Source code for torchvision.datasets.celeba
from collections import namedtuple
import csv
from functools import partial
import torch
import os
import PIL
from typing import Any, Callable, List, Optional, Union, Tuple
from .vision import VisionDataset
from .utils import download_file_from_google_drive, check_integrity, verify_str_arg
CSV = namedtuple("CSV", ["header", "index", "data"])
[docs]class CelebA(VisionDataset):
"""`Large-scale CelebFaces Attributes (CelebA) Dataset <http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html>`_ 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`` (np.array 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`` (np.array shape=(4,) dtype=int): bounding box (x, y, width, height)
- ``landmarks`` (np.array 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.ToTensor``
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 a 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: 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(CelebA, self).__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:
data, indices, headers = [], [], []
fn = partial(os.path.join, self.root, self.base_folder)
with open(fn(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:]
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:
import zipfile
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)
with zipfile.ZipFile(os.path.join(self.root, self.base_folder, "img_align_celeba.zip"), "r") as f:
f.extractall(os.path.join(self.root, self.base_folder))
[docs] 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("Target type \"{}\" is not recognized.".format(t))
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__)
