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import os
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from .folder import default_loader
from .utils import check_integrity, download_and_extract_archive, download_url, verify_str_arg
from .vision import VisionDataset
class _LFW(VisionDataset):
base_folder = "lfw-py"
download_url_prefix = "http://vis-www.cs.umass.edu/lfw/"
file_dict = {
"original": ("lfw", "lfw.tgz", "a17d05bd522c52d84eca14327a23d494"),
"funneled": ("lfw_funneled", "lfw-funneled.tgz", "1b42dfed7d15c9b2dd63d5e5840c86ad"),
"deepfunneled": ("lfw-deepfunneled", "lfw-deepfunneled.tgz", "68331da3eb755a505a502b5aacb3c201"),
}
checksums = {
"pairs.txt": "9f1ba174e4e1c508ff7cdf10ac338a7d",
"pairsDevTest.txt": "5132f7440eb68cf58910c8a45a2ac10b",
"pairsDevTrain.txt": "4f27cbf15b2da4a85c1907eb4181ad21",
"people.txt": "450f0863dd89e85e73936a6d71a3474b",
"peopleDevTest.txt": "e4bf5be0a43b5dcd9dc5ccfcb8fb19c5",
"peopleDevTrain.txt": "54eaac34beb6d042ed3a7d883e247a21",
"lfw-names.txt": "a6d0a479bd074669f656265a6e693f6d",
}
annot_file = {"10fold": "", "train": "DevTrain", "test": "DevTest"}
names = "lfw-names.txt"
def __init__(
self,
root: Union[str, Path],
split: str,
image_set: str,
view: str,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
download: bool = False,
loader: Callable[[str], Any] = default_loader,
) -> None:
super().__init__(os.path.join(root, self.base_folder), transform=transform, target_transform=target_transform)
self.image_set = verify_str_arg(image_set.lower(), "image_set", self.file_dict.keys())
images_dir, self.filename, self.md5 = self.file_dict[self.image_set]
self.view = verify_str_arg(view.lower(), "view", ["people", "pairs"])
self.split = verify_str_arg(split.lower(), "split", ["10fold", "train", "test"])
self.labels_file = f"{self.view}{self.annot_file[self.split]}.txt"
self.data: List[Any] = []
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.images_dir = os.path.join(self.root, images_dir)
self._loader = loader
def _check_integrity(self) -> bool:
st1 = check_integrity(os.path.join(self.root, self.filename), self.md5)
st2 = check_integrity(os.path.join(self.root, self.labels_file), self.checksums[self.labels_file])
if not st1 or not st2:
return False
if self.view == "people":
return check_integrity(os.path.join(self.root, self.names), self.checksums[self.names])
return True
def download(self) -> None:
if self._check_integrity():
return
url = f"{self.download_url_prefix}{self.filename}"
download_and_extract_archive(url, self.root, filename=self.filename, md5=self.md5)
download_url(f"{self.download_url_prefix}{self.labels_file}", self.root)
if self.view == "people":
download_url(f"{self.download_url_prefix}{self.names}", self.root)
def _get_path(self, identity: str, no: Union[int, str]) -> str:
return os.path.join(self.images_dir, identity, f"{identity}_{int(no):04d}.jpg")
def extra_repr(self) -> str:
return f"Alignment: {self.image_set}\nSplit: {self.split}"
def __len__(self) -> int:
return len(self.data)
class LFWPeople(_LFW):
"""`LFW <http://vis-www.cs.umass.edu/lfw/>`_ Dataset.
Args:
root (str or ``pathlib.Path``): Root directory of dataset where directory
``lfw-py`` exists or will be saved to if download is set to True.
split (string, optional): The image split to use. Can be one of ``train``, ``test``,
``10fold`` (default).
image_set (str, optional): Type of image funneling to use, ``original``, ``funneled`` or
``deepfunneled``. Defaults to ``funneled``.
transform (callable, optional): A function/transform that takes in a PIL image or torch.Tensor, depends on the given loader,
and returns a transformed version. E.g, ``transforms.RandomCrop``
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.
loader (callable, optional): A function to load an image given its path.
By default, it uses PIL as its image loader, but users could also pass in
``torchvision.io.decode_image`` for decoding image data into tensors directly.
"""
def __init__(
self,
root: str,
split: str = "10fold",
image_set: str = "funneled",
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
download: bool = False,
loader: Callable[[str], Any] = default_loader,
) -> None:
super().__init__(root, split, image_set, "people", transform, target_transform, download, loader=loader)
self.class_to_idx = self._get_classes()
self.data, self.targets = self._get_people()
def _get_people(self) -> Tuple[List[str], List[int]]:
data, targets = [], []
with open(os.path.join(self.root, self.labels_file)) as f:
lines = f.readlines()
n_folds, s = (int(lines[0]), 1) if self.split == "10fold" else (1, 0)
for fold in range(n_folds):
n_lines = int(lines[s])
people = [line.strip().split("\t") for line in lines[s + 1 : s + n_lines + 1]]
s += n_lines + 1
for i, (identity, num_imgs) in enumerate(people):
for num in range(1, int(num_imgs) + 1):
img = self._get_path(identity, num)
data.append(img)
targets.append(self.class_to_idx[identity])
return data, targets
def _get_classes(self) -> Dict[str, int]:
with open(os.path.join(self.root, self.names)) as f:
lines = f.readlines()
names = [line.strip().split()[0] for line in lines]
class_to_idx = {name: i for i, name in enumerate(names)}
return class_to_idx
def __getitem__(self, index: int) -> Tuple[Any, Any]:
"""
Args:
index (int): Index
Returns:
tuple: Tuple (image, target) where target is the identity of the person.
"""
img = self._loader(self.data[index])
target = self.targets[index]
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def extra_repr(self) -> str:
return super().extra_repr() + f"\nClasses (identities): {len(self.class_to_idx)}"
class LFWPairs(_LFW):
"""`LFW <http://vis-www.cs.umass.edu/lfw/>`_ Dataset.
Args:
root (str or ``pathlib.Path``): Root directory of dataset where directory
``lfw-py`` exists or will be saved to if download is set to True.
split (string, optional): The image split to use. Can be one of ``train``, ``test``,
``10fold``. Defaults to ``10fold``.
image_set (str, optional): Type of image funneling to use, ``original``, ``funneled`` or
``deepfunneled``. Defaults to ``funneled``.
transform (callable, optional): A function/transform that takes in a PIL image
and returns a transformed version. E.g, ``transforms.RandomRotation``
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.
loader (callable, optional): A function to load an image given its path.
By default, it uses PIL as its image loader, but users could also pass in
``torchvision.io.decode_image`` for decoding image data into tensors directly.
"""
def __init__(
self,
root: str,
split: str = "10fold",
image_set: str = "funneled",
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
download: bool = False,
loader: Callable[[str], Any] = default_loader,
) -> None:
super().__init__(root, split, image_set, "pairs", transform, target_transform, download, loader=loader)
self.pair_names, self.data, self.targets = self._get_pairs(self.images_dir)
def _get_pairs(self, images_dir: str) -> Tuple[List[Tuple[str, str]], List[Tuple[str, str]], List[int]]:
pair_names, data, targets = [], [], []
with open(os.path.join(self.root, self.labels_file)) as f:
lines = f.readlines()
if self.split == "10fold":
n_folds, n_pairs = lines[0].split("\t")
n_folds, n_pairs = int(n_folds), int(n_pairs)
else:
n_folds, n_pairs = 1, int(lines[0])
s = 1
for fold in range(n_folds):
matched_pairs = [line.strip().split("\t") for line in lines[s : s + n_pairs]]
unmatched_pairs = [line.strip().split("\t") for line in lines[s + n_pairs : s + (2 * n_pairs)]]
s += 2 * n_pairs
for pair in matched_pairs:
img1, img2, same = self._get_path(pair[0], pair[1]), self._get_path(pair[0], pair[2]), 1
pair_names.append((pair[0], pair[0]))
data.append((img1, img2))
targets.append(same)
for pair in unmatched_pairs:
img1, img2, same = self._get_path(pair[0], pair[1]), self._get_path(pair[2], pair[3]), 0
pair_names.append((pair[0], pair[2]))
data.append((img1, img2))
targets.append(same)
return pair_names, data, targets
def __getitem__(self, index: int) -> Tuple[Any, Any, int]:
"""
Args:
index (int): Index
Returns:
tuple: (image1, image2, target) where target is `0` for different indentities and `1` for same identities.
"""
img1, img2 = self.data[index]
img1, img2 = self._loader(img1), self._loader(img2)
target = self.targets[index]
if self.transform is not None:
img1, img2 = self.transform(img1), self.transform(img2)
if self.target_transform is not None:
target = self.target_transform(target)
return img1, img2, target
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