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celeba.py
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import torch
import os
import PIL
from vision import VisionDataset
from utils import download_file_from_google_drive, check_integrity
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'}.
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``.
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,
split="train",
target_type="attr",
transform=None, target_transform=None,
download=False):
import pandas
super(CelebA, self).__init__(root)
self.split = split
if isinstance(target_type, list):
self.target_type = target_type
else:
self.target_type = [target_type]
self.transform = transform
self.target_transform = target_transform
if download:
self.download()
if not self._check_integrity():
raise RuntimeError('Dataset not found or corrupted.' +
' You can use download=True to download it')
self.transform = transform
self.target_transform = target_transform
if split.lower() == "train":
split = 0
elif split.lower() == "valid":
split = 1
elif split.lower() == "test":
split = 2
else:
raise ValueError('Wrong split entered! Please use split="train" '
'or split="valid" or split="test"')
with open(os.path.join(self.root, self.base_folder, "list_eval_partition.txt"), "r") as f:
splits = pandas.read_csv(f, delim_whitespace=True, header=None, index_col=0)
with open(os.path.join(self.root, self.base_folder, "identity_CelebA.txt"), "r") as f:
self.identity = pandas.read_csv(f, delim_whitespace=True, header=None, index_col=0)
with open(os.path.join(self.root, self.base_folder, "list_bbox_celeba.txt"), "r") as f:
self.bbox = pandas.read_csv(f, delim_whitespace=True, header=1, index_col=0)
with open(os.path.join(self.root, self.base_folder, "list_landmarks_align_celeba.txt"), "r") as f:
self.landmarks_align = pandas.read_csv(f, delim_whitespace=True, header=1)
with open(os.path.join(self.root, self.base_folder, "list_attr_celeba.txt"), "r") as f:
self.attr = pandas.read_csv(f, delim_whitespace=True, header=1)
mask = (splits[1] == split)
self.filename = splits[mask].index.values
self.identity = torch.as_tensor(self.identity[mask].values)
self.bbox = torch.as_tensor(self.bbox[mask].values)
self.landmarks_align = torch.as_tensor(self.landmarks_align[mask].values)
self.attr = torch.as_tensor(self.attr[mask].values)
self.attr = (self.attr + 1) // 2 # map from {-1, 1} to {0, 1}
def _check_integrity(self):
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):
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))
def __getitem__(self, index):
X = PIL.Image.open(os.path.join(self.root, self.base_folder, "img_align_celeba", self.filename[index]))
target = []
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:
raise ValueError("Target type \"{}\" is not recognized.".format(t))
target = tuple(target) if len(target) > 1 else target[0]
if self.transform is not None:
X = self.transform(X)
if self.target_transform is not None:
target = self.target_transform(target)
return X, target
def __len__(self):
return len(self.attr)
def extra_repr(self):
lines = ["Target type: {target_type}", "Split: {split}"]
return '\n'.join(lines).format(**self.__dict__)