def get_args_parser()

in references/classification/train_quantization.py [0:0]

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

def get_args_parser(add_help=True):
    import argparse

    parser = argparse.ArgumentParser(description="PyTorch Quantized Classification Training", add_help=add_help)

    parser.add_argument("--data-path", default="/datasets01/imagenet_full_size/061417/", type=str, help="dataset path")
    parser.add_argument("--model", default="mobilenet_v2", type=str, help="model name")
    parser.add_argument("--backend", default="qnnpack", type=str, help="fbgemm or qnnpack")
    parser.add_argument("--device", default="cuda", type=str, help="device (Use cuda or cpu Default: cuda)")

    parser.add_argument(
        "-b", "--batch-size", default=32, type=int, help="images per gpu, the total batch size is $NGPU x batch_size"
    )
    parser.add_argument("--eval-batch-size", default=128, type=int, help="batch size for evaluation")
    parser.add_argument("--epochs", default=90, type=int, metavar="N", help="number of total epochs to run")
    parser.add_argument(
        "--num-observer-update-epochs",
        default=4,
        type=int,
        metavar="N",
        help="number of total epochs to update observers",
    )
    parser.add_argument(
        "--num-batch-norm-update-epochs",
        default=3,
        type=int,
        metavar="N",
        help="number of total epochs to update batch norm stats",
    )
    parser.add_argument(
        "--num-calibration-batches",
        default=32,
        type=int,
        metavar="N",
        help="number of batches of training set for \
                              observer calibration ",
    )

    parser.add_argument(
        "-j", "--workers", default=16, type=int, metavar="N", help="number of data loading workers (default: 16)"
    )
    parser.add_argument("--lr", default=0.0001, type=float, help="initial learning rate")
    parser.add_argument("--momentum", default=0.9, type=float, metavar="M", help="momentum")
    parser.add_argument(
        "--wd",
        "--weight-decay",
        default=1e-4,
        type=float,
        metavar="W",
        help="weight decay (default: 1e-4)",
        dest="weight_decay",
    )
    parser.add_argument("--lr-step-size", default=30, type=int, help="decrease lr every step-size epochs")
    parser.add_argument("--lr-gamma", default=0.1, type=float, help="decrease lr by a factor of lr-gamma")
    parser.add_argument("--print-freq", default=10, type=int, help="print frequency")
    parser.add_argument("--output-dir", default=".", type=str, help="path to save outputs")
    parser.add_argument("--resume", default="", type=str, help="path of checkpoint")
    parser.add_argument("--start-epoch", default=0, type=int, metavar="N", help="start epoch")
    parser.add_argument(
        "--cache-dataset",
        dest="cache_dataset",
        help="Cache the datasets for quicker initialization. \
             It also serializes the transforms",
        action="store_true",
    )
    parser.add_argument(
        "--sync-bn",
        dest="sync_bn",
        help="Use sync batch norm",
        action="store_true",
    )
    parser.add_argument(
        "--test-only",
        dest="test_only",
        help="Only test the model",
        action="store_true",
    )
    parser.add_argument(
        "--post-training-quantize",
        dest="post_training_quantize",
        help="Post training quantize the model",
        action="store_true",
    )

    # distributed training parameters
    parser.add_argument("--world-size", default=1, type=int, help="number of distributed processes")
    parser.add_argument("--dist-url", default="env://", type=str, help="url used to set up distributed training")

    parser.add_argument(
        "--interpolation", default="bilinear", type=str, help="the interpolation method (default: bilinear)"
    )
    parser.add_argument(
        "--val-resize-size", default=256, type=int, help="the resize size used for validation (default: 256)"
    )
    parser.add_argument(
        "--val-crop-size", default=224, type=int, help="the central crop size used for validation (default: 224)"
    )
    parser.add_argument(
        "--train-crop-size", default=224, type=int, help="the random crop size used for training (default: 224)"
    )
    parser.add_argument("--clip-grad-norm", default=None, type=float, help="the maximum gradient norm (default None)")

    # Prototype models only
    parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load")

    return parser