=32,
channel=3,
ds_path="D:/DataSet/cifar/cifar-10-batches-py/",
ds_name='cifar-10')
ds_iter = DataIterator(
x=iu.transform(ds.train_images, '127'),
y=ds.train_labels,
batch_size=train_step['batch_size'],
label_off=False
) # using label # maybe someday, i'll change this param's name
# Generated image save
test_images = iu.transform(ds.test_images[:100], inv_type='127')
iu.save_images(test_images,
size=[10, 10],
image_path=results['output'] + 'sample.png',
inv_type='127')
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# ACGAN Model
model = acgan.ACGAN(s,
batch_size=train_step['batch_size'],
n_classes=ds.n_classes)
# Initializing
s.run(tf.global_variables_initializer())
sample_y = np.zeros(shape=[model.sample_num, model.n_classes])
for i in range(10):
sample_y[10 * i:10 * (i + 1), i] = 1
saved_global_step = 0
ckpt = tf.train.get_checkpoint_state('./model/')
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
model.saver.restore(s, ckpt.model_checkpoint_path)
saved_global_step = int(
ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
print("[+] global step : %d" % saved_global_step,
" successfully loaded")
else:
print('[-] No checkpoint file found')
global_step = saved_global_step
start_epoch = global_step // (len(ds.train_images) // model.batch_size
) # recover n_epoch
ds_iter.pointer = saved_global_step % (
len(ds.train_images) // model.batch_size) # recover n_iter
for epoch in range(start_epoch, train_step['epochs']):
for batch_x, batch_y in ds_iter.iterate():
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
# Update D network
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.y: batch_y,
model.z: batch_z,
})
# Update G/C networks
_, g_loss, _, c_loss = s.run(
[model.g_op, model.g_loss, model.c_op, model.c_loss],
feed_dict={
model.x: batch_x,
model.y: batch_y,
model.z: batch_z,
})
if global_step % train_step['logging_interval'] == 0:
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
d_loss, g_loss, c_loss, summary = s.run([
model.d_loss, model.g_loss, model.c_loss, model.merged
],
feed_dict={
model.x:
batch_x,
model.y:
batch_y,
model.z:
batch_z,
})
# Print loss
print(
"[+] Epoch %04d Step %08d => " % (epoch, global_step),
" D loss : {:.8f}".format(d_loss),
" G loss : {:.8f}".format(g_loss),
" C loss : {:.8f}".format(c_loss))
# Training G model with sample image and noise
sample_z = np.random.uniform(
-1., 1.,
[model.sample_num, model.z_dim]).astype(np.float32)
samples = s.run(model.g,
feed_dict={
model.y: sample_y,
model.z: sample_z,
})
# Summary saver
model.writer.add_summary(summary, global_step)
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results['output'] + 'train_{:08d}.png'.format(
global_step)
# Generated image save
iu.save_images(
samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir,
inv_type='127')
# Model save
model.saver.save(s, results['model'], global_step)
global_step += 1
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()
def main():
start_time = time.time() # Clocking start
# loading CelebA DataSet
ds = DataSet(
height=64,
width=64,
channel=3,
ds_image_path="/home/zero/hdd/DataSet/CelebA/CelebA-64.h5",
ds_label_path="/home/zero/hdd/DataSet/CelebA/Anno/list_attr_celeba.txt",
# ds_image_path="/home/zero/hdd/DataSet/CelebA/Img/img_align_celeba/",
ds_type="CelebA",
use_save=False,
save_file_name="/home/zero/hdd/DataSet/CelebA/CelebA-64.h5",
save_type="to_h5",
use_img_scale=False,
# img_scale="-1,1"
)
# saving sample images
test_images = np.reshape(iu.transform(ds.images[:100], inv_type='127'),
(100, 64, 64, 3))
iu.save_images(test_images,
size=[10, 10],
image_path=results['output'] + 'sample.png',
inv_type='127')
ds_iter = DataIterator(x=ds.images,
y=None,
batch_size=train_step['batch_size'],
label_off=True)
# GPU configure
gpu_config = tf.GPUOptions(allow_growth=True)
config = tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_config)
with tf.Session(config=config) as s:
# BEGAN Model
model = began.BEGAN(s, batch_size=train_step['batch_size'],
gamma=0.5) # BEGAN
# Initializing
s.run(tf.global_variables_initializer())
print("[*] Reading checkpoints...")
saved_global_step = 0
ckpt = tf.train.get_checkpoint_state('./model/')
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
model.saver.restore(s, ckpt.model_checkpoint_path)
saved_global_step = int(
ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
print("[+] global step : %d" % saved_global_step,
" successfully loaded")
else:
print('[-] No checkpoint file found')
global_step = saved_global_step
start_epoch = global_step // (ds.num_images // model.batch_size
) # recover n_epoch
ds_iter.pointer = saved_global_step % (
ds.num_images // model.batch_size) # recover n_iter
for epoch in range(start_epoch, train_step['epoch']):
for batch_x in ds_iter.iterate():
batch_x = iu.transform(batch_x, inv_type='127')
batch_x = np.reshape(batch_x, (model.batch_size, model.height,
model.width, model.channel))
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
# Update D network
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
feed_dict={
model.z: batch_z,
})
# Update k_t
_, k, m_global = s.run(
[model.k_update, model.k, model.m_global],
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
if global_step % train_step['logging_step'] == 0:
summary = s.run(model.merged,
feed_dict={
model.x: batch_x,
model.z: batch_z,
})
# Print loss
print("[+] Epoch %03d Step %07d =>" % (epoch, global_step),
" D loss : {:.6f}".format(d_loss),
" G loss : {:.6f}".format(g_loss),
" k : {:.6f}".format(k),
" M : {:.6f}".format(m_global))
# Summary saver
model.writer.add_summary(summary, global_step)
# Training G model with sample image and noise
sample_z = np.random.uniform(
-1., 1.,
[model.sample_num, model.z_dim]).astype(np.float32)
samples = s.run(model.g, feed_dict={
model.z: sample_z,
})
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results['output'] + 'train_{0}.png'.format(
global_step)
# Generated image save
iu.save_images(
samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir,
inv_type='127')
# Model save
model.saver.save(s,
results['model'],
global_step=global_step)
# Learning Rate update
if global_step and global_step % model.lr_update_step == 0:
s.run([model.g_lr_update, model.d_lr_update])
global_step += 1
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()
def main():
start_time = time.time() # Clocking start
# loading CelebA DataSet
labels = [
'Black_Hair', 'Blond_Hair', 'Blurry', 'Eyeglasses', 'Gray_Hair',
'Male', 'Smiling', 'Wavy_Hair', 'Wearing_Hat', 'Young'
]
ds = DataSet(
height=64,
width=64,
channel=3,
ds_image_path="/home/zero/hdd/DataSet/CelebA/CelebA-64.h5",
ds_label_path="/home/zero/hdd/DataSet/CelebA/Anno/list_attr_celeba.txt",
attr_labels=labels,
# ds_image_path="D:\\DataSet/CelebA/Img/img_align_celeba/",
ds_type="CelebA",
use_save=False,
save_file_name="D:\\DataSet/CelebA/CelebA-64.h5",
save_type="to_h5",
use_img_scale=False,
# img_scale="-1,1"
)
# saving sample images
test_images = np.reshape(iu.transform(ds.images[:16], inv_type='127'),
(16, 64, 64, 3))
iu.save_images(test_images,
size=[4, 4],
image_path=results['output'] + 'sample.png',
inv_type='127')
ds_iter = DataIterator(x=ds.images,
y=ds.labels,
batch_size=train_step['batch_size'],
label_off=False)
# GPU configure
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as s:
# InfoGAN Model
model = infogan.InfoGAN(s,
height=64,
width=64,
channel=3,
batch_size=train_step['batch_size'],
n_categories=len(ds.labels))
# fixed z-noise
sample_z = np.random.uniform(
-1., 1., [model.sample_num, model.z_dim]).astype(np.float32)
# Initializing
s.run(tf.global_variables_initializer())
# Load model & Graph & Weights
saved_global_step = 0
ckpt = tf.train.get_checkpoint_state('./model/')
if ckpt and ckpt.model_checkpoint_path:
model.saver.restore(s, ckpt.model_checkpoint_path)
saved_global_step = int(
ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
print("[+] global step : %s" % saved_global_step,
" successfully loaded")
else:
print('[-] No checkpoint file found')
global_step = saved_global_step
start_epoch = global_step // (ds.num_images // model.batch_size
) # recover n_epoch
ds_iter.pointer = saved_global_step % (
ds.num_images // model.batch_size) # recover n_iter
for epoch in range(start_epoch, train_step['epochs']):
for batch_x, batch_y in ds_iter.iterate():
batch_x = iu.transform(batch_x, inv_type='127')
batch_x = np.reshape(batch_x, (model.batch_size, model.height,
model.width, model.channel))
batch_z = np.random.uniform(
-1., 1.,
[model.batch_size, model.z_dim]).astype(np.float32)
batch_z_con = gen_continuous(model.batch_size,
model.n_continous_factor)
batch_z_cat = gen_category(model.batch_size,
model.n_categories)
batch_c = np.concatenate((batch_z_con, batch_z_cat), axis=1)
# Update D network
_, d_loss = s.run([model.d_op, model.d_loss],
feed_dict={
model.c: batch_c,
model.x: batch_x,
model.z: batch_z,
})
# Update G network
_, g_loss = s.run([model.g_op, model.g_loss],
feed_dict={
model.c: batch_c,
model.x: batch_x,
model.z: batch_z,
})
# Logging
if global_step % train_step['logging_interval'] == 0:
summary = s.run(model.merged,
feed_dict={
model.c: batch_c,
model.x: batch_x,
model.z: batch_z,
})
# Print loss
print(
"[+] Epoch %02d Step %08d => " % (epoch, global_step),
" D loss : {:.8f}".format(d_loss),
" G loss : {:.8f}".format(g_loss))
# Training G model with sample image and noise
sample_z_con = np.zeros(
(model.sample_num, model.n_continous_factor))
for i in range(10):
sample_z_con[10 * i:10 * (i + 1),
0] = np.linspace(-2, 2, 10)
sample_z_cat = np.zeros(
(model.sample_num, model.n_categories))
for i in range(10):
sample_z_cat[10 * i:10 * (i + 1), i] = 1
sample_c = np.concatenate((sample_z_con, sample_z_cat),
axis=1)
samples = s.run(model.g,
feed_dict={
model.c: sample_c,
model.z: sample_z,
})
# Summary saver
model.writer.add_summary(summary, global_step)
# Export image generated by model G
sample_image_height = model.sample_size
sample_image_width = model.sample_size
sample_dir = results['output'] + 'train_{:08d}.png'.format(
global_step)
# Generated image save
iu.save_images(
samples,
size=[sample_image_height, sample_image_width],
image_path=sample_dir,
inv_type='127')
# Model save
model.saver.save(s, results['model'], global_step)
global_step += 1
end_time = time.time() - start_time # Clocking end
# Elapsed time
print("[+] Elapsed time {:.8f}s".format(end_time))
# Close tf.Session
s.close()