gaussian-splatting-my/envs/gaussian_splatting/Lib/site-packages/torchvision/datasets/imagenet.py

import os
import shutil
import tempfile
from contextlib import contextmanager
from typing import Any, Dict, List, Iterator, 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.

    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)))