def console_entry_point()

in basic_pitch/train.py [0:0]

• 110 lines of code
• 6 McCabe index (conditional complexity)

def console_entry_point() -> None:
    """From pip installed script."""
    parser = argparse.ArgumentParser(description="")
    parser.add_argument("--source", required=True, help="Path to directory containing train/validation splits.")
    parser.add_argument("--output", required=True, help="Directory to save the model in.")
    parser.add_argument("-e", "--epochs", type=int, default=500, help="Number of training epochs.")
    parser.add_argument(
        "-b",
        "--batch-size",
        type=int,
        default=16,
        help="batch size of training. Unlike Estimator API, this specifies the batch size per-GPU.",
    )
    parser.add_argument(
        "-l",
        "--learning-rate",
        type=float,
        default=0.001,
        help="ADAM optimizer learning rate",
    )
    parser.add_argument(
        "-s",
        "--steps-per-epoch",
        type=int,
        default=100,
        help="steps_per_epoch (batch) of each training loop",
    )
    parser.add_argument(
        "-v",
        "--validation-steps",
        type=int,
        default=10,
        help="validation steps (number of BATCHES) for each validation run. MUST be a positive integer",
    )
    parser.add_argument(
        "-z",
        "--training-shuffle-size",
        type=int,
        default=100,
        help="training dataset shuffle size",
    )
    parser.add_argument(
        "--size-evaluation-callback-datasets",
        type=int,
        default=4,
        help="number of elements in the dataset used by the evaluation callback function",
    )
    for dataset in DATASET_SAMPLING_FREQUENCY.keys():
        parser.add_argument(
            f"--{dataset.lower()}",
            action="store_true",
            default=False,
            help=f"Use {dataset} dataset in training",
        )
    parser.add_argument(
        "--no-sonify",
        action="store_true",
        default=False,
        help="if given, exclude sonifications from the tensorboard / data visualization",
    )
    parser.add_argument(
        "--no-contours",
        action="store_true",
        default=False,
        help="if given, trains without supervising the contour layer",
    )
    parser.add_argument(
        "--weighted-onset-loss",
        action="store_true",
        default=False,
        help="if given, trains onsets with a class-weighted loss",
    )
    parser.add_argument(
        "--positive-onset-weight",
        type=float,
        default=0.5,
        help="Positive class onset weight. Only applies when weignted onset loss is true.",
    )

    args = parser.parse_args()
    datasets_to_use = [
        dataset.lower()
        for dataset in DATASET_SAMPLING_FREQUENCY.keys()
        if getattr(args, dataset.lower().replace("-", "_"))
    ]
    dataset_sampling_frequency = np.array(
        [
            frequency
            for dataset, frequency in DATASET_SAMPLING_FREQUENCY.items()
            if getattr(args, dataset.lower().replace("-", "_"))
        ]
    )
    dataset_sampling_frequency = dataset_sampling_frequency / np.sum(dataset_sampling_frequency)

    assert args.steps_per_epoch is not None
    assert args.validation_steps > 0

    main(
        args.source,
        args.output,
        args.training_shuffle_size,
        args.batch_size,
        args.learning_rate,
        args.epochs,
        args.steps_per_epoch,
        args.validation_steps,
        args.size_evaluation_callback_datasets,
        datasets_to_use,
        dataset_sampling_frequency,
        args.dont_sonify,
        args.no_contours,
        args.weighted_onset_loss,
        args.positive_onset_weight,
    )