research/active_learning/archive/filebased_main.py [26:67]:
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model_names = sorted(name for name in models.__dict__
    if name.islower() and not name.startswith("__")
    and callable(models.__dict__[name]))

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('--train_data', metavar='DIR',
                    help='path to train dataset', default='../../crops_train')
parser.add_argument('--val_data', metavar='DIR',
                    help='path to validation dataset', default=None)
parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet18',
                    choices=model_names,
                    help='model architecture: ' +
                        ' | '.join(model_names) +
                        ' (default: resnet18)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=20, type=int, metavar='N',
                    help='number of total epochs to run')
parser.add_argument('-b', '--batch_size', default=256, type=int,
                    metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--lr', '--learning_rate', default=0.001, type=float,
                    metavar='LR', help='initial learning rate')
parser.add_argument('--weight_decay', '--wd', default=5e-4, type=float,
                    metavar='W', help='weight decay (default: 5e-4)')
parser.add_argument('--print_freq', '-p', default=10, type=int,
                    metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')

parser.add_argument('--checkpoint_prefix', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')

parser.add_argument('--plot_freq', dest='plot_freq', type= int, action='store',
                    help='plot embedding frequence', default=1)
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                    help='use pre-trained model')
parser.add_argument('--seed', default=None, type=int,
                    help='seed for initializing training. ')
parser.add_argument('--loss_type', default='triplet', 
                    help='Loss Type')
parser.add_argument('--margin', default=1.0, type=float, metavar='M',
                    help='margin for siamese or triplet loss')
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research/active_learning/main.py [27:68]:
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model_names = sorted(name for name in models.__dict__
    if name.islower() and not name.startswith("__")
    and callable(models.__dict__[name]))

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('--train_data', metavar='DIR',
                    help='path to train dataset', default='../../crops_train')
parser.add_argument('--val_data', metavar='DIR',
                    help='path to validation dataset', default=None)
parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet18',
                    choices=model_names,
                    help='model architecture: ' +
                        ' | '.join(model_names) +
                        ' (default: resnet18)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=20, type=int, metavar='N',
                    help='number of total epochs to run')
parser.add_argument('-b', '--batch_size', default=256, type=int,
                    metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--lr', '--learning_rate', default=0.001, type=float,
                    metavar='LR', help='initial learning rate')
parser.add_argument('--weight_decay', '--wd', default=5e-4, type=float,
                    metavar='W', help='weight decay (default: 5e-4)')
parser.add_argument('--print_freq', '-p', default=10, type=int,
                    metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')

parser.add_argument('--checkpoint_prefix', default='', type=str, metavar='PATH',
                    help='path to latest checkpoint (default: none)')

parser.add_argument('--plot_freq', dest='plot_freq', type= int, action='store',
                    help='plot embedding frequence', default=1)
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                    help='use pre-trained model')
parser.add_argument('--seed', default=None, type=int,
                    help='seed for initializing training. ')
parser.add_argument('--loss_type', default='triplet', 
                    help='Loss Type')
parser.add_argument('--margin', default=1.0, type=float, metavar='M',
                    help='margin for siamese or triplet loss')
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