def main(_):
if not os.path.exists('./test'):
os.makedirs('./test')
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = cfg.device_id
cfg.batch_size = 1
net = WGAN_GP()
profile = tf.placeholder(tf.float32, [1,224,224,3], name='profile')
front = tf.placeholder(tf.float32, [1,224,224,3], name='profile')
net.build_up(profile, front)
print('Load Finetuned Model Successfully!')
# Train or Test
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config, graph=net.graph) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(max_to_keep=0) #
saver.restore(sess, cfg.checkpoint_ft)
test_list = np.loadtxt(cfg.test_list, dtype='string',delimiter=',')
for img in test_list[:50]:
print(img)
img_np = read_img(cfg.test_path, img)
img_gen = sess.run(net.gen_p, {profile:img_np, net.is_train:False}) #
save_img('test', img, img_np, img_gen)
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 == 'EBGAN':
gan = EBGAN(args)
if load_flag == True:
EBGAN.load(gan)
elif args.gan_type == 'WGAN_GP':
gan = WGAN_GP(args)
if load_flag == True:
WGAN_GP.load(gan)
elif args.gan_type == 'BEGAN':
gan = BEGAN(args)
if load_flag == True:
BEGAN.load(gan)
print('load successful')
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()
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()
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(_):
check_dir()
print_config()
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
run_option = tf.ConfigProto(gpu_options=gpu_options)
with tf.Session(config=run_option) as sess:
wgan_gp = WGAN_GP(config=FLAGS, sess=sess)
wgan_gp.build_model()
if FLAGS.is_training:
wgan_gp.train_model()
if FLAGS.is_testing:
wgan_gp.test_model()
def main(args):
# load the wished dataset (WGAN-GP: We only train it on a subcategory, manually moved)
dataset_path = args.dataset_path
target_size = (args.img_size, args.img_size)
transforms = Compose([
Resize(target_size),
ToTensor(),
Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
train_dataset = ImageFolder(dataset_path, transform=transforms)
dataloader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True)
print(len(dataloader.dataset))
# initiative the model, relevant args: Channels, BatchSize, iterations, cuda, train
model = WGAN_GP(args)
# Load datasets to train and test loaders
# Start model training
if args.is_train is True:
model.train(dataloader)
def main():
with tf.Session() as sess:
gan = WGAN_GP(sess,
epoch=10000,
batch_size=16,
dataset_name='eyes.tfrecords',
checkpoint_dir='checkpoint',
result_dir='results',
log_dir='logs')
# build graph
gan.build_model()
show_all_variables()
gan.train()
print(" [*] Training finished!")
gan.visualize_results(20 - 1)
print(" [*] Testing finished!")
def main():
# parse arguments
args = parse_args()
if args is None:
exit()
if args.gan_type == 'LSGAN':
gan = LSGAN(args)
elif args.gan_type == 'LSGAN_gene':
gan = LSGAN_gene(args)
elif args.gan_type == 'WGAN_GP':
gan = WGAN_GP(args)
'''
# 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 == '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
gan.train()
print(" [*] Training finished!")
# visualize learned generator
gan.visualize_results(args.epoch)
print(" [*] Testing finished!")
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():
with tf.Session() as sess:
gan = WGAN_GP(sess,
epoch=10000,
batch_size=16,
dataset_name= 'eyes.tfrecords',
checkpoint_dir='checkpoint',
result_dir='results',
log_dir='logs')
# build graph
gan.build_model()
show_all_variables()
gan.train()
print(" [*] Training finished!")
gan.visualize_results(20-1)
print(" [*] Testing finished!")
def main(_):
# Environment Setting
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = cfg.device_id
if not os.path.exists(cfg.results):
os.makedirs(cfg.results)
if not os.path.exists(cfg.checkpoint):
os.makedirs(cfg.checkpoint)
if not os.path.exists(cfg.summary_dir):
os.makedirs(cfg.summary_dir)
# Construct Networks
net = WGAN_GP()
data_feed = loadData(batch_size=cfg.batch_size, train_shuffle=True) # False
profile, front = data_feed.get_train()
net.build_up(profile, front)
# Train or Test
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config, graph=net.graph) as sess:
sess.run(tf.global_variables_initializer())
# Start Thread
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver = tf.train.Saver(max_to_keep=0) #
if cfg.is_finetune:
saver.restore(sess, cfg.checkpoint_ft)
print('Load Finetuned Model Successfully!')
num_batch = int(cfg.dataset_size / cfg.batch_size)
writer = tf.summary.FileWriter(cfg.summary_dir, sess.graph)
# Train by minibatch and critic
for epoch in range(cfg.epoch):
for step in range(num_batch):
# Discriminator Part
if(step < 25 and epoch == 0 and not cfg.is_finetune):
critic = 25
else:
critic = cfg.critic
for i in range(critic):
_ = sess.run(net.train_dis, {net.is_train:True})
# Generative Part
#_,fl,gl,dl,gen,summary = sess.run([net.train_gen,net.feature_loss,net.g_loss,
# net.d_loss,net.gen_p,net.train_summary],
# {net.is_train:True})
_,fl,gl,dl,gen,g1,g2,g4,summary = sess.run([net.train_gen, net.feature_loss,net.g_loss,
net.d_loss,net.gen_p,net.grad1,net.grad2,net.grad4,net.train_summary],
{net.is_train:True})
#print('%d-%d, Fea Loss:%.2f, D Loss:%4.1f, G Loss:%4.1f,' % (epoch, step, fl, dl, gl))
print('%d-%d, Fea Loss:%.2f, D Loss:%4.1f, G Loss:%4.1f, g1/2/4:%.5f/%.5f/%.5f ' % #
(epoch, step, fl, dl, gl, g1*cfg.lambda_fea,g2,g4))
# Save Model and Summary and Test
if(step % cfg.save_freq == 0):
writer.add_summary(summary, epoch*num_batch + step)
print("Saving Model....")
saver.save(sess, os.path.join(cfg.checkpoint, 'ck-%02d' % (epoch))) #
# test
fl, dl, gl = 0., 0., 0.
for i in range(50): # 25791 / 16
te_profile, te_front = data_feed.get_test_batch(cfg.batch_size)
dl_, gl_, fl_, images = sess.run([net.d_loss,net.g_loss, net.feature_loss, net.gen_p],
{profile:te_profile, front:te_front, net.is_train:False}) #
data_feed.save_images(images, epoch)
dl += dl_; gl += gl_; fl += fl_
print('Testing: Fea Loss:%.1f, D Loss:%.1f, G Loss:%.1f' % (fl, dl, gl))
# Close Threads
coord.request_stop()
coord.join(threads)
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
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!")
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(_):
# Environment Setting
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
if not os.path.exists(cfg.results):
os.mkdir(cfg.results)
# Construct Networks
# Change this line if 'LSGAN' or 'WGAN'
net = WGAN_GP()
# Train and Test
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config, graph=net.graph) as sess:
sess.run(tf.global_variables_initializer())
# Start Thread
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver = tf.train.Saver(max_to_keep=0) #
if cfg.is_finetune:
saver.restore(sess, cfg.model_path)
print('Load Finetuned Model Successfully!')
num_batch = int(cfg.dataset_size / cfg.batch_size)
writer = tf.summary.FileWriter(cfg.summary_dir, sess.graph)
# Train by minibatch and critic
for epoch in range(cfg.epoch):
for step in range(num_batch):
# Discriminator Part
if (step < 25 and epoch == 0 and not cfg.is_finetune):
critic = 25
else:
critic = cfg.critic
for i in range(critic):
# add 'net.clip_D' into ops if 'LSGAN' or 'WGAN'
_ = sess.run(net.train_dis,
{net.is_train: True}) # net.clip_D
# Generative Part
_, fl, gl, dl, gen, summary = sess.run([
net.train_gen, net.feature_loss, net.g_loss, net.d_loss,
net.gen_p, net.train_summary
], {net.is_train: True})
writer.add_summary(summary, epoch * num_batch + step)
print('%d-%d, Fea Loss:%.2f, D Loss:%4.1f, G Loss:%4.1f'
% #g1/2/3:%.5f/%.5f/%.5f
(epoch, step, fl, dl, gl)) #g1*cfg.lambda_fea,g2,g4
# Test Part
if step % cfg.test_sum_freq == 0:
net.data_feed.save_train(gen)
fl, dl, gl = 0., 0., 0.
for i in range(test_num):
te_profile, te_front = net.data_feed.get_test_batch(
cfg.batch_size)
dl_, gl_, fl_, images = sess.run([net.d_loss,net.g_loss,\
net.feature_loss, net.gen_p],
{net.profile:te_profile, net.front:te_front, net.is_train:False}) #
net.data_feed.save_images(images, epoch)
dl += dl_
gl += gl_
fl += fl_
print('Testing: Fea Loss:%.1f, D Loss:%.1f, G Loss:%.1f' %
(fl / test_num, dl / test_num, gl / test_num))
# Save Model
if (step != 0 and step % cfg.save_freq == 0):
print("Saving Model....")
saver.save(sess, cfg.logdir + '-%02d' % (epoch)) #
# Close Threads
coord.request_stop()
coord.join(threads)
from utils import check_folder
import tensorflow as tf
import argparse
desc = "dimension"
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('--dim', type=int, help='input dimension')
args = parser.parse_args()
dim = args.dim
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
gan = WGAN_GP(sess,
epoch=20,
batch_size=128,
z_dim=dim,
dataset_name='fashion-mnist',
checkpoint_dir='checkpoints',
result_dir='results',
log_dir='logs')
if gan is None:
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!")