def main():
# parse arguments
args = parse_args()
if args is None:
exit()
# open session
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
gan = SAGAN(sess, args)
# build graph
gan.build_model()
# show network architecture
show_all_variables()
if args.phase == 'train':
# launch the graph in a session
gan.train()
# visualize learned generator
gan.visualize_results(args.epoch - 1)
print(" [*] Training finished!")
if args.phase == 'test':
gan.test()
print(" [*] Test finished!")
def main():
# parse arguments
args = parse_args()
if args is None:
exit()
# open session
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
gan = SAGAN(sess, args)
# build graph
gan.build_model()
# show network architecture
show_all_variables()
if args.phase == 'train' :
# launch the graph in a session
gan.train()
# visualize learned generator
gan.visualize_results(args.epoch - 1)
print(" [*] Training finished!")
if args.phase == 'test' :
gan.test()
print(" [*] Test finished!")
def main():
args = parse_args()
if args is None:
exit()
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
gan = SAGAN(sess, args)
gan.build_model()
show_all_variables()
if args.phase == 'train':
gan.train()
gan.visualize_results(args.epoch - 1)
print(" [*] Training finished!")
if args.phase == 'test':
gan.test()
print(" [*] Test finished!")
def main():
# parse arguments
args = parse_args()
if args is None:
exit()
# open session
#tf.ConfigProto(allow_soft_placement=True):如果指定的设备不存在，允许tf自动分配设备。（我哪里来的GPU，sigh）
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
#建立模型
gan = SAGAN(sess, args)
gan.build_model()
show_all_variables()
#两个调用命令：train&&test
if args.phase == 'train':
gan.train()
gan.visualize_results(args.epoch - 1)
print(" [*] Training finished")
if args.phase == 'test':
gan.test()
print(" [*] Test finished")
def main():
# parse arguments
args = parse_args()
if args is None:
exit()
# declare instance for GAN
if args.gan_type == 'GAN':
gan = GAN(args)
elif args.gan_type == 'CGAN':
gan = CGAN(args)
elif args.gan_type == 'WGAN':
gan = WGAN(args)
elif args.gan_type == 'VAE':
gan = VAE(args)
elif args.gan_type == 'LSGAN':
gan = LSGAN(args)
elif args.gan_type == 'CVAE':
gan = CVAE(args)
elif args.gan_type == 'WGAN_GP':
gan = WGAN_GP(args)
elif args.gan_type == 'LSGAN':
gan = LSGAN(args)
elif args.gan_type == 'EBGAN':
gan = EBGAN(args)
elif args.gan_type == 'infoGAN':
gan = infoGAN(args)
elif args.gan_type == 'ACGAN':
gan = ACGAN(args)
elif args.gan_type == 'SAGAN':
gan = SAGAN(args)
else:
raise Exception("[!] There is no option for " + args.gan_type)
# launch the graph in a session
gan.train()
print(" [*] Training finished!")
# visualize learned generator
gan.visualize_results(args.epoch)
print(" [*] Testing finished!")
def main():
# parse arguments
args = parse_args()
if args is None:
exit()
# open session
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
# declare instance for GAN
if args.gan_type == 'GAN':
gan = GAN(sess,
epoch=args.epoch,
batch_size=args.batch_size,
z_dim=args.z_dim,
dataset_name=args.dataset,
checkpoint_dir=args.checkpoint_dir,
result_dir=args.result_dir,
log_dir=args.log_dir)
elif args.gan_type == 'CGAN':
gan = CGAN(sess,
epoch=args.epoch,
batch_size=args.batch_size,
z_dim=args.z_dim,
dataset_name=args.dataset,
checkpoint_dir=args.checkpoint_dir,
result_dir=args.result_dir,
log_dir=args.log_dir)
elif args.gan_type == 'ACGAN':
gan = ACGAN(sess,
epoch=args.epoch,
batch_size=args.batch_size,
z_dim=args.z_dim,
dataset_name=args.dataset,
checkpoint_dir=args.checkpoint_dir,
result_dir=args.result_dir,
log_dir=args.log_dir)
elif args.gan_type == 'infoGAN':
gan = infoGAN(sess,
epoch=args.epoch,
batch_size=args.batch_size,
z_dim=args.z_dim,
dataset_name=args.dataset,
checkpoint_dir=args.checkpoint_dir,
result_dir=args.result_dir,
log_dir=args.log_dir)
elif args.gan_type == 'EBGAN':
gan = EBGAN(sess,
epoch=args.epoch,
batch_size=args.batch_size,
z_dim=args.z_dim,
dataset_name=args.dataset,
checkpoint_dir=args.checkpoint_dir,
result_dir=args.result_dir,
log_dir=args.log_dir)
elif args.gan_type == 'WGAN':
gan = WGAN(sess,
epoch=args.epoch,
batch_size=args.batch_size,
z_dim=args.z_dim,
dataset_name=args.dataset,
checkpoint_dir=args.checkpoint_dir,
result_dir=args.result_dir,
log_dir=args.log_dir)
elif args.gan_type == 'WGAN_GP':
gan = WGAN_GP(sess,
epoch=args.epoch,
batch_size=args.batch_size,
z_dim=args.z_dim,
dataset_name=args.dataset,
checkpoint_dir=args.checkpoint_dir,
result_dir=args.result_dir,
log_dir=args.log_dir)
elif args.gan_type == 'DRAGAN':
gan = DRAGAN(sess,
epoch=args.epoch,
batch_size=args.batch_size,
z_dim=args.z_dim,
dataset_name=args.dataset,
checkpoint_dir=args.checkpoint_dir,
result_dir=args.result_dir,
log_dir=args.log_dir)
elif args.gan_type == 'LSGAN':
gan = LSGAN(sess,
epoch=args.epoch,
batch_size=args.batch_size,
z_dim=args.z_dim,
dataset_name=args.dataset,
checkpoint_dir=args.checkpoint_dir,
result_dir=args.result_dir,
log_dir=args.log_dir)
elif args.gan_type == 'BEGAN':
gan = BEGAN(sess,
epoch=args.epoch,
batch_size=args.batch_size,
z_dim=args.z_dim,
dataset_name=args.dataset,
checkpoint_dir=args.checkpoint_dir,
result_dir=args.result_dir,
log_dir=args.log_dir)
elif args.gan_type == 'VAE':
gan = VAE(sess,
epoch=args.epoch,
batch_size=args.batch_size,
z_dim=args.z_dim,
dataset_name=args.dataset,
checkpoint_dir=args.checkpoint_dir,
result_dir=args.result_dir,
log_dir=args.log_dir)
elif args.gan_type == 'CVAE':
gan = CVAE(sess,
epoch=args.epoch,
batch_size=args.batch_size,
z_dim=args.z_dim,
dataset_name=args.dataset,
checkpoint_dir=args.checkpoint_dir,
result_dir=args.result_dir,
log_dir=args.log_dir)
elif args.gan_type == 'VAE_GAN':
gan = VAE_GAN(sess,
epoch=args.epoch,
batch_size=args.batch_size,
z_dim=args.z_dim,
dataset_name=args.dataset,
checkpoint_dir=args.checkpoint_dir,
result_dir=args.result_dir,
log_dir=args.log_dir)
elif args.gan_type == 'SAGAN':
gan = SAGAN(sess,
epoch=args.epoch,
batch_size=args.batch_size,
z_dim=args.z_dim,
dataset_name=args.dataset,
checkpoint_dir=args.checkpoint_dir,
result_dir=args.result_dir,
log_dir=args.log_dir)
else:
raise Exception("[!] There is no option for " + args.gan_type)
# build graph
gan.build_model()
# show network architecture
show_all_variables()
# launch the graph in a session
gan.train()
print(" [*] Training finished!")
# visualize learned generator
gan.visualize_results(args.epoch - 1)
print(" [*] Testing finished!")
# -*- coding: utf-8 -*-
from config import Config
from SAGAN import SAGAN
if __name__ == "__main__":
config = Config()
model = SAGAN(config)
if config.RESUME_TRAIN:
discriminator_path = "./result/181104_1905/weights/ramen_cam/discriminator245000.hdf5"
generator_path = "./result/181104_1905/weights/ramen_cam/generator245000.hdf5"
print("Training start at {} iterations".format(config.COUNTER))
model.resume_train(discriminator_path, generator_path, config.COUNTER)
else:
print("Training start")
model.train()