mt/Deep-SAD-PyTorch/src/datasets/main.py

from .cifar10 import CIFAR10_Dataset
from .elpv import ELPV_Dataset
from .fmnist import FashionMNIST_Dataset
from .mnist import MNIST_Dataset
from .odds import ODDSADDataset
from .subter import SubTer_Dataset
from .subtersplit import SubTerSplit_Dataset


def load_dataset(
    dataset_name,
    data_path,
    normal_class,
    known_outlier_class,
    n_known_outlier_classes: int = 0,
    ratio_known_normal: float = 0.0,
    ratio_known_outlier: float = 0.0,
    ratio_pollution: float = 0.0,
    random_state=None,
    inference: bool = False,
    k_fold: bool = False,
    num_known_normal: int = 0,
    num_known_outlier: int = 0,
):
    """Loads the dataset."""

    implemented_datasets = (
        "mnist",
        "elpv",
        "subter",
        "subtersplit",
        "fmnist",
        "cifar10",
        "arrhythmia",
        "cardio",
        "satellite",
        "satimage-2",
        "shuttle",
        "thyroid",
    )
    assert dataset_name in implemented_datasets

    dataset = None

    if dataset_name == "subter":
        dataset = SubTer_Dataset(
            root=data_path,
            ratio_known_normal=ratio_known_normal,
            ratio_known_outlier=ratio_known_outlier,
            ratio_pollution=ratio_pollution,
            inference=inference,
            k_fold=k_fold,
            num_known_normal=num_known_normal,
            num_known_outlier=num_known_outlier,
        )

    if dataset_name == "subtersplit":
        dataset = SubTerSplit_Dataset(
            root=data_path,
            ratio_known_normal=ratio_known_normal,
            ratio_known_outlier=ratio_known_outlier,
            ratio_pollution=ratio_pollution,
            inference=inference,
        )

    if dataset_name == "elpv":
        dataset = ELPV_Dataset(
            root=data_path,
            ratio_known_normal=ratio_known_normal,
            ratio_known_outlier=ratio_known_outlier,
            ratio_pollution=ratio_pollution,
        )

    if dataset_name == "mnist":
        dataset = MNIST_Dataset(
            root=data_path,
            normal_class=normal_class,
            known_outlier_class=known_outlier_class,
            n_known_outlier_classes=n_known_outlier_classes,
            ratio_known_normal=ratio_known_normal,
            ratio_known_outlier=ratio_known_outlier,
            ratio_pollution=ratio_pollution,
        )

    if dataset_name == "fmnist":
        dataset = FashionMNIST_Dataset(
            root=data_path,
            normal_class=normal_class,
            known_outlier_class=known_outlier_class,
            n_known_outlier_classes=n_known_outlier_classes,
            ratio_known_normal=ratio_known_normal,
            ratio_known_outlier=ratio_known_outlier,
            ratio_pollution=ratio_pollution,
        )

    if dataset_name == "cifar10":
        dataset = CIFAR10_Dataset(
            root=data_path,
            normal_class=normal_class,
            known_outlier_class=known_outlier_class,
            n_known_outlier_classes=n_known_outlier_classes,
            ratio_known_normal=ratio_known_normal,
            ratio_known_outlier=ratio_known_outlier,
            ratio_pollution=ratio_pollution,
        )

    if dataset_name in (
        "arrhythmia",
        "cardio",
        "satellite",
        "satimage-2",
        "shuttle",
        "thyroid",
    ):
        dataset = ODDSADDataset(
            root=data_path,
            dataset_name=dataset_name,
            n_known_outlier_classes=n_known_outlier_classes,
            ratio_known_normal=ratio_known_normal,
            ratio_known_outlier=ratio_known_outlier,
            ratio_pollution=ratio_pollution,
            random_state=random_state,
        )

    return dataset