main.py

from UGATIT import UGATIT
import argparse
from utils import *
import matplotlib.pyplot as plt

#start
"""parsing and configuration"""

def parse_args():
    desc = "Pytorch implementation of U-GAT-IT"
    parser = argparse.ArgumentParser(description=desc)
    parser.add_argument('--phase', type=str, default='train', help='[train / test]')
    parser.add_argument('--light', type=str2bool, default=False, help='[U-GAT-IT full version / U-GAT-IT light version]')
    parser.add_argument('--dataset', type=str, default='YOUR_DATASET_NAME', help='dataset_name')

    parser.add_argument('--show_real', type=str2bool, default=True, help='show real/fake or fake only when test phase')
    parser.add_argument('--metric_freq', type=int, default=10000, help='The number of metric evaluate freq')
    parser.add_argument('--strength', type=float, default=0.0, help='Initial augmentation strength')
    parser.add_argument('--kimg', type=int, default=500, help='K img to reach unit rt')
    parser.add_argument('--group', type=str, default='0,1,2,3', help='Groups you want to train')
    parser.add_argument('--neptune', type=str2bool, default=False, help='Send metrics to neptune')
    parser.add_argument('--useADA', type=str2bool, default=True, help='Use ADA')
    parser.add_argument('--use_grouploss', type=str2bool, default=True, help='Use group loss')

    parser.add_argument('--iteration', type=int, default=1000000, help='The number of training iterations')
    parser.add_argument('--batch_size', type=int, default=1, help='The size of batch size')
    parser.add_argument('--print_freq', type=int, default=1000, help='The number of image print freq')
    parser.add_argument('--save_freq', type=int, default=100000, help='The number of model save freq')
    parser.add_argument('--decay_flag', type=str2bool, default=True, help='The decay_flag')

    parser.add_argument('--lr', type=float, default=0.0001, help='The learning rate')
    parser.add_argument('--weight_decay', type=float, default=0.0001, help='The weight decay')
    parser.add_argument('--adv_weight', type=int, default=1, help='Weight for GAN')
    parser.add_argument('--cycle_weight', type=int, default=10, help='Weight for Cycle')
    parser.add_argument('--identity_weight', type=int, default=10, help='Weight for Identity')
    parser.add_argument('--cam_weight', type=int, default=1000, help='Weight for CAM')
    parser.add_argument('--group_weight', type=float, default=1.0, help='Weight for group')

    parser.add_argument('--ch', type=int, default=64, help='base channel number per layer')
    parser.add_argument('--n_res', type=int, default=4, help='The number of resblock')
    parser.add_argument('--n_dis', type=int, default=6, help='The number of discriminator layer')

    parser.add_argument('--img_size', type=int, default=256, help='The size of image')
    parser.add_argument('--img_ch', type=int, default=3, help='The size of image channel')

    parser.add_argument('--result_dir', type=str, default='results', help='Directory name to save the results')
    parser.add_argument('--device', type=str, default='cuda', choices=['cpu', 'cuda'], help='Set gpu mode; [cpu, cuda]')
    parser.add_argument('--benchmark_flag', type=str2bool, default=False)
    parser.add_argument('--resume', type=str2bool, default=False)

    return check_args(parser.parse_args())

"""checking arguments"""
def check_args(args):
    # --result_dir
    check_folder(os.path.join(args.result_dir, args.dataset, 'model'))
    check_folder(os.path.join(args.result_dir, args.dataset, 'img'))
    check_folder(os.path.join(args.result_dir, args.dataset, 'test'))

    # --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')
    return args

"""main"""
def main():
    # parse arguments
    args = parse_args()
    if args is None:
      exit()

    # open session
    gan = UGATIT(args)

    # build graph
    gan.build_model()


    if args.phase == 'train' :
        gan.train()
        print(" [*] Training finished!")
        plt.plot(gan.logit_list_real, label="real_logit", alpha=0.3)
        plt.plot(gan.logit_list_fake, label="fake_logit", alpha=0.3)
        plt.legend()
        plt.show()

    if args.phase == 'test':
        gan.test(show_real=args.show_real)
        print(" [*] Test finished!")


if __name__ == '__main__':
    main()