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main.py
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main.py
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# -*- coding: utf-8 -*-
import os
## GAN Variants
from TGAN_64 import TGAN_64
from TGAN_128 import TGAN_128
from utils import show_all_variables
from utils import check_folder
import tensorflow as tf
import argparse
"""parsing and configuration"""
def parse_args():
desc = "Tensorflow implementation of GAN collections"
parser = argparse.ArgumentParser(description=desc)
parser.add_argument('--gan_type', type=str, default='TGAN_64',choices=['TGAN_64', 'TGAN_128'],
help='The type of GAN', required=True)
parser.add_argument('--dataset', type=str, default='celebA', choices=['celebA'],
help='The name of dataset')
parser.add_argument('--epoch', type=int, default=50, help='The number of epochs')
parser.add_argument('--batch_size', type=int, default=64, help='The size of batch')
parser.add_argument('--z_dim', type=int, default=100, help='Dimension of noise vector')
parser.add_argument('--loss_type', type=str, default='sgan', choices=['sgan', 'wgan', 'lsgan'], help='GAN loss')
parser.add_argument('--checkpoint_dir', type=str, default='checkpoint',
help='Directory name to save the checkpoints')
parser.add_argument('--result_dir', type=str, default='results',
help='Directory name to save the generated images')
parser.add_argument('--log_dir', type=str, default='logs',
help='Directory name to save training logs')
parser.add_argument('--test_dir', type=str, default='tests',
help='Directory name to save the generated images')
return check_args(parser.parse_args())
"""checking arguments"""
def check_args(args):
# --checkpoint_dir
check_folder(args.checkpoint_dir)
# --result_dir
check_folder(args.result_dir)
# --test_dir
check_folder(args.test_dir)
# --result_dir
check_folder(args.log_dir)
# --epoch
try:
assert args.epoch >= 1
except:
print('number of epochs must be larger than or equal to one')
# --batch_size
try:
assert args.batch_size >= 1
except:
print('batch size must be larger than or equal to one')
# --z_dim
try:
assert args.z_dim >= 1
except:
print('dimension of noise vector must be larger than or equal to one')
return args
"""main"""
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 == 'TGAN_64':
gan = TGAN_64(sess, args)
elif args.gan_type == 'TGAN_128':
gan = TGAN_128(sess, args)
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()
gan.train_check()
if __name__ == '__main__':
main()