def __init__(self):
    parser = argparse.ArgumentParser(description='Deep Encoding')
    parser.add_argument('--dataset', type=str, default='imagenet', help='training dataset (default: imagenet)')
    parser.add_argument('--base-size', type=int, default=None, help='base image size')
    parser.add_argument('--crop-size', type=int, default=224, help='crop image size')
    parser.add_argument('--label-smoothing', type=float, default=0.0, help='label-smoothing (default eta: 0.0)')
    parser.add_argument('--mixup', type=float, default=0.0, help='mixup (default eta: 0.0)')
    parser.add_argument('--auto-policy', type=str, default=None, help='path to auto augment policy')
    parser.add_argument('--data-dir', type=str, default=os.path.expanduser('~/.encoding/data'), help='data location for training')
    parser.add_argument('--arch', type=str, default='regnet', help='network type (default: regnet)')
    parser.add_argument('--config-file', type=str, required=True, help='network node config file')
    parser.add_argument('--last-gamma', action='store_true', default=False, help='whether to init gamma of the last BN layer in                             each bottleneck to 0 (default: False)')
    parser.add_argument('--amp', action='store_true', default=False, help='using amp')
    parser.add_argument('--batch-size', type=int, default=128, metavar='N', help='batch size for training (default: 128)')
    parser.add_argument('--test-batch-size', type=int, default=256, metavar='N', help='batch size for testing (default: 256)')
    parser.add_argument('--epochs', type=int, default=120, metavar='N', help='number of epochs to train (default: 600)')
    parser.add_argument('--start_epoch', type=int, default=0, metavar='N', help='the epoch number to start (default: 1)')
    parser.add_argument('--workers', type=int, default=8, metavar='N', help='dataloader threads')
    parser.add_argument('--lr', type=float, default=0.1, metavar='LR', help='learning rate (default: 0.1)')
    parser.add_argument('--lr-scheduler', type=str, default='cos', help='learning rate scheduler (default: cos)')
    parser.add_argument('--warmup-epochs', type=int, default=0, help='number of warmup epochs (default: 0)')
    parser.add_argument('--momentum', type=float, default=0.9, metavar='M', help='SGD momentum (default: 0.9)')
    parser.add_argument('--wd', type=float, default=0.0001, metavar='M', help='SGD weight decay (default: 1e-4)')
    parser.add_argument('--no-bn-wd', action='store_true', default=False, help='no bias decay')
    parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)')
    parser.add_argument('--resume', type=str, default=None, help='put the path to resuming file if needed')
    parser.add_argument('--checkname', type=str, default='default', help='set the checkpoint name')
    parser.add_argument('--world-size', default=1, type=int, help='number of nodes for distributed training')
    parser.add_argument('--rank', default=0, type=int, help='node rank for distributed training')
    parser.add_argument('--dist-url', default='tcp://localhost:23456', type=str, help='url used to set up distributed training')
    parser.add_argument('--dist-backend', default='nccl', type=str, help='distributed backend')
    parser.add_argument('--eval', action='store_true', default=False, help='evaluating')
    parser.add_argument('--export', type=str, default=None, help='put the path to resuming file if needed')
    self.parser = parser
def __init__(self):
    parser = argparse.ArgumentParser(description='Deep Encoding')
    parser.add_argument('--dataset', type=str, default='imagenet', help='training dataset (default: imagenet)')
    parser.add_argument('--base-size', type=int, default=None, help='base image size')
    parser.add_argument('--crop-size', type=int, default=224, help='crop image size')
    parser.add_argument('--label-smoothing', type=float, default=0.0, help='label-smoothing (default eta: 0.0)')
    parser.add_argument('--mixup', type=float, default=0.0, help='mixup (default eta: 0.0)')
    parser.add_argument('--auto-policy', type=str, default=None, help='path to auto augment policy')
    parser.add_argument('--data-dir', type=str, default=os.path.expanduser('~/.encoding/data'), help='data location for training')
    parser.add_argument('--arch', type=str, default='regnet', help='network type (default: regnet)')
    parser.add_argument('--config-file', type=str, required=True, help='network node config file')
    parser.add_argument('--last-gamma', action='store_true', default=False, help='whether to init gamma of the last BN layer in                             each bottleneck to 0 (default: False)')
    parser.add_argument('--amp', action='store_true', default=False, help='using amp')
    parser.add_argument('--batch-size', type=int, default=128, metavar='N', help='batch size for training (default: 128)')
    parser.add_argument('--test-batch-size', type=int, default=256, metavar='N', help='batch size for testing (default: 256)')
    parser.add_argument('--epochs', type=int, default=120, metavar='N', help='number of epochs to train (default: 600)')
    parser.add_argument('--start_epoch', type=int, default=0, metavar='N', help='the epoch number to start (default: 1)')
    parser.add_argument('--workers', type=int, default=8, metavar='N', help='dataloader threads')
    parser.add_argument('--lr', type=float, default=0.1, metavar='LR', help='learning rate (default: 0.1)')
    parser.add_argument('--lr-scheduler', type=str, default='cos', help='learning rate scheduler (default: cos)')
    parser.add_argument('--warmup-epochs', type=int, default=0, help='number of warmup epochs (default: 0)')
    parser.add_argument('--momentum', type=float, default=0.9, metavar='M', help='SGD momentum (default: 0.9)')
    parser.add_argument('--wd', type=float, default=0.0001, metavar='M', help='SGD weight decay (default: 1e-4)')
    parser.add_argument('--no-bn-wd', action='store_true', default=False, help='no bias decay')
    parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)')
    parser.add_argument('--resume', type=str, default=None, help='put the path to resuming file if needed')
    parser.add_argument('--checkname', type=str, default='default', help='set the checkpoint name')
    parser.add_argument('--world-size', default=1, type=int, help='number of nodes for distributed training')
    parser.add_argument('--rank', default=0, type=int, help='node rank for distributed training')
    parser.add_argument('--dist-url', default='tcp://localhost:23456', type=str, help='url used to set up distributed training')
    parser.add_argument('--dist-backend', default='nccl', type=str, help='distributed backend')
    parser.add_argument('--eval', action='store_true', default=False, help='evaluating')
    parser.add_argument('--export', type=str, default=None, help='put the path to resuming file if needed')
    self.parser = parser