ninco ssb clean benchmark

src/detectors/data/ninco_ssb_clean.py

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+import os
+from typing import Callable, Dict, List, Optional, Tuple
+
+from torchvision.datasets import ImageFolder
+from torchvision.datasets.utils import check_integrity, download_and_extract_archive
+
+PAPER_URL = "https://arxiv.org/pdf/2306.00826.pdf"
+
+
+class NINCOFull(ImageFolder):
+    """`NINCO` Dataset subset.
+
+    Args:
+        root (string): Root directory of dataset where directory
+            exists or will be saved to if download is set to True.
+        split (string, optional): The dataset split, not used.
+        transform (callable, optional): A function/transform that takes in an PIL image
+            and returns a transformed version. E.g, `transforms.RandomCrop`.
+        download (bool, optional): If true, downloads the dataset from the internet and
+            puts it in root directory. If dataset is already downloaded, it is not
+            downloaded again.
+        **kwargs: Additional arguments passed to :class:`~torchvision.datasets.ImageFolder`.
+    """
+
+    base_folder = "ninco"
+    filename = "NINCO_all.tar.gz"
+    file_md5 = "b9ffae324363cd900a81ce3c367cd834"
+    url = "https://zenodo.org/record/8013288/files/NINCO_all.tar.gz"
+    # size: 15393
+
+    def __init__(
+        self, root: str, split=None, transform: Optional[Callable] = None, download: bool = False, **kwargs
+    ) -> None:
+        self.root = os.path.expanduser(root)
+        self.dataset_folder = os.path.join(self.root, self.base_folder)
+        self.archive = os.path.join(self.root, self.filename)
+
+        if download:
+            self.download()
+
+        if not self._check_integrity():
+            raise RuntimeError("Dataset not found or corrupted." + " You can use download=True to download it")
+
+        super().__init__(self.dataset_folder, transform=transform, **kwargs)
+
+    def _check_integrity(self) -> bool:
+        return check_integrity(self.archive, self.file_md5)
+
+    def _check_exists(self) -> bool:
+        return os.path.exists(self.dataset_folder)
+
+    def download(self) -> None:
+        if self._check_integrity() and self._check_exists():
+            return
+        download_and_extract_archive(
+            self.url, download_root=self.root, extract_root=self.dataset_folder, md5=self.file_md5
+        )
+
+
+class NINCO(NINCOFull):
+    base_folder = "ninco/NINCO/NINCO_OOD_classes/"
+
+    def download(self) -> None:
+        if self._check_integrity() and self._check_exists():
+            return
+        download_and_extract_archive(
+            self.url, download_root=self.root, extract_root=os.path.join(self.root, "ninco"), md5=self.file_md5
+        )
+
+
+class SSBHard(NINCO):
+    base_folder = "ninco/NINCO/NINCO_popular_datasets_subsamples/SSB_hard"
+
+    def find_classes(self, directory: str) -> Tuple[List[str], Dict[str, int]]:
+        classes = [""]
+
+        class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
+        return classes, class_to_idx
+
+
+class SSBEasy(SSBHard):
+    base_folder = "ninco/NINCO/NINCO_popular_datasets_subsamples/SSB_hard"
+
+
+class TexturesClean(SSBHard):
+    base_folder = "ninco/NINCO/NINCO_popular_datasets_subsamples/Textures"
+
+
+# ninco/NINCO/NINCO_popular_datasets_subsamples/