image_classification/infer_imagenet.py [11:25]:
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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')
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image_classification/infer_imagenet_gpu.py [14:28]:
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    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')
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