"""
Some of the datasets used in OpenOOD 1.5 benchmark.
"""
import json
import logging
import os
from os.path import dirname, exists, join
from typing import Callable, Optional
from torchvision.datasets.utils import extract_archive
from pytorch_ood.dataset.img.base import ImageDatasetBase, _get_resource_file
log = logging.getLogger(__name__)
[docs]
class iNaturalist(ImageDatasetBase):
"""
Subset of the iNaturalist dataset used as OOD data for ImageNet, proposed in
*MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space*.
All labels are -1 by default.
:see Paper: `MOS <https://arxiv.org/pdf/2105.01879.pdf>`__
:see Paper: `iNaturalist <https://openaccess.thecvf.com/content_cvpr_2018/html/Van_Horn_The_INaturalist_Species_CVPR_2018_paper.html>`__
"""
gdrive_id = "1zfLfMvoUD0CUlKNnkk7LgxZZBnTBipdj"
filename = "iNaturalist.zip"
target_dir = "iNaturalist"
base_folder = join(target_dir, "images")
def __init__(
self,
root: str,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
download: bool = False,
) -> None:
self.archive_file = join(root, self.filename)
super(iNaturalist, self).__init__(
root=root,
transform=transform,
target_transform=target_transform,
download=download,
)
def download(self) -> None:
if self._check_integrity():
log.debug("Files already downloaded and verified")
return
try:
import gdown
gdown.download(id=self.gdrive_id, output=self.archive_file)
except ImportError:
raise RuntimeError("You have to install 'gdown' to download this dataset")
extract_archive(from_path=self.archive_file, to_path=join(self.root, self.target_dir))
def _check_integrity(self) -> bool:
return exists(join(self.root, self.filename))
[docs]
class OpenImagesO(iNaturalist):
"""
Images sourced from the OpenImages dataset used as OOD data for ImageNet, as provided in
*OpenOOD: Benchmarking Generalized Out-of-Distribution Detection*.
All labels are -1 by default.
:see Website: `OpenImages <https://storage.googleapis.com/openimages/web/index.html>`__
The test set contains 15869 , the validation set 1763 images.
"""
gdrive_id = "1VUFXnB_z70uHfdgJG2E_pjYOcEgqM7tE"
filename = "openimage_o.zip"
target_dir = "OpenImagesO"
base_folder = join(target_dir, "images")
inclusion_json = {
"test": "test_openimage_o.json",
"val": "val_openimage_o.json",
}
def __init__(
self,
root: str,
subset="test",
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
download: bool = False,
) -> None:
"""
:param subset: can be either ``val`` or ``test``
"""
assert subset in list(self.inclusion_json.keys())
super(OpenImagesO, self).__init__(
root=root,
transform=transform,
target_transform=target_transform,
download=download,
)
p = _get_resource_file(self.inclusion_json[subset])
with open(p, "r") as f:
included = json.load(f)
self.files = [join(self.basedir, f) for f in included]
[docs]
class Places365(iNaturalist):
"""
Images sourced from the Places365 dataset used as OOD data, usually for CIFAR 10 and 100.
All labels are -1 by default.
Dataset set contains 36500 images.
:see Website: `Places <http://places.csail.mit.edu/browser.html>`__
.. image:: https://production-media.paperswithcode.com/datasets/Places-0000003475-4b6da14b.jpg
:target: http://places.csail.mit.edu/browser.html
:alt: Places 365 examples
"""
gdrive_id = "1Ec-LRSTf6u5vEctKX9vRp9OA6tqnJ0Ay"
filename = "places365.zip"
target_dir = "places365"
base_folder = target_dir
def __init__(
self,
root: str,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
download: bool = False,
) -> None:
super(Places365, self).__init__(
root=root,
transform=transform,
target_transform=target_transform,
download=download,
)
self.files = []
for d in os.listdir(self.basedir):
p = join(self.basedir, d)
if not os.path.isdir(join(p)):
continue
self.files += [join(p, f) for f in os.listdir(p)]