main.py

import argparse
import os
import torch
from WiggleGAN import WiggleGAN
#from MyACGAN import MyACGAN
#from MyGAN import MyGAN

"""parsing and configuration"""


def parse_args():
    desc = "Pytorch implementation of GAN collections"
    parser = argparse.ArgumentParser(description=desc)

    parser.add_argument('--gan_type', type=str, default='WiggleGAN',
                        choices=['MyACGAN', 'MyGAN', 'WiggleGAN'],
                        help='The type of GAN')
    parser.add_argument('--dataset', type=str, default='4cam',
                        choices=['mnist', 'fashion-mnist', 'cifar10', 'cifar100', 'svhn', 'stl10', 'lsun-bed', '4cam'],
                        help='The name of dataset')
    parser.add_argument('--split', type=str, default='', help='The split flag for svhn and stl10')
    parser.add_argument('--epoch', type=int, default=50, help='The number of epochs to run')
    parser.add_argument('--batch_size', type=int, default=16, help='The size of batch')
    parser.add_argument('--input_size', type=int, default=10, help='The size of input image')
    parser.add_argument('--save_dir', type=str, default='models',
                        help='Directory name to save the model')
    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('--lrG', type=float, default=0.0002)
    parser.add_argument('--lrD', type=float, default=0.001)
    parser.add_argument('--beta1', type=float, default=0.5)
    parser.add_argument('--beta2', type=float, default=0.999)
    parser.add_argument('--gpu_mode', type=str2bool, default=True)
    parser.add_argument('--benchmark_mode', type=str2bool, default=True)
    parser.add_argument('--cameras', type=int, default=2)
    parser.add_argument('--imageDim', type=int, default=128)
    parser.add_argument('--epochV', type=int, default=0)
    parser.add_argument('--cIm', type=int, default=4)
    parser.add_argument('--seedLoad', type=str, default="-0000")
    parser.add_argument('--zGF', type=float, default=0.2)
    parser.add_argument('--zDF', type=float, default=0.2)
    parser.add_argument('--bF', type=float, default=0.2)
    parser.add_argument('--expandGen', type=int, default=3)
    parser.add_argument('--expandDis', type=int, default=3)
    parser.add_argument('--wiggleDepth', type=int, default=-1)
    parser.add_argument('--visdom', type=str2bool, default=True)
    parser.add_argument('--lambdaL1', type=int, default=100)
    parser.add_argument('--clipping', type=float, default=-1)
    parser.add_argument('--depth', type=str2bool, default=True)
    parser.add_argument('--recreate', type=str2bool, default=False)
    parser.add_argument('--name_wiggle', type=str, default='wiggle-result')

    return check_args(parser.parse_args())


"""checking arguments"""

def str2bool(v):
    if isinstance(v, bool):
       return v
    if v.lower() in ('yes', 'true', 't', 'y', '1'):
        return True
    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
        return False
    else:
        raise argparse.ArgumentTypeError('Boolean value expected.')


def check_args(args):
    # --save_dir
    if not os.path.exists(args.save_dir):
        os.makedirs(args.save_dir)

    # --result_dir
    if not os.path.exists(args.result_dir):
        os.makedirs(args.result_dir)

    # --result_dir
    if not os.path.exists(args.log_dir):
        os.makedirs(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')

    return args


"""main"""


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 == 'WiggleGAN':
        gan = WiggleGAN(args)
    #elif args.gan_type == 'MyACGAN':
    #    gan = MyACGAN(args)
    #elif args.gan_type == 'MyGAN':
    #    gan = MyGAN(args)
    else:
        raise Exception("[!] There is no option for " + args.gan_type)

    # launch the graph in a session
    if (args.wiggleDepth < 0 and not args.recreate):
        print(" [*] Training Starting!")
        gan.train()
        print(" [*] Training finished!")
    else:
      if not args.recreate:
            print(" [*] Wiggle Started!")
            gan.wiggleEf()
            print(" [*] Wiggle finished!")
      else:
            print(" [*] Dataset recreation Started")
            gan.recreate()
            print(" [*] Dataset recreation finished")


if __name__ == '__main__':
    main()