def main():
# parse arguments
args = parse_args()
if args is None:
exit()
print(args)
if args.benchmark_mode:
torch.backends.cudnn.benchmark = True
# declare instance for GAN
if args.gan_type == 'GAN':
gan = GAN(args)
elif args.gan_type == 'CGAN':
gan = CGAN(args)
elif args.gan_type == 'ACGAN':
gan = ACGAN(args)
elif args.gan_type == 'infoGAN':
gan = infoGAN(args, SUPERVISED=False)
elif args.gan_type == 'EBGAN':
gan = EBGAN(args)
elif args.gan_type == 'WGAN':
gan = WGAN(args)
elif args.gan_type == 'WGAN_GP':
gan = WGAN_GP(args)
elif args.gan_type == 'DRAGAN':
gan = DRAGAN(args)
elif args.gan_type == 'LSGAN':
generator = model.InfoGANGenerator(input_dim=62,
output_dim=3,
input_size=args.input_size)
discriminator = model.InfoGANDiscriminator(input_dim=3,
output_dim=1,
input_size=args.input_size)
gan = LSGAN(args, generator, discriminator)
elif args.gan_type == 'LSGAN_classifier':
generator = model.InfoGANGenerator(input_dim=62,
output_dim=3,
input_size=args.input_size)
discriminator = model.InfoGANDiscriminatorClassifier(
input_dim=3,
output_dim=1,
input_size=args.input_size,
save_dir=args.save_dir,
model_name=args.gan_type)
gan = LSGAN(args, generator, discriminator)
elif args.gan_type == 'BEGAN':
gan = BEGAN(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()
# declare instance for GAN
if args.gan_type == 'GAN':
gan = GAN(args)
elif args.gan_type == 'CGAN':
gan = CGAN(args)
elif args.gan_type == 'ACGAN':
gan = ACGAN(args)
elif args.gan_type == 'infoGAN':
gan = infoGAN(args, SUPERVISED=True)
elif args.gan_type == 'EBGAN':
gan = EBGAN(args)
elif args.gan_type == 'WGAN':
gan = WGAN(args)
elif args.gan_type == 'DRAGAN':
gan = DRAGAN(args)
elif args.gan_type == 'LSGAN':
gan = LSGAN(args)
elif args.gan_type == 'BEGAN':
gan = BEGAN(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()
if args.benchmark_mode:
torch.backends.cudnn.benchmark = True
# declare instance for GAN
if args.gan_type == 'GAN':
gan = GAN(args)
elif args.gan_type == 'ACGAN':
gan = ACGAN(args)
elif args.gan_type == 'infoGAN':
gan = infoGAN(args, SUPERVISED=False)
elif args.gan_type == 'WGAN':
gan = WGAN(args)
elif args.gan_type == 'WGAN_GP':
gan = WGAN_GP(args)
elif args.gan_type == 'LSGAN':
gan = LSGAN(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()
if args.benchmark_mode:
torch.backends.cudnn.benchmark = True
'''
如果网络的输入数据维度或类型上变化不大，设置
torch.backends.cudnn.benchmark = true
可以增加运行效率；如果网络的输入数据在每次
iteration都变化的话，会导致cudnn
每次都会去寻找一遍最优配置，这样反而会降低运行效率。
'''
# declare instance for GAN
if args.gan_type == 'GAN':
print("GAN is "+args.gan_type)
gan = GAN(args)
elif args.gan_type == 'CGAN':
print("GAN is " + args.gan_type)
gan = CGAN(args)
elif args.gan_type == 'ACGAN':
print("GAN is " + args.gan_type)
gan = ACGAN(args)
elif args.gan_type == 'infoGAN':
print("GAN is " + args.gan_type)
gan = infoGAN(args, SUPERVISED=False)
elif args.gan_type == 'EBGAN':
print("GAN is " + args.gan_type)
gan = EBGAN(args)
elif args.gan_type == 'WGAN':
print("GAN is " + args.gan_type)
gan = WGAN(args)
elif args.gan_type == 'WGAN_GP':
print("GAN is " + args.gan_type)
gan = WGAN_GP(args)
elif args.gan_type == 'DRAGAN':
print("GAN is " + args.gan_type)
gan = DRAGAN(args)
elif args.gan_type == 'LSGAN':
print("GAN is " + args.gan_type)
gan = LSGAN(args)
elif args.gan_type == 'BEGAN':
print("GAN is " + args.gan_type)
gan = BEGAN(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():
args = parse_args()
data_loader = Data.DataLoader(datasets.FashionMNIST(
root='data/fashion-MNIST',
train=True,
download=True,
transform=transforms.ToTensor(),
),
shuffle=True,
batch_size=args.batch_size)
if args.gan_type == "GAN":
gan = GAN(args, data_loader)
elif args.gan_type == "LSGAN":
gan = LSGAN(args, data_loader)
gan.train()
def main():
"""main"""
# parse arguments
args = parse_args()
print('Training {},started at {}'.format(
args.gan_type, time.asctime(time.localtime(time.time()))))
if args is None:
exit()
if args.benchmark_mode:
torch.backends.cudnn.benchmark = True
# declare instance for GAN
if args.gan_type == 'GAN':
gan = GAN(args)
# elif args.gan_type == 'CGAN':
# gan = CGAN(args)
elif args.gan_type == 'ACGAN':
gan = ACGAN(args)
# elif args.gan_type == 'infoGAN':
# gan = infoGAN(args, SUPERVISED=False)
# elif args.gan_type == 'EBGAN':
# gan = EBGAN(args)
elif args.gan_type == 'WGAN':
gan = WGAN(args)
elif args.gan_type == 'WGAN_GP':
gan = WGAN_GP(args)
# elif args.gan_type == 'DRAGAN':
# gan = DRAGAN(args)
elif args.gan_type == 'LSGAN':
gan = LSGAN(args)
elif args.gan_type == 'BEGAN':
gan = BEGAN(args)
else:
raise Exception("[!] There is no option for " + args.gan_type)
# launch the graph in a session
# return
gan.train()
print('Training {},finished at {}'.format(
args.gan_type, time.asctime(time.localtime(time.time()))))
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)
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!")
def main():
# parse arguments
args = parse_args()
if args is None:
exit()
if args.benchmark_mode:
torch.backends.cudnn.benchmark = True
# declare instance for GAN
if args.gan_type == 'GAN':
gan = GAN(args)
elif args.gan_type == 'CGAN':
gan = CGAN(args)
elif args.gan_type == 'ACGAN':
gan = ACGAN(args)
elif args.gan_type == 'infoGAN':
gan = infoGAN(args, SUPERVISED=False)
elif args.gan_type == 'EBGAN':
gan = EBGAN(args)
elif args.gan_type == 'WGAN':
gan = WGAN(args)
elif args.gan_type == 'WGAN_GP':
gan = WGAN_GP(args)
elif args.gan_type == 'DRAGAN':
gan = DRAGAN(args)
elif args.gan_type == 'LSGAN':
gan = LSGAN(args)
elif args.gan_type == 'BEGAN':
gan = BEGAN(args)
elif args.gan_type == 'TOGAN':
gan = TOGAN(args)
elif args.gan_type == 'CVAE':
gan = CVAE(args)
elif args.gan_type == None:
pass
else:
raise Exception("[!] There is no option for " + args.gan_type)
if args.use_fake_data:
fakedata = gan.load()
else:
fakedata = None
if args.clf_type == 'clf':
clf = CLF(args, fakedata)
clf.load()
# clf.train()
else:
gan.train()
# gan()
# gan.load()
# launch the graph in a session
# clf.train()
print(" [*] Training finished!")
# visualize learned generator
# gan.visualize_results(args.epoch)
print(" [*] Testing finished!")
def main():
# parse arguments
opts = parse_args()
if opts is None:
exit()
# declare instance for GAN
if opts.gan_type == 'GAN':
gan = GAN(opts)
elif opts.gan_type == 'CGAN':
gan = CGAN(opts)
elif opts.gan_type == 'ACGAN':
gan = ACGAN(opts)
elif opts.gan_type == 'infoGAN':
gan = infoGAN(opts, SUPERVISED=True)
elif opts.gan_type == 'EBGAN':
gan = EBGAN(opts)
elif opts.gan_type == 'WGAN':
gan = WGAN(opts)
elif opts.gan_type == 'WGAN_GP':
gan = WGAN_GP(opts)
elif opts.gan_type == 'DRAGAN':
gan = DRAGAN(opts)
elif opts.gan_type == 'LSGAN':
gan = LSGAN(opts)
elif opts.gan_type == 'BEGAN':
gan = BEGAN(opts)
elif opts.gan_type == 'DRGAN':
gan = DRGAN(opts)
elif opts.gan_type == 'AE':
gan = AutoEncoder(opts)
elif opts.gan_type == 'GAN3D':
gan = GAN3D(opts)
elif opts.gan_type == 'VAEGAN3D':
gan = VAEGAN3D(opts)
elif opts.gan_type == 'DRGAN3D':
gan = DRGAN3D(opts)
elif opts.gan_type == 'Recog3D':
gan = Recog3D(opts)
elif opts.gan_type == 'Recog2D':
gan = Recog2D(opts)
elif opts.gan_type == 'VAEDRGAN3D':
gan = VAEDRGAN3D(opts)
elif opts.gan_type == 'DRcycleGAN3D':
gan = DRcycleGAN3D(opts)
elif opts.gan_type == 'CycleGAN3D':
gan = CycleGAN3D(opts)
elif opts.gan_type == 'AE3D':
gan = AutoEncoder3D(opts)
elif opts.gan_type == 'DRGAN2D':
gan = DRGAN2D(opts)
elif opts.gan_type == 'DRecon3DGAN':
gan = DRecon3DGAN(opts)
elif opts.gan_type == 'DRecon2DGAN':
gan = DRecon2DGAN(opts)
elif opts.gan_type == 'DReconVAEGAN':
gan = DReconVAEGAN(opts)
else:
raise Exception("[!] There is no option for " + opts.gan_type)
if opts.resume or len(opts.eval) > 0:
print(" [*] Loading saved model...")
gan.load()
print(" [*] Loading finished!")
# launch the graph in a session
if len(opts.eval) == 0:
gan.train()
print(" [*] Training finished!")
else:
print(" [*] Training skipped!")
# visualize learned generator
if len(opts.eval) == 0:
print(" [*] eval mode is not specified!")
else:
if opts.eval == 'generate':
gan.visualize_results(opts.epoch)
elif opts.eval == 'interp_z':
gan.interpolate_z(opts)
elif opts.eval == 'interp_id':
gan.interpolate_id(opts)
elif opts.eval == 'interp_expr':
gan.interpolate_expr(opts)
elif opts.eval == 'recon':
gan.reconstruct()
elif opts.eval == 'control_expr':
gan.control_expr()
else:
gan.manual_inference(opts)
print(" [*] Testing finished!")
