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added deepsad base code

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This commit is contained in:
Jan Kowalczyk
2024-06-28 07:42:12 +02:00
parent 2eb1bf2e05
commit 914bb020d0
57 changed files with 4974 additions and 0 deletions
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54
Deep-SAD-PyTorch/src/datasets/main.py Normal file
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from .mnist import MNIST_Dataset
from .fmnist import FashionMNIST_Dataset
from .cifar10 import CIFAR10_Dataset
from .odds import ODDSADDataset
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):
"""Loads the dataset."""
implemented_datasets = ('mnist', 'fmnist', 'cifar10',
'arrhythmia', 'cardio', 'satellite', 'satimage-2', 'shuttle', 'thyroid')
assert dataset_name in implemented_datasets
dataset = None
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