def add_parser_arguments()

in sdk/python/jobs/pipelines/2d_image_classification_with_densenet/imagecnn_train/main.py [0:0]

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

def add_parser_arguments(parser):
    model_names = models.resnet_versions.keys()
    model_configs = models.resnet_configs.keys()

    parser.add_argument("--data", metavar="DIR", help="path to dataset")
    parser.add_argument(
        "--data-backend",
        metavar="BACKEND",
        default="dali-cpu",
        choices=DATA_BACKEND_CHOICES,
        help="data backend: "
        + " | ".join(DATA_BACKEND_CHOICES)
        + " (default: dali-cpu)",
    )

    parser.add_argument(
        "--arch",
        "-a",
        metavar="ARCH",
        default="resnet50",
        choices=model_names,
        help="model architecture: " + " | ".join(model_names) + " (default: resnet50)",
    )

    parser.add_argument(
        "--model-config",
        "-c",
        metavar="CONF",
        default="classic",
        choices=model_configs,
        help="model configs: " + " | ".join(model_configs) + "(default: classic)",
    )

    parser.add_argument(
        "-j",
        "--workers",
        default=5,
        type=int,
        metavar="N",
        help="number of data loading workers (default: 5)",
    )
    parser.add_argument(
        "--epochs",
        default=90,
        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) per gpu",
    )

    parser.add_argument(
        "--optimizer-batch-size",
        default=-1,
        type=int,
        metavar="N",
        help="size of a total batch size, for simulating bigger batches using gradient accumulation",
    )

    parser.add_argument(
        "--lr",
        "--learning-rate",
        default=0.1,
        type=float,
        metavar="LR",
        help="initial learning rate",
    )
    parser.add_argument(
        "--lr-schedule",
        default="step",
        type=str,
        metavar="SCHEDULE",
        choices=["step", "linear", "cosine"],
        help="Type of LR schedule: {}, {}, {}".format("step", "linear", "cosine"),
    )

    parser.add_argument(
        "--warmup", default=0, type=int, metavar="E", help="number of warmup epochs"
    )

    parser.add_argument(
        "--label-smoothing",
        default=0.0,
        type=float,
        metavar="S",
        help="label smoothing",
    )
    parser.add_argument(
        "--mixup", default=0.0, type=float, metavar="ALPHA", help="mixup alpha"
    )

    parser.add_argument(
        "--momentum", default=0.9, type=float, metavar="M", help="momentum"
    )
    parser.add_argument(
        "--weight-decay",
        "--wd",
        default=1e-4,
        type=float,
        metavar="W",
        help="weight decay (default: 1e-4)",
    )
    parser.add_argument(
        "--bn-weight-decay",
        action="store_true",
        help="use weight_decay on batch normalization learnable parameters, (default: false)",
    )
    parser.add_argument(
        "--nesterov",
        action="store_true",
        help="use nesterov momentum, (default: false)",
    )

    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(
        "--pretrained-weights",
        default="",
        type=str,
        metavar="PATH",
        help="load weights from here",
    )

    parser.add_argument("--fp16", action="store_true", help="Run model fp16 mode.")
    parser.add_argument(
        "--static-loss-scale",
        type=float,
        default=1,
        help="Static loss scale, positive power of 2 values can improve fp16 convergence.",
    )
    parser.add_argument(
        "--dynamic-loss-scale",
        action="store_true",
        help="Use dynamic loss scaling.  If supplied, this argument supersedes "
        + "--static-loss-scale.",
    )
    parser.add_argument(
        "--prof", type=int, default=-1, metavar="N", help="Run only N iterations"
    )
    parser.add_argument(
        "--amp",
        action="store_true",
        help="Run model AMP (automatic mixed precision) mode.",
    )

    parser.add_argument(
        "--seed", default=None, type=int, help="random seed used for numpy and pytorch"
    )

    parser.add_argument(
        "--gather-checkpoints",
        action="store_true",
        help="Gather checkpoints throughout the training, without this flag only best and last checkpoints will be stored",
    )

    parser.add_argument(
        "--raport-file",
        default="experiment_raport.json",
        type=str,
        help="file in which to store JSON experiment raport",
    )

    parser.add_argument(
        "--evaluate", action="store_true", help="evaluate checkpoint/model"
    )
    parser.add_argument("--training-only", action="store_true", help="do not evaluate")

    parser.add_argument(
        "--no-checkpoints",
        action="store_false",
        dest="save_checkpoints",
        help="do not store any checkpoints, useful for benchmarking",
    )

    parser.add_argument(
        "--workspace",
        type=str,
        default="./",
        metavar="DIR",
        help="path to directory where checkpoints will be stored",
    )

    parser.add_argument(
        "--save-checkpoint-epochs",
        default=10,
        type=int,
        metavar="N",
        help="how many epochs run between saving checkpoints",
    )

    parser.add_argument(
        "--log_redirect", action="store_true", help="Redirect log to files."
    )