Source code for torchvision.datasets.celeba

import csv
import os
from collections import namedtuple
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"])

[docs]class CelebA(VisionDataset): """`Large-scale CelebFaces Attributes (CelebA) Dataset <>`_ Dataset. Args: root (string): 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 an 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. """ 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", ""), # ("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: str, 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: 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 ( == 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 =[mask] self.bbox =[mask] self.landmarks_align =[mask] self.attr =[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, ""))
[docs] def __getitem__(self, index: int) -> Tuple[Any, Any]: X =, 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__)


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