def arg_parser()

in image_classification/infer_imagenet_gpu.py [0:0]

• 63 lines of code
• 1 McCabe index (conditional complexity)

def arg_parser():
    """Parses command line arguments"""
    parser = argparse.ArgumentParser(description='Train a model for image classification.')
    parser.add_argument('--data-dir', type=str, default='~/.mxnet/datasets/imagenet',
                        help='training and validation pictures to use.')
    parser.add_argument('--rec-val', type=str, default='/media/ramdisk/data/val-passthrough.rec',
                        help='the validation data')
    parser.add_argument('--rec-val-idx', type=str, default='/media/ramdisk/data/val-passthrough.idx',
                        help='the index of validation data')
    parser.add_argument('--use-rec', action='store_true',
                        help='use image record iter for data input. default is false.')
    parser.add_argument('--batch-size', type=int, default=32,
                        help='training batch size per device (CPU/GPU).')
    parser.add_argument('--dataset-size', type=int, default=32,
                        help='training batch size per device (CPU/GPU).')
    parser.add_argument('--dtype', type=str, default='float32',
                        help='data type for training. default is float32')
    parser.add_argument('--num_gpus', type=str, default=0,
                        help='number of gpus to use.')
    parser.add_argument('-j', '--num-data-workers', dest='num_workers', default=4, type=int,
                        help='number of preprocessing workers')
    parser.add_argument('--num-epochs', type=int, default=3,
                        help='number of training epochs.')
    parser.add_argument('--lr', type=float, default=0.1,
                        help='learning rate. default is 0.1.')
    parser.add_argument('--momentum', type=float, default=0.9,
                        help='momentum value for optimizer, default is 0.9.')
    parser.add_argument('--wd', type=float, default=0.0001,
                        help='weight decay rate. default is 0.0001.')
    parser.add_argument('--lr-mode', type=str, default='step',
                        help='learning rate scheduler mode. options are step, poly.')
    parser.add_argument('--lr-poly-power', type=int, default=2,
                        help='if learning rate scheduler mode is poly, then power is used')
    parser.add_argument('--lr-decay', type=float, default=0.1,
                        help='decay rate of learning rate. default is 0.1.')
    parser.add_argument('--lr-decay-epoch', type=str, default='40,60',
                        help='epoches at which learning rate decays. default is 40,60.')
    parser.add_argument('--warmup-lr', type=float, default=0.0,
                        help='starting warmup learning rate. default is 0.0.')
    parser.add_argument('--warmup-epochs', type=int, default=0,
                        help='number of warmup epochs.')
    parser.add_argument('--last-gamma', action='store_true',
                        help='whether to initialize the gamma of the last BN layer in each bottleneck to zero')
    parser.add_argument('--mode', type=str,
                        help='mode in which to train the model. options are symbolic, imperative, hybrid')
    parser.add_argument('--model', type=str, required=True,
                        help='type of model to use. see vision_model for options.')
    parser.add_argument('--use-pretrained', action='store_true',
                        help='enable using pretrained model from gluon.')
    parser.add_argument('--use_se', action='store_true',
                        help='use SE layers or not in resnext. default is false.')
    parser.add_argument('--batch-norm', action='store_true',
                        help='enable batch normalization or not in vgg. default is false.')
    parser.add_argument('--log-interval', type=int, default=50,
                        help='Number of batches to wait before logging.')
    parser.add_argument('--save-frequency', type=int, default=0,
                        help='frequency of model saving.')
    parser.add_argument('--save-dir', type=str, default='params',
                        help='directory of saved models')
    parser.add_argument('--logging-dir', type=str, default='logs',
                        help='directory of training logs')
    parser.add_argument('--kvstore', type=str, default='nccl')
    parser.add_argument('--top-k', type=int, default=0, help='give 5 for top5 accuracy, if 0 only prints top1 accuracy')
    return parser.parse_args()