import os
import shutil
import tempfile
from contextlib import contextmanager
from typing import Any, Dict, Iterator, List, Optional, Tuple
import torch
from .folder import ImageFolder
from .utils import check_integrity, extract_archive, verify_str_arg
ARCHIVE_META = {
"train": ("ILSVRC2012_img_train.tar", "1d675b47d978889d74fa0da5fadfb00e"),
"val": ("ILSVRC2012_img_val.tar", "29b22e2961454d5413ddabcf34fc5622"),
"devkit": ("ILSVRC2012_devkit_t12.tar.gz", "fa75699e90414af021442c21a62c3abf"),
}
META_FILE = "meta.bin"
class ImageNet(ImageFolder):
"""`ImageNet <http://image-net.org/>`_ 2012 Classification Dataset.
.. note::
Before using this class, it is required to download ImageNet 2012 dataset from
`here <https://image-net.org/challenges/LSVRC/2012/2012-downloads.php>`_ and
place the files ``ILSVRC2012_devkit_t12.tar.gz`` and ``ILSVRC2012_img_train.tar``
or ``ILSVRC2012_img_val.tar`` based on ``split`` in the root directory.
Args:
root (string): Root directory of the ImageNet Dataset.
split (string, optional): The dataset split, supports ``train``, or ``val``.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.RandomCrop``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
loader (callable, optional): A function to load an image given its path.
Attributes:
classes (list): List of the class name tuples.
class_to_idx (dict): Dict with items (class_name, class_index).
wnids (list): List of the WordNet IDs.
wnid_to_idx (dict): Dict with items (wordnet_id, class_index).
imgs (list): List of (image path, class_index) tuples
targets (list): The class_index value for each image in the dataset
"""
def __init__(self, root: str, split: str = "train", **kwargs: Any) -> None:
root = self.root = os.path.expanduser(root)
self.split = verify_str_arg(split, "split", ("train", "val"))
self.parse_archives()
wnid_to_classes = load_meta_file(self.root)[0]
super().__init__(self.split_folder, **kwargs)
self.root = root
self.wnids = self.classes
self.wnid_to_idx = self.class_to_idx
self.classes = [wnid_to_classes[wnid] for wnid in self.wnids]
self.class_to_idx = {cls: idx for idx, clss in enumerate(self.classes) for cls in clss}
def parse_archives(self) -> None:
if not check_integrity(os.path.join(self.root, META_FILE)):
parse_devkit_archive(self.root)
if not os.path.isdir(self.split_folder):
if self.split == "train":
parse_train_archive(self.root)
elif self.split == "val":
parse_val_archive(self.root)
@property
def split_folder(self) -> str:
return os.path.join(self.root, self.split)
def extra_repr(self) -> str:
return "Split: {split}".format(**self.__dict__)
def load_meta_file(root: str, file: Optional[str] = None) -> Tuple[Dict[str, str], List[str]]:
if file is None:
file = META_FILE
file = os.path.join(root, file)
if check_integrity(file):
return torch.load(file)
else:
msg = (
"The meta file {} is not present in the root directory or is corrupted. "
"This file is automatically created by the ImageNet dataset."
)
raise RuntimeError(msg.format(file, root))
def _verify_archive(root: str, file: str, md5: str) -> None:
if not check_integrity(os.path.join(root, file), md5):
msg = (
"The archive {} is not present in the root directory or is corrupted. "
"You need to download it externally and place it in {}."
)
raise RuntimeError(msg.format(file, root))
def parse_devkit_archive(root: str, file: Optional[str] = None) -> None:
"""Parse the devkit archive of the ImageNet2012 classification dataset and save
the meta information in a binary file.
Args:
root (str): Root directory containing the devkit archive
file (str, optional): Name of devkit archive. Defaults to
'ILSVRC2012_devkit_t12.tar.gz'
"""
import scipy.io as sio
def parse_meta_mat(devkit_root: str) -> Tuple[Dict[int, str], Dict[str, Tuple[str, ...]]]:
metafile = os.path.join(devkit_root, "data", "meta.mat")
meta = sio.loadmat(metafile, squeeze_me=True)["synsets"]
nums_children = list(zip(*meta))[4]
meta = [meta[idx] for idx, num_children in enumerate(nums_children) if num_children == 0]
idcs, wnids, classes = list(zip(*meta))[:3]
classes = [tuple(clss.split(", ")) for clss in classes]
idx_to_wnid = {idx: wnid for idx, wnid in zip(idcs, wnids)}
wnid_to_classes = {wnid: clss for wnid, clss in zip(wnids, classes)}
return idx_to_wnid, wnid_to_classes
def parse_val_groundtruth_txt(devkit_root: str) -> List[int]:
file = os.path.join(devkit_root, "data", "ILSVRC2012_validation_ground_truth.txt")
with open(file) as txtfh:
val_idcs = txtfh.readlines()
return [int(val_idx) for val_idx in val_idcs]
@contextmanager
def get_tmp_dir() -> Iterator[str]:
tmp_dir = tempfile.mkdtemp()
try:
yield tmp_dir
finally:
shutil.rmtree(tmp_dir)
archive_meta = ARCHIVE_META["devkit"]
if file is None:
file = archive_meta[0]
md5 = archive_meta[1]
_verify_archive(root, file, md5)
with get_tmp_dir() as tmp_dir:
extract_archive(os.path.join(root, file), tmp_dir)
devkit_root = os.path.join(tmp_dir, "ILSVRC2012_devkit_t12")
idx_to_wnid, wnid_to_classes = parse_meta_mat(devkit_root)
val_idcs = parse_val_groundtruth_txt(devkit_root)
val_wnids = [idx_to_wnid[idx] for idx in val_idcs]
torch.save((wnid_to_classes, val_wnids), os.path.join(root, META_FILE))
def parse_train_archive(root: str, file: Optional[str] = None, folder: str = "train") -> None:
"""Parse the train images archive of the ImageNet2012 classification dataset and
prepare it for usage with the ImageNet dataset.
Args:
root (str): Root directory containing the train images archive
file (str, optional): Name of train images archive. Defaults to
'ILSVRC2012_img_train.tar'
folder (str, optional): Optional name for train images folder. Defaults to
'train'
"""
archive_meta = ARCHIVE_META["train"]
if file is None:
file = archive_meta[0]
md5 = archive_meta[1]
_verify_archive(root, file, md5)
train_root = os.path.join(root, folder)
extract_archive(os.path.join(root, file), train_root)
archives = [os.path.join(train_root, archive) for archive in os.listdir(train_root)]
for archive in archives:
extract_archive(archive, os.path.splitext(archive)[0], remove_finished=True)
def parse_val_archive(
root: str, file: Optional[str] = None, wnids: Optional[List[str]] = None, folder: str = "val"
) -> None:
"""Parse the validation images archive of the ImageNet2012 classification dataset
and prepare it for usage with the ImageNet dataset.
Args:
root (str): Root directory containing the validation images archive
file (str, optional): Name of validation images archive. Defaults to
'ILSVRC2012_img_val.tar'
wnids (list, optional): List of WordNet IDs of the validation images. If None
is given, the IDs are loaded from the meta file in the root directory
folder (str, optional): Optional name for validation images folder. Defaults to
'val'
"""
archive_meta = ARCHIVE_META["val"]
if file is None:
file = archive_meta[0]
md5 = archive_meta[1]
if wnids is None:
wnids = load_meta_file(root)[1]
_verify_archive(root, file, md5)
val_root = os.path.join(root, folder)
extract_archive(os.path.join(root, file), val_root)
images = sorted(os.path.join(val_root, image) for image in os.listdir(val_root))
for wnid in set(wnids):
os.mkdir(os.path.join(val_root, wnid))
for wnid, img_file in zip(wnids, images):
shutil.move(img_file, os.path.join(val_root, wnid, os.path.basename(img_file)))