import os
import subprocess
from typing import Callable, Optional
from torchvision.datasets import ImageFolder
from torchvision.datasets.utils import check_integrity, extract_archive
[docs]class iSUN(ImageFolder):
"""`iSUN <ODIN_PAPER_URL>`_ 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 = "iSUN"
filename = "iSUN.tar.gz"
file_md5 = "be77b0f2c26fda898afac5f99645ee70"
url = "https://www.dropbox.com/s/ssz7qxfqae0cca5/iSUN.tar.gz"
# size 8925
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)
[docs] def download(self) -> None:
if self._check_integrity() and self._check_exists():
return
print(f"Downloading {self.filename}...")
subprocess.run(f"wget {self.url} -P {self.root}".split(" "), capture_output=True, text=True)
extract_archive(self.archive, self.dataset_folder)