def main():
root_log_dir = "./logs/"
mkdir_p(root_log_dir)
batch_size = FLAGS.batch_size
max_iters = FLAGS.max_iters
sample_size = 256
GAN_learn_rate = FLAGS.learn_rate
OPER_FLAG = FLAGS.OPER_FLAG
data_In = CelebA(FLAGS.path)
#print ("the num of dataset", len(data_In.image_list))
if OPER_FLAG == 0:
r_fl = 5
test_list = [sys.argv[1]]
#'image_03902', 'image_06751', 'image_06069',
#'image_05211', 'image_05757', 'image_05758',
#'image_05105', 'image_03877', 'image_04325',
#'image_05173', 'image_06667', 'image_03133',
#'image_06625', 'image_06757', 'image_04065',
#'image_03155'
t = False
f = open("captions_all.txt","a")
f_c = open(sys.argv[2],"r")
f.write(sys.argv[1]+'\n')
for line in f_c:
f.write(line)
f.write("----\n")
f.close()
f_c.close()
pggan_checkpoint_dir_write = "./model_flowers_test/"
sample_path = "./PGGanFlowers/sample_test/"
mkdir_p(pggan_checkpoint_dir_write)
mkdir_p(sample_path)
pggan_checkpoint_dir_read = "./model_flowers_{}/{}/".format(OPER_FLAG, r_fl)
pggan = PGGAN(batch_size=batch_size, max_iters=max_iters,
model_path=pggan_checkpoint_dir_write, read_model_path=pggan_checkpoint_dir_read,
data=data_In, sample_size=sample_size,
sample_path=sample_path, log_dir=root_log_dir, learn_rate=GAN_learn_rate, PG= r_fl, t=t)
pggan.build_model_PGGan()
pggan.test(test_list,int(sys.argv[3]))
for i in range(FLAGS.flag):
t = False if (i % 2 == 0) else True
pggan_checkpoint_dir_write = "./PGGanCeleba{}/model_pggan_{}/{}/".format(
OPER_NAME, OPER_FLAG, fl[i])
sample_path = "./PGGanCeleba{}/{}/sample_{}_{}".format(
OPER_NAME, FLAGS.OPER_FLAG, fl[i], t)
mkdir_p(pggan_checkpoint_dir_write)
mkdir_p(sample_path)
pggan_checkpoint_dir_read = "./PGGanCeleba{}/model_pggan_{}/{}/".format(
OPER_NAME, OPER_FLAG, r_fl[i])
pggan = PGGAN(batch_size=FLAGS.batch_size,
max_iters=FLAGS.max_iters,
model_path=pggan_checkpoint_dir_write,
read_model_path=pggan_checkpoint_dir_read,
data=data_In,
sample_size=FLAGS.sample_size,
sample_path=sample_path,
log_dir=root_log_dir,
learn_rate=FLAGS.learn_rate,
lam_gp=FLAGS.lam_gp,
lam_eps=FLAGS.lam_eps,
PG=fl[i],
t=t,
use_wscale=FLAGS.use_wscale)
pggan.build_model_PGGan()
pggan.train()
return parser.parse_args()
if __name__ == "__main__":
args = arg_parse()
args.save_dir = "%s/outs/%s" % (os.getcwd(), args.save_dir)
if os.path.exists(args.save_dir) is False:
os.mkdir(args.save_dir)
CUDA = True if torch.cuda.is_available() else False
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
torch_device = torch.device("cuda") if CUDA else torch.device('cpu')
data_path = "../../data/celeba-" # resolution string will be concatenated in ScalableLoader
loader = ScalableLoader(data_path,
shuffle=True,
drop_last=True,
num_workers=args.cpus,
shuffled_cycle=True)
g = nn.DataParallel(Generator()).to(torch_device)
d = nn.DataParallel(Discriminator()).to(torch_device)
tensorboard = TensorboardLogger("%s/tb" % (args.save_dir))
pggan = PGGAN(args, g, d, loader, torch_device, args.loss, tensorboard)
pggan.train()
# r_fl = [1,1,2,2,3,3,4]
fl = [4]
r_fl = [4]
for i in range(int(FLAGS.flag)):
t = False if (i % 2 == 0) else True
# t = False
pggan_checkpoint_dir_write = "./model_pggan_{}/{}/".format(OPER_FLAG, fl[i])
sample_path = "./PGGanCifar10/{}/sample_{}_{}".format(FLAGS.OPER_FLAG, fl[i], t)
mkdir_p(pggan_checkpoint_dir_write)
mkdir_p(sample_path)
pggan_checkpoint_dir_read = "./model_pggan_{}/{}/".format(OPER_FLAG, r_fl[i])
pggan = PGGAN(batch_size=batch_size, max_iters=max_iters,
model_path=pggan_checkpoint_dir_write, read_model_path=pggan_checkpoint_dir_read,
data=data_In, sample_size=sample_size,
sample_path=sample_path, log_dir=root_log_dir, learn_rate=GAN_learn_rate, PG= fl[i],
t=t)
pggan.build_model_PGGan()
pggan.train()
sample_path = "./output/{}/{}/sample_{}_{}".format(
FLAGS.OPER_NAME, FLAGS.OPER_FLAG, fl[i], t)
mkdir_p(pggan_checkpoint_dir_write)
mkdir_p(sample_path)
pggan_checkpoint_dir_read = "./output/{}/model_pggan_{}/{}/".format(
FLAGS.OPER_NAME, FLAGS.OPER_FLAG, r_fl[i])
pggan = PGGAN(oper_name=FLAGS.OPER_NAME,
batch_size=FLAGS.batch_size,
max_iters=FLAGS.max_iters,
model_path=pggan_checkpoint_dir_write,
read_model_path=pggan_checkpoint_dir_read,
data=data_In,
sample_size=FLAGS.sample_size,
sample_path=sample_path,
log_dir=root_log_dir,
learn_rate=FLAGS.learn_rate,
lam_gp=FLAGS.lam_gp,
lam_eps=FLAGS.lam_eps,
PG=fl[i],
trans=t,
use_wscale=FLAGS.use_wscale,
is_celeba=FLAGS.celeba,
step_by_save_sample=FLAGS.step_by_save_sample,
step_by_save_weights=FLAGS.step_by_save_weights)
pggan.build_model_PGGan()
start_time = datetime.datetime.now()
pggan.train()
end_time = datetime.datetime.now()
pggan.f_logger.write('start_time:{}'.format(str(start_time)) +
sample_path = "./output/{}/{}/sample_{}_{}".format(
FLAGS.OPER_NAME, 0, 7, t)
sample_path22 = "./output/{}/{}/Indiaviual_sample_{}_{}".format(
FLAGS.OPER_NAME, FLAGS.OPER_FLAG, 7, t)
mkdir_p(pggan_checkpoint_dir_write)
mkdir_p(sample_path)
mkdir_p(sample_path22)
pggan_checkpoint_dir_read = "./output/{}/model_pggan_{}/{}/".format(
FLAGS.OPER_NAME, FLAGS.OPER_FLAG, 7)
print(pggan_checkpoint_dir_read)
#pdb.set_trace()
pggan = PGGAN(batch_size=FLAGS.batch_size,
max_iters=1,
model_path=pggan_checkpoint_dir_write,
read_model_path=pggan_checkpoint_dir_read,
data=data_In,
sample_size=FLAGS.sample_size,
sample_path=sample_path,
sample_path2=sample_path22,
log_dir=root_log_dir,
learn_rate=FLAGS.learn_rate,
lam_gp=FLAGS.lam_gp,
lam_eps=FLAGS.lam_eps,
PG=7,
t=t,
use_wscale=FLAGS.use_wscale,
is_celeba=FLAGS.celeba)
pggan.build_model_PGGan()
pggan.test()
