def main(_):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
model = UNet(batch_size=args.batch_size,embedding_dim=args.embedding_dim,input_width=args.image_size,output_width=args.image_size,embedding_num=args.embedding_num)
model.register_session(sess)
model.build_model(is_training=False, inst_norm=args.inst_norm)
embedding_ids = [int(i) for i in args.embedding_ids.split(",")]
if not args.interpolate:
if len(embedding_ids) == 1:
embedding_ids = embedding_ids[0]
model.infer(model_dir=args.model_dir, source_obj=args.source_obj, embedding_ids=embedding_ids,
save_dir=args.save_dir)
else:
if len(embedding_ids) < 2:
raise Exception("no need to interpolate yourself unless you are a narcissist")
chains = embedding_ids[:]
if args.uroboros:
chains.append(chains[0])
pairs = list()
for i in range(len(chains) - 1):
pairs.append((chains[i], chains[i + 1]))
for s, e in pairs:
model.interpolate(model_dir=args.model_dir, source_obj=args.source_obj, between=[s, e],
save_dir=args.save_dir, steps=args.steps)
if args.output_gif:
gif_path = os.path.join(args.save_dir, args.output_gif)
compile_frames_to_gif(args.save_dir, gif_path)
print("gif saved at %s" % gif_path)
def main(_):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
model = UNet(args.experiment_dir,
batch_size=args.batch_size,
experiment_id=args.experiment_id,
input_width=args.image_size,
output_width=args.image_size,
embedding_num=args.embedding_num,
embedding_dim=args.embedding_dim)
model.register_session(sess)
if args.flip_labels:
model.build_model(is_training=True,
inst_norm=args.inst_norm,
no_target_source=True)
else:
model.build_model(is_training=True, inst_norm=args.inst_norm)
fine_tune_list = None
if args.fine_tune:
ids = args.fine_tune.split(",")
fine_tune_list = set([int(i) for i in ids])
model.train(lr=args.lr,
epoch=args.epoch,
resume=args.resume,
schedule=args.schedule,
freeze_encoder=args.freeze_encoder,
fine_tune=fine_tune_list,
sample_steps=args.sample_steps,
checkpoint_steps=args.checkpoint_steps,
flip_labels=args.flip_labels)
def main():
model = UNet(args.experiment_dir,
batch_size=args.batch_size,
experiment_id=args.experiment_id,
input_width=args.image_size,
output_width=args.image_size,
embedding_num=args.embedding_num,
embedding_dim=args.embedding_dim,
L1_penalty=args.L1_penalty,
Lconst_penalty=args.Lconst_penalty,
Ltv_penalty=args.Ltv_penalty,
Lcategory_penalty=args.Lcategory_penalty)
# model.register_session(sess)
if args.flip_labels:
model.build_model(is_training=True,
inst_norm=args.inst_norm,
no_target_source=True)
else:
model.build_model(is_training=True, inst_norm=args.inst_norm)
fine_tune_list = None
if args.fine_tune:
ids = args.fine_tune.split(",")
fine_tune_list = set([int(i) for i in ids])
model.train(lr=args.lr,
epoch=args.epoch,
resume=args.resume,
schedule=args.schedule,
freeze_encoder=args.freeze_encoder,
fine_tune=fine_tune_list,
sample_steps=args.sample_steps,
checkpoint_steps=args.checkpoint_steps,
flip_labels=args.flip_labels,
no_val=args.no_val)
def main(_):
# 탄력적으로 GPU 메모리를 사용하기 위해 allow_growth를 true로 설정
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
# zi2zi 전체 모델 생성(GAN 모델 전체)
model = UNet(args.experiment_dir, batch_size=args.batch_size, experiment_id=args.experiment_id,
input_width=args.image_size, output_width=args.image_size, embedding_num=args.embedding_num,
embedding_dim=args.embedding_dim, L1_penalty=args.L1_penalty, Lconst_penalty=args.Lconst_penalty,
Ltv_penalty=args.Ltv_penalty, Lcategory_penalty=args.Lcategory_penalty)
model.register_session(sess)
if args.flip_labels:
model.build_model(is_training=True, inst_norm=args.inst_norm, no_target_source=True)
else:
model.build_model(is_training=True, inst_norm=args.inst_norm)
fine_tune_list = None
# 구체적인 미세조정 글자가 옵션으로 지정되었다면,
if args.fine_tune:
ids = args.fine_tune.split(",")
fine_tune_list = set([int(i) for i in ids])
# zi2zi 모델 학습 시작
model.train(lr=args.lr, epoch=args.epoch, resume=args.resume,
schedule=args.schedule, freeze_encoder=args.freeze_encoder, fine_tune=fine_tune_list,
sample_steps=args.sample_steps, checkpoint_steps=args.checkpoint_steps,
flip_labels=args.flip_labels)
def main(_):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
model = UNet(batch_size=args.batch_size)
model.register_session(sess)
model.build_model(is_training=False, inst_norm=args.inst_norm)
model.export_generator(save_dir=args.save_dir, model_dir=args.model_dir)
def main(_):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
src_font = ImageFont.truetype(args.src_font, size=args.char_size)
with tf.Session(config=config) as sess:
model = UNet(batch_size=args.batch_size,
input_width=args.canvas_size,
output_width=args.canvas_size,
experiment_id=args.experiment_id,
embedding_dim=args.embedding_dim,
embedding_num=args.embedding_num)
model.register_session(sess)
model.build_model(is_training=False, inst_norm=args.inst_norm)
model.load_model(args.model_dir)
count = 0
batch_buffer = list()
examples = []
for ch in list(args.text):
src_img = draw_single_char_by_font(ch, src_font, args.canvas_size,
args.char_size)
paired_img = draw_paired_image(src_img, src_img, args.canvas_size)
p = os.path.join(args.save_dir, "inferred_%04d.png" % 100)
misc.imsave(p, paired_img)
buffered = BytesIO()
paired_img.save(buffered, format="JPEG")
examples.append((args.embedding_id, buffered.getvalue()))
batch_iter = get_batch_iter(examples, args.batch_size, augment=False)
for _, images in batch_iter:
# inject specific embedding style here
labels = [args.embedding_id] * len(images)
fake_imgs = model.generate_fake_samples(images, labels)[0]
merged_fake_images = merge(scale_back(fake_imgs),
[-1, 1]) # scale 0-1
print("getshape", type(merged_fake_images),
merged_fake_images.shape)
if len(batch_buffer
) > 0 and merged_fake_images.shape != batch_buffer[0].shape:
continue
batch_buffer.append(merged_fake_images)
# if len(batch_buffer) == 10:
# save_imgs(batch_buffer, count, args.save_dir)
# batch_buffer = list()
count += 1
if batch_buffer:
# last batch
save_imgs(batch_buffer, count, args.save_dir)
def main(_):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
model = UNet(args.experiment_dir,
batch_size=args.batch_size,
experiment_id=args.experiment_id,
input_width=args.image_size,
output_width=args.image_size,
embedding_num=args.embedding_num,
embedding_dim=args.embedding_dim,
L1_penalty=args.L1_penalty,
Lconst_penalty=args.Lconst_penalty,
Ltv_penalty=args.Ltv_penalty,
Lcategory_penalty=args.Lcategory_penalty)
model.register_session(sess)
if args.flip_labels:
model.build_model(is_training=True,
inst_norm=args.inst_norm,
no_target_source=True)
else:
model.build_model(is_training=True, inst_norm=args.inst_norm)
fine_tune_list = None
if args.fine_tune:
ids = args.fine_tune.split(",")
fine_tune_list = set([int(i) for i in ids])
print("***************** number of parameters *******************")
def get_num_params():
num_params = 0
for variable in tf.trainable_variables():
shape = variable.get_shape()
p = reduce(mul, [dim.value for dim in shape], 1)
print(variable.name, p)
num_params += p
return num_params
print(get_num_params())
print("***************** number of parameters *******************")
model.train(lr=args.lr,
epoch=args.epoch,
resume=args.resume,
schedule=args.schedule,
freeze_encoder=args.freeze_encoder,
fine_tune=fine_tune_list,
sample_steps=args.sample_steps,
checkpoint_steps=args.checkpoint_steps,
flip_labels=args.flip_labels)
def main(_):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
##tensorflow auto-select available GPu
#with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True)) as sess:
with tf.Session(config=config) as sess:
model = UNet(args.experiment_dir,
batch_size=args.batch_size,
experiment_id=args.experiment_id,
input_width=args.image_size,
output_width=args.image_size,
embedding_num=args.embedding_num,
embedding_dim=args.embedding_dim,
L1_penalty=args.L1_penalty,
Lconst_penalty=args.Lconst_penalty,
Ltv_penalty=args.Ltv_penalty,
Lcategory_penalty=args.Lcategory_penalty)
model.register_session(sess)
if args.flip_labels:
model.build_model(is_training=True,
inst_norm=args.inst_norm,
no_target_source=True)
else:
model.build_model(is_training=True, inst_norm=args.inst_norm)
fine_tune_list = None
if args.fine_tune:
ids = args.fine_tune.split(",")
fine_tune_list = set([int(i) for i in ids])
model.train(lr=args.lr,
epoch=args.epoch,
resume=args.resume,
schedule=args.schedule,
freeze_encoder=args.freeze_encoder,
fine_tune=fine_tune_list,
sample_steps=args.sample_steps,
checkpoint_steps=args.checkpoint_steps,
flip_labels=args.flip_labels)
def infer_by_text_api2(str, str2, embedding_id, path):
# CUDA_VISIBLE_DEVICES=0
# --model_dir=experiments/checkpoint/experiment_0
# --batch_size=32
# --embedding_id=67
# --save_dir=save_dir
rootpath = os.path.dirname(os.path.abspath(__file__))
print(rootpath)
# path = os.path.join(rootpath, 'zi2zi')
# default
experiment_id = 0
model_dir = os.path.join(rootpath, "experiments/checkpoint/experiment_0")
batch_size = 16
text = "库昊又双叒叕进三分了"
# embedding_id = 67
embedding_dim = EMBEDDING_DIM
# save_dir = os.path.join(rootpath, 'save_dir')
inst_norm = 1
char_size = CHAR_SIZE
src_font = os.path.join(rootpath, 'data/raw_fonts/SimSun.ttf')
canvas_size = CANVAS_SIZE
embedding_num = 185
# ours
text = str
batch_size = 32
model_dir = os.path.join(rootpath, "experiments/checkpoint/experiment_0")
# embedding_id = 67
save_dir = os.path.join(rootpath, "save_dir")
# print(str, path)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
src_font = ImageFont.truetype(src_font, size=char_size)
with tf.Session(config=config) as sess:
model = UNet(batch_size=batch_size,
input_width=canvas_size,
output_width=canvas_size,
experiment_id=experiment_id,
embedding_dim=embedding_dim,
embedding_num=embedding_num)
model.register_session(sess)
model.build_model(is_training=False, inst_norm=inst_norm)
model.load_model(model_dir)
count = 0
batch_buffer = list()
examples = []
for ch in list(text):
src_img = draw_single_char_by_font(ch, src_font, canvas_size,
char_size)
paired_img = draw_paired_image(src_img, src_img, canvas_size)
# p = os.path.join(save_dir, "inferred_%04d.png" % 100)
# p = path
# misc.imsave(p, paired_img)
buffered = BytesIO()
paired_img.save(buffered, format="JPEG")
examples.append((embedding_id, buffered.getvalue()))
batch_iter1 = get_batch_iter(examples, batch_size, augment=False)
examples = []
for ch in list(str2):
src_img = draw_single_char_by_font(ch, src_font, canvas_size,
char_size)
paired_img = draw_paired_image(src_img, src_img, canvas_size)
# p = os.path.join(save_dir, "inferred_%04d.png" % 100)
# p = path
# misc.imsave(p, paired_img)
buffered = BytesIO()
paired_img.save(buffered, format="JPEG")
examples.append((embedding_id, buffered.getvalue()))
batch_iter2 = get_batch_iter(examples, batch_size, augment=False)
for _, images in batch_iter1:
# inject specific embedding style here
labels = [embedding_id] * len(images)
fake_imgs = model.generate_fake_samples(images, labels)[0]
merged_fake_images = merge(scale_back(fake_imgs),
[-1, 1]) # scale 0-1
# print("getshape", type(merged_fake_images), merged_fake_images.shape)
# if len(batch_buffer) > 0 and merged_fake_images.shape != batch_buffer[0].shape:
# continue
batch_buffer.append(merged_fake_images)
# print("getshape",merged_fake_images.shape)
# if len(batch_buffer) == 10:
# save_imgs(batch_buffer, count, save_dir, path)
# batch_buffer = list()
# count += 1
for _, images in batch_iter2:
# inject specific embedding style here
labels = [embedding_id] * len(images)
fake_imgs = model.generate_fake_samples(images, labels)[0]
merged_fake_images = merge(scale_back(fake_imgs),
[-1, 1]) # scale 0-1
# print("getshape", type(merged_fake_images), merged_fake_images.shape)
# if len(batch_buffer) > 0 and merged_fake_images.shape != batch_buffer[0].shape:
# continue
batch_buffer.append(merged_fake_images)
# print("getshape",merged_fake_images.shape)
# if len(batch_buffer) == 10:
# save_imgs(batch_buffer, count, save_dir, path)
# batch_buffer = list()
# count += 1
if batch_buffer:
# last batch
# l = len(batch_buffer)
# for i in range(l, 10):
# batch_buffer.append(np.ones(81))
save_imgs2(batch_buffer, count, save_dir, path)
model = None
return path
