CeleA是香港中文大学的开放数据，包含10177个名人身份的202599张图片，并且都做好了特征标记，这个数据集对人脸相关的训练来说是非常好用的数据集。
但是它不像其他数据集一样可以自动下载，比如mnist
import torchvision.datasets as dset
import torchvision.transforms as transforms
dataroot = './'
imagesize = 64
ataset = dset.MNIST(root=dataroot, download=True,
transform=transforms.Compose([
transforms.Resize(imagesize),
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,)),
]))在torchvision.datasets.celeba.py文件中，celeba的下载方式有两种：
def download(self) -> None:
# 第一种下载方式，手动下载
if self._check_integrity():
print("Files already downloaded and verified")
return
# 第二种下载方式，从谷歌云盘下载
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"))显然，如果不能手动下载，就要从谷歌云盘下了。但是谷歌云盘需要，所以还是手动下吧。
谷歌云盘下载的错误信息：
requests.exceptions.ConnectionError: HTTPSConnectionPool(host='drive.google.com', port=443): Max retries exceeded with url: /uc?id=0B7EVK8r0v71pblRyaVFSWGxPY0U&export=download (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x000002746E4E7E20>: Failed to establish a new connection: [WinError 10060] 由于连接方在一段时间后没有正确答复或连接的主机没有反应，连接尝试失败。'))百度网盘地址：CelebA_免费高速下载|百度网盘-分享无限制 (baidu.com)
那么问题来了，这么多文件，该下哪个呢？ 下完之后又放到哪里呢？
还是在torchvision.datasets.celeba.py文件中，有一个检查完整性的函数_check_integrity(),
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这个函数会扫描self.file_list中的内容，
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"),
]被注释掉了三个，显然，我们只要把没被注释的六个文件下载就好了。
我们需要建一个存放数据的文件夹data，再在data下建一个文件夹celeba，最后把需要下载的文件放到celeba下。
因为
fpath = os.path.join(self.root, self.base_folder, filename)base_folder = "celeba"，所以使用的时候只需要写根路径就好，比如：
import torchvision.datasets as dset
import torchvision.transforms as transforms
dataroot = './data'
dataset = dset.CelebA(root=dataroot, download=True,
transform=transforms.Compose([
transforms.Resize(64),
transforms.CenterCrop(64),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]))
print(dataset)最终结果：
Files already downloaded and verified
Dataset CelebA
Number of datapoints: 162770
Root location: ./data
Target type: ['attr']
Split: train
StandardTransform
Transform: Compose(
Resize(size=64, interpolation=bilinear, max_size=None, antialias=None)
CenterCrop(size=(64, 64))
ToTensor()
Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
)感觉其他人好像轻轻松松就使用成功了，不知道为啥我就频频踩坑，先是通过程序无法下载，然后去kaggle上下了，结果报错。然后看了pytorch的官方文档，
以为只用下一个文件，又花时间下了，结果可想而知。
而csdn上大家都是在介绍这个数据集，这篇文章介绍得还蛮简洁，如果有不知道这个数据集的可以看看这个。
最后附上celeba.py
import csv
import os
from collections import namedtuple
from typing import Any, Callable, List, Optional, Union, Tuple
import PIL
import torch
from .utils import download_file_from_google_drive, check_integrity, verify_str_arg, extract_archive
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 (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.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 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().__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__)
check_integrity函数在torchvision.datasets.utils.py中，
def check_integrity(fpath: str, md5: Optional[str] = None) -> bool:
if not os.path.isfile(fpath):
return False
if md5 is None:
return True
return check_md5(fpath, md5)本文章为转载内容，我们尊重原作者对文章享有的著作权。如有内容错误或侵权问题，欢迎原作者联系我们进行内容更正或删除文章。
