SARGAN/driver.py at main · arbishakram/SARGAN · GitHub

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import os
import argparse
from solver import Solver
from data_loader import get_loader
from torch.backends import cudnn


def str2bool(v):
    return v.lower() in ('true')

if __name__ == '__main__':
    parser = argparse.ArgumentParser()

    parser.add_argument('--c_dim', type=int, default=7, help='dimension of expressions')
    parser.add_argument('--image_size', type=int, default=128, help='image resolution')
    parser.add_argument('--g_conv_dim', type=int, default=64, help='number of conv filters in the first layer of G')
    parser.add_argument('--d_conv_dim', type=int, default=64, help='number of conv filters in the first layer of D')
    parser.add_argument('--d_repeat_num', type=int, default=6, help='number of strided conv layers in D')
    parser.add_argument('--lambda_cls', type=float, default=1, help='weight for domain classification loss')
    parser.add_argument('--lambda_rec', type=float, default=1, help='weight for reconstruction loss')
    parser.add_argument('--lambda_gp', type=float, default=10, help='weight for gradient penalty')
    parser.add_argument('--batch_size', type=int, default=1, help='mini-batch size')
    parser.add_argument('--num_iters', type=int, default=200000, help='number of total iterations for training D')
    parser.add_argument('--num_iters_decay', type=int, default=100000, help='number of iterations for decaying lr')
    parser.add_argument('--g_lr', type=float, default=0.0001, help='learning rate for G')
    parser.add_argument('--d_lr', type=float, default=0.0001, help='learning rate for D')
    parser.add_argument('--n_critic', type=int, default=5, help='number of D updates per each G update')
    parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for Adam optimizer')
    parser.add_argument('--beta2', type=float, default=0.999, help='beta2 for Adam optimizer')
    parser.add_argument('--resume_iters', type=int, default=None, help='resume training from this step')
    parser.add_argument('--test_iters', type=int, default=None, help='test model from this step')
    parser.add_argument('--num_workers', type=int, default=1)
    parser.add_argument('--mode', type=str, default='test', choices=['train', 'test'])
    parser.add_argument('--use_tensorboard', type=str2bool, default=False)
    parser.add_argument('--image_dir', type=str, default='./testing_imgs/')
    parser.add_argument('--log_dir', type=str, default='sargan/logs')
    parser.add_argument('--model_save_dir', type=str, default='pre-trained_model/')
    parser.add_argument('--sample_dir', type=str, default='sargan/samples')
    parser.add_argument('--result_dir', type=str, default='sargan/results')

    # Step size.
    parser.add_argument('--log_step', type=int, default=10)
    parser.add_argument('--sample_step', type=int, default=100)
    parser.add_argument('--model_save_step', type=int, default=1000)
    parser.add_argument('--lr_update_step', type=int, default=5000)

    config = parser.parse_args()
    print(config)


def main(config):
    cudnn.benchmark = True

    if not os.path.exists(config.log_dir):
        os.makedirs(config.log_dir)
    if not os.path.exists(config.model_save_dir):
        os.makedirs(config.model_save_dir)
    if not os.path.exists(config.sample_dir):
        os.makedirs(config.sample_dir)
    if not os.path.exists(config.result_dir):
        os.makedirs(config.result_dir)

    dataset_loader = get_loader(config.image_dir, config.image_size, config.batch_size,
                                 config.mode, config.num_workers)
    solver = Solver(dataset_loader, config)

    if config.mode == 'train':
            solver.train()
    elif config.mode == 'test':
            solver.test()

main(config)