Source code for torchvision.datasets.celeba

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(), "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))]
        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():
            print("Files already downloaded and verified")
            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__)