def train(self, args, ckpt_nmbr=None):
# Initialize augmentor.
augmentor = img_augm.Augmentor(crop_size=[self.options.image_size, self.options.image_size],
vertical_flip_prb=0.,
hsv_augm_prb=1.0,
hue_augm_shift=0.05,
saturation_augm_shift=0.05, saturation_augm_scale=0.05,
value_augm_shift=0.05, value_augm_scale=0.05, )
content_dataset_places = prepare_dataset.PlacesDataset(path_to_dataset=self.options.path_to_content_dataset)
art_dataset = prepare_dataset.ArtDataset(path_to_art_dataset=self.options.path_to_art_dataset)
# Initialize queue workers for both datasets.
q_art = multiprocessing.Queue(maxsize=10)
q_content = multiprocessing.Queue(maxsize=10)
jobs = []
for i in range(5):
p = multiprocessing.Process(target=content_dataset_places.initialize_batch_worker,
args=(q_content, augmentor, self.batch_size, i))
p.start()
jobs.append(p)
p = multiprocessing.Process(target=art_dataset.initialize_batch_worker,
args=(q_art, augmentor, self.batch_size, i))
p.start()
jobs.append(p)
print("Processes are started.")
time.sleep(3)
# Now initialize the graph
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
print("Start training.")
if self.load(self.checkpoint_dir, ckpt_nmbr):
print(" [*] Load SUCCESS")
else:
if self.load(self.checkpoint_long_dir, ckpt_nmbr):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
# Initial discriminator success rate.
win_rate = args.discr_success_rate
discr_success = args.discr_success_rate
alpha = 0.05
for step in tqdm(range(self.initial_step, self.options.total_steps+1),
initial=self.initial_step,
total=self.options.total_steps):
# Get batch from the queue with batches q, if the last is non-empty.
while q_art.empty() or q_content.empty():
pass
batch_art = q_art.get()
batch_content = q_content.get()
if discr_success >= win_rate:
# Train generator
_, summary_all, gener_acc_ = self.sess.run(
[self.g_optim_step, self.summary_merged_all, self.gener_acc],
feed_dict={
self.input_painting: normalize_arr_of_imgs(batch_art['image']),
self.input_photo: normalize_arr_of_imgs(batch_content['image']),
self.lr: self.options.lr
})
discr_success = discr_success * (1. - alpha) + alpha * (1. - gener_acc_)
else:
# Train discriminator.
_, summary_all, discr_acc_ = self.sess.run(
[self.d_optim_step, self.summary_merged_all, self.discr_acc],
feed_dict={
self.input_painting: normalize_arr_of_imgs(batch_art['image']),
self.input_photo: normalize_arr_of_imgs(batch_content['image']),
self.lr: self.options.lr
})
discr_success = discr_success * (1. - alpha) + alpha * discr_acc_
self.writer.add_summary(summary_all, step * self.batch_size)
if step % self.options.save_freq == 0 and step > self.initial_step:
self.save(step)
# And additionally save all checkpoints each 15000 steps.
if step % 15000 == 0 and step > self.initial_step:
self.save(step, is_long=True)
if step % 500 == 0:
output_paintings_, output_photos_= self.sess.run(
[self.input_painting, self.output_photo],
feed_dict={
self.input_painting: normalize_arr_of_imgs(batch_art['image']),
self.input_photo: normalize_arr_of_imgs(batch_content['image']),
self.lr: self.options.lr
})
save_batch(input_painting_batch=batch_art['image'],
input_photo_batch=batch_content['image'],
output_painting_batch=denormalize_arr_of_imgs(output_paintings_),
output_photo_batch=denormalize_arr_of_imgs(output_photos_),
filepath='%s/step_%d.jpg' % (self.sample_dir, step))
print("Training is finished. Terminate jobs.")
for p in jobs:
p.join()
p.terminate()
print("Done.")
config['feature_loss_weight'],
config['discr_success_rate']
))
trainer = ArtGAN(opts).cuda()
initial_step = trainer.resume(checkpoint_directory, opts) if options.resume else 0
# prepare data
augmentor = img_augm.Augmentor(crop_size=[opts.image_size, opts.image_size],
vertical_flip_prb=0.,
hsv_augm_prb=1.0,
hue_augm_shift=0.05,
saturation_augm_shift=0.05,
saturation_augm_scale=0.05,
value_augm_shift=0.05,
value_augm_scale=0.05, )
content_dataset_places = prepare_dataset.PlacesDataset(opts.content_data_path)
art_dataset = prepare_dataset.ArtDataset(opts.art_data_path)
q_art = multiprocessing.Queue(maxsize=10)
q_content = multiprocessing.Queue(maxsize=10)
jobs = []
for i in range(4):
p = multiprocessing.Process(target=content_dataset_places.initialize_batch_worker,
args=(q_content, augmentor, opts.batch_size, i))
p.start()
jobs.append(p)
p = multiprocessing.Process(target=art_dataset.initialize_batch_worker,
args=(q_art, augmentor, opts.batch_size, i))
p.start()
jobs.append(p)
print("Processes are started.")
time.sleep(3)
def train(self, args):
# Initialize augmentor.
augmentor = img_augm.Augmentor(
crop_size=[self.options.image_size, self.options.image_size],
hue_augm_shift=0.05,
saturation_augm_shift=0.05,
saturation_augm_scale=0.05,
value_augm_shift=0.05,
value_augm_scale=0.05,
)
content_dataset_places = prepare_dataset.PlacesDataset(
path_to_dataset=self.options.path_to_content_dataset)
art_dataset = prepare_dataset.ArtDataset(
path_to_art_dataset=self.options.path_to_art_dataset)
# Now initialize the graph
init_op = tf.compat.v1.global_variables_initializer()
self.sess.run(init_op)
print("Start training.")
if self.load(self.checkpoint_dir):
print(" [*] Load SUCCESS")
else:
if self.load(self.checkpoint_long_dir):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
# Initial discriminator success rate.
win_rate = args.discr_success_rate
discr_success = args.discr_success_rate
alpha = 0.05
for step in tqdm(range(self.initial_step,
self.options.total_steps + 1),
initial=self.initial_step,
total=self.options.total_steps):
#print('step {}'.format(step))
batch_art = art_dataset.get_batch(augmentor=augmentor,
batch_size=self.batch_size)
batch_content = content_dataset_places.get_batch(
augmentor=augmentor, batch_size=self.batch_size)
if discr_success >= win_rate:
# Train generator
_, summary_all, gener_acc_ = self.sess.run(
[
self.g_optim_step, self.summary_merged_all,
self.gener_acc
],
feed_dict={
self.input_painting:
normalize_arr_of_imgs(batch_art['image']),
self.input_photo:
normalize_arr_of_imgs(batch_content['image']),
self.lr:
self.options.lr
})
discr_success = discr_success * (1. - alpha) + alpha * (
1. - gener_acc_)
else:
# Train discriminator.
_, summary_all, discr_acc_ = self.sess.run(
[
self.d_optim_step, self.summary_merged_all,
self.discr_acc
],
feed_dict={
self.input_painting:
normalize_arr_of_imgs(batch_art['image']),
self.input_photo:
normalize_arr_of_imgs(batch_content['image']),
self.lr:
self.options.lr
})
discr_success = discr_success * (1. -
alpha) + alpha * discr_acc_
self.writer.add_summary(summary_all, step * self.batch_size)
if step % self.options.save_freq == 0 and step > self.initial_step:
self.save(step)
# And additionally save all checkpoints each 15000 steps.
if step % 15000 == 0 and step > self.initial_step:
self.save(step, is_long=True)
if step % 500 == 0:
output_paintings_, output_photos_ = self.sess.run(
[self.input_painting, self.output_photo],
feed_dict={
self.input_painting:
normalize_arr_of_imgs(batch_art['image']),
self.input_photo:
normalize_arr_of_imgs(batch_content['image']),
self.lr:
self.options.lr
})
save_batch(
input_painting_batch=batch_art['image'],
input_photo_batch=batch_content['image'],
output_painting_batch=denormalize_arr_of_imgs(
output_paintings_),
output_photo_batch=denormalize_arr_of_imgs(output_photos_),
filepath='%s/step_%d.jpg' % (self.sample_dir, step))
print("Done.")
