venv/lib/python3.11/site-packages/torchvision/datasets/celeba.py

import csv
import os
from collections import namedtuple
from pathlib import Path
from typing import Any, Callable, List, Optional, Tuple, Union

import PIL
import torch

from .utils import check_integrity, download_file_from_google_drive, extract_archive, verify_str_arg
from .vision import VisionDataset

CSV = namedtuple("CSV", ["header", "index", "data"])


class CelebA(VisionDataset):
    """`Large-scale CelebFaces Attributes (CelebA) Dataset <http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html>`_ Dataset.

    Args:
        root (str or ``pathlib.Path``): Root directory where images are downloaded to.
        split (string): One of {'train', 'valid', 'test', 'all'}.
            Accordingly dataset is selected.
        target_type (string or list, optional): Type of target to use, ``attr``, ``identity``, ``bbox``,
            or ``landmarks``. Can also be a list to output a tuple with all specified target types.
            The targets represent:

                - ``attr`` (Tensor shape=(40,) dtype=int): binary (0, 1) labels for attributes
                - ``identity`` (int): label for each person (data points with the same identity are the same person)
                - ``bbox`` (Tensor shape=(4,) dtype=int): bounding box (x, y, width, height)
                - ``landmarks`` (Tensor shape=(10,) dtype=int): landmark points (lefteye_x, lefteye_y, righteye_x,
                  righteye_y, nose_x, nose_y, leftmouth_x, leftmouth_y, rightmouth_x, rightmouth_y)

            Defaults to ``attr``. If empty, ``None`` will be returned as target.

        transform (callable, optional): A function/transform that takes in a PIL image
            and returns a transformed version. E.g, ``transforms.PILToTensor``
        target_transform (callable, optional): A function/transform that takes in the
            target and transforms it.
        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.

            .. warning::

                To download the dataset `gdown <https://github.com/wkentaro/gdown>`_ is required.
    """

    base_folder = "celeba"
    # There currently does not appear to be an easy way to extract 7z in python (without introducing additional
    # dependencies). The "in-the-wild" (not aligned+cropped) images are only in 7z, so they are not available
    # right now.
    file_list = [
        # File ID                                      MD5 Hash                            Filename
        ("0B7EVK8r0v71pZjFTYXZWM3FlRnM", "00d2c5bc6d35e252742224ab0c1e8fcb", "img_align_celeba.zip"),
        # ("0B7EVK8r0v71pbWNEUjJKdDQ3dGc","b6cd7e93bc7a96c2dc33f819aa3ac651", "img_align_celeba_png.7z"),
        # ("0B7EVK8r0v71peklHb0pGdDl6R28", "b6cd7e93bc7a96c2dc33f819aa3ac651", "img_celeba.7z"),
        ("0B7EVK8r0v71pblRyaVFSWGxPY0U", "75e246fa4810816ffd6ee81facbd244c", "list_attr_celeba.txt"),
        ("1_ee_0u7vcNLOfNLegJRHmolfH5ICW-XS", "32bd1bd63d3c78cd57e08160ec5ed1e2", "identity_CelebA.txt"),
        ("0B7EVK8r0v71pbThiMVRxWXZ4dU0", "00566efa6fedff7a56946cd1c10f1c16", "list_bbox_celeba.txt"),
        ("0B7EVK8r0v71pd0FJY3Blby1HUTQ", "cc24ecafdb5b50baae59b03474781f8c", "list_landmarks_align_celeba.txt"),
        # ("0B7EVK8r0v71pTzJIdlJWdHczRlU", "063ee6ddb681f96bc9ca28c6febb9d1a", "list_landmarks_celeba.txt"),
        ("0B7EVK8r0v71pY0NSMzRuSXJEVkk", "d32c9cbf5e040fd4025c592c306e6668", "list_eval_partition.txt"),
    ]

    def __init__(
        self,
        root: Union[str, Path],
        split: str = "train",
        target_type: Union[List[str], str] = "attr",
        transform: Optional[Callable] = None,
        target_transform: Optional[Callable] = None,
        download: bool = False,
    ) -> None:
        super().__init__(root, transform=transform, target_transform=target_transform)
        self.split = split
        if isinstance(target_type, list):
            self.target_type = target_type
        else:
            self.target_type = [target_type]

        if not self.target_type and self.target_transform is not None:
            raise RuntimeError("target_transform is specified but target_type is empty")

        if download:
            self.download()

        if not self._check_integrity():
            raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")

        split_map = {
            "train": 0,
            "valid": 1,
            "test": 2,
            "all": None,
        }
        split_ = split_map[
            verify_str_arg(
                split.lower() if isinstance(split, str) else split,
                "split",
                ("train", "valid", "test", "all"),
            )
        ]
        splits = self._load_csv("list_eval_partition.txt")
        identity = self._load_csv("identity_CelebA.txt")
        bbox = self._load_csv("list_bbox_celeba.txt", header=1)
        landmarks_align = self._load_csv("list_landmarks_align_celeba.txt", header=1)
        attr = self._load_csv("list_attr_celeba.txt", header=1)

        mask = slice(None) if split_ is None else (splits.data == split_).squeeze()

        if mask == slice(None):  # if split == "all"
            self.filename = splits.index
        else:
            self.filename = [splits.index[i] for i in torch.squeeze(torch.nonzero(mask))]  # type: ignore[arg-type]
        self.identity = identity.data[mask]
        self.bbox = bbox.data[mask]
        self.landmarks_align = landmarks_align.data[mask]
        self.attr = attr.data[mask]
        # map from {-1, 1} to {0, 1}
        self.attr = torch.div(self.attr + 1, 2, rounding_mode="floor")
        self.attr_names = attr.header

    def _load_csv(
        self,
        filename: str,
        header: Optional[int] = None,
    ) -> CSV:
        with open(os.path.join(self.root, self.base_folder, filename)) as csv_file:
            data = list(csv.reader(csv_file, delimiter=" ", skipinitialspace=True))

        if header is not None:
            headers = data[header]
            data = data[header + 1 :]
        else:
            headers = []

        indices = [row[0] for row in data]
        data = [row[1:] for row in data]
        data_int = [list(map(int, i)) for i in data]

        return CSV(headers, indices, torch.tensor(data_int))

    def _check_integrity(self) -> bool:
        for (_, md5, filename) in self.file_list:
            fpath = os.path.join(self.root, self.base_folder, filename)
            _, ext = os.path.splitext(filename)
            # Allow original archive to be deleted (zip and 7z)
            # Only need the extracted images
            if ext not in [".zip", ".7z"] and not check_integrity(fpath, md5):
                return False

        # Should check a hash of the images
        return os.path.isdir(os.path.join(self.root, self.base_folder, "img_align_celeba"))

    def download(self) -> None:
        if self._check_integrity():
            return

        for (file_id, md5, filename) in self.file_list:
            download_file_from_google_drive(file_id, os.path.join(self.root, self.base_folder), filename, md5)

        extract_archive(os.path.join(self.root, self.base_folder, "img_align_celeba.zip"))

    def __getitem__(self, index: int) -> Tuple[Any, Any]:
        X = PIL.Image.open(os.path.join(self.root, self.base_folder, "img_align_celeba", self.filename[index]))

        target: Any = []
        for t in self.target_type:
            if t == "attr":
                target.append(self.attr[index, :])
            elif t == "identity":
                target.append(self.identity[index, 0])
            elif t == "bbox":
                target.append(self.bbox[index, :])
            elif t == "landmarks":
                target.append(self.landmarks_align[index, :])
            else:
                # TODO: refactor with utils.verify_str_arg
                raise ValueError(f'Target type "{t}" is not recognized.')

        if self.transform is not None:
            X = self.transform(X)

        if target:
            target = tuple(target) if len(target) > 1 else target[0]

            if self.target_transform is not None:
                target = self.target_transform(target)
        else:
            target = None

        return X, target

    def __len__(self) -> int:
        return len(self.attr)

    def extra_repr(self) -> str:
        lines = ["Target type: {target_type}", "Split: {split}"]
        return "\n".join(lines).format(**self.__dict__)