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