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195 lines
8.3 KiB
195 lines
8.3 KiB
5 months ago
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import csv
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import os
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from collections import namedtuple
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from pathlib import Path
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from typing import Any, Callable, List, Optional, Tuple, Union
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import PIL
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import torch
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from .utils import check_integrity, download_file_from_google_drive, extract_archive, verify_str_arg
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from .vision import VisionDataset
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CSV = namedtuple("CSV", ["header", "index", "data"])
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class CelebA(VisionDataset):
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"""`Large-scale CelebFaces Attributes (CelebA) Dataset <http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html>`_ Dataset.
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Args:
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root (str or ``pathlib.Path``): Root directory where images are downloaded to.
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split (string): One of {'train', 'valid', 'test', 'all'}.
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Accordingly dataset is selected.
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target_type (string or list, optional): Type of target to use, ``attr``, ``identity``, ``bbox``,
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or ``landmarks``. Can also be a list to output a tuple with all specified target types.
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The targets represent:
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- ``attr`` (Tensor shape=(40,) dtype=int): binary (0, 1) labels for attributes
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- ``identity`` (int): label for each person (data points with the same identity are the same person)
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- ``bbox`` (Tensor shape=(4,) dtype=int): bounding box (x, y, width, height)
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- ``landmarks`` (Tensor shape=(10,) dtype=int): landmark points (lefteye_x, lefteye_y, righteye_x,
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righteye_y, nose_x, nose_y, leftmouth_x, leftmouth_y, rightmouth_x, rightmouth_y)
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Defaults to ``attr``. If empty, ``None`` will be returned as target.
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.PILToTensor``
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target_transform (callable, optional): A function/transform that takes in the
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target and transforms it.
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download (bool, optional): If true, downloads the dataset from the internet and
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puts it in root directory. If dataset is already downloaded, it is not
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downloaded again.
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.. warning::
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To download the dataset `gdown <https://github.com/wkentaro/gdown>`_ is required.
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"""
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base_folder = "celeba"
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# There currently does not appear to be an easy way to extract 7z in python (without introducing additional
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# dependencies). The "in-the-wild" (not aligned+cropped) images are only in 7z, so they are not available
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# right now.
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file_list = [
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# File ID MD5 Hash Filename
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("0B7EVK8r0v71pZjFTYXZWM3FlRnM", "00d2c5bc6d35e252742224ab0c1e8fcb", "img_align_celeba.zip"),
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# ("0B7EVK8r0v71pbWNEUjJKdDQ3dGc","b6cd7e93bc7a96c2dc33f819aa3ac651", "img_align_celeba_png.7z"),
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# ("0B7EVK8r0v71peklHb0pGdDl6R28", "b6cd7e93bc7a96c2dc33f819aa3ac651", "img_celeba.7z"),
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("0B7EVK8r0v71pblRyaVFSWGxPY0U", "75e246fa4810816ffd6ee81facbd244c", "list_attr_celeba.txt"),
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("1_ee_0u7vcNLOfNLegJRHmolfH5ICW-XS", "32bd1bd63d3c78cd57e08160ec5ed1e2", "identity_CelebA.txt"),
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("0B7EVK8r0v71pbThiMVRxWXZ4dU0", "00566efa6fedff7a56946cd1c10f1c16", "list_bbox_celeba.txt"),
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("0B7EVK8r0v71pd0FJY3Blby1HUTQ", "cc24ecafdb5b50baae59b03474781f8c", "list_landmarks_align_celeba.txt"),
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# ("0B7EVK8r0v71pTzJIdlJWdHczRlU", "063ee6ddb681f96bc9ca28c6febb9d1a", "list_landmarks_celeba.txt"),
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("0B7EVK8r0v71pY0NSMzRuSXJEVkk", "d32c9cbf5e040fd4025c592c306e6668", "list_eval_partition.txt"),
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]
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def __init__(
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self,
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root: Union[str, Path],
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split: str = "train",
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target_type: Union[List[str], str] = "attr",
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transform: Optional[Callable] = None,
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target_transform: Optional[Callable] = None,
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download: bool = False,
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) -> None:
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super().__init__(root, transform=transform, target_transform=target_transform)
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self.split = split
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if isinstance(target_type, list):
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self.target_type = target_type
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else:
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self.target_type = [target_type]
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if not self.target_type and self.target_transform is not None:
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raise RuntimeError("target_transform is specified but target_type is empty")
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if download:
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self.download()
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if not self._check_integrity():
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raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")
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split_map = {
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"train": 0,
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"valid": 1,
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"test": 2,
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"all": None,
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}
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split_ = split_map[verify_str_arg(split.lower(), "split", ("train", "valid", "test", "all"))]
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splits = self._load_csv("list_eval_partition.txt")
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identity = self._load_csv("identity_CelebA.txt")
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bbox = self._load_csv("list_bbox_celeba.txt", header=1)
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landmarks_align = self._load_csv("list_landmarks_align_celeba.txt", header=1)
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attr = self._load_csv("list_attr_celeba.txt", header=1)
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mask = slice(None) if split_ is None else (splits.data == split_).squeeze()
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if mask == slice(None): # if split == "all"
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self.filename = splits.index
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else:
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self.filename = [splits.index[i] for i in torch.squeeze(torch.nonzero(mask))]
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self.identity = identity.data[mask]
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self.bbox = bbox.data[mask]
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self.landmarks_align = landmarks_align.data[mask]
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self.attr = attr.data[mask]
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# map from {-1, 1} to {0, 1}
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self.attr = torch.div(self.attr + 1, 2, rounding_mode="floor")
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self.attr_names = attr.header
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def _load_csv(
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self,
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filename: str,
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header: Optional[int] = None,
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) -> CSV:
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with open(os.path.join(self.root, self.base_folder, filename)) as csv_file:
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data = list(csv.reader(csv_file, delimiter=" ", skipinitialspace=True))
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if header is not None:
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headers = data[header]
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data = data[header + 1 :]
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else:
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headers = []
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indices = [row[0] for row in data]
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data = [row[1:] for row in data]
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data_int = [list(map(int, i)) for i in data]
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return CSV(headers, indices, torch.tensor(data_int))
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def _check_integrity(self) -> bool:
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for (_, md5, filename) in self.file_list:
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fpath = os.path.join(self.root, self.base_folder, filename)
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_, ext = os.path.splitext(filename)
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# Allow original archive to be deleted (zip and 7z)
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# Only need the extracted images
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if ext not in [".zip", ".7z"] and not check_integrity(fpath, md5):
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return False
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# Should check a hash of the images
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return os.path.isdir(os.path.join(self.root, self.base_folder, "img_align_celeba"))
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def download(self) -> None:
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if self._check_integrity():
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print("Files already downloaded and verified")
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return
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for (file_id, md5, filename) in self.file_list:
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download_file_from_google_drive(file_id, os.path.join(self.root, self.base_folder), filename, md5)
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extract_archive(os.path.join(self.root, self.base_folder, "img_align_celeba.zip"))
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def __getitem__(self, index: int) -> Tuple[Any, Any]:
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X = PIL.Image.open(os.path.join(self.root, self.base_folder, "img_align_celeba", self.filename[index]))
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target: Any = []
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for t in self.target_type:
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if t == "attr":
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target.append(self.attr[index, :])
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elif t == "identity":
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target.append(self.identity[index, 0])
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elif t == "bbox":
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target.append(self.bbox[index, :])
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elif t == "landmarks":
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target.append(self.landmarks_align[index, :])
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else:
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# TODO: refactor with utils.verify_str_arg
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raise ValueError(f'Target type "{t}" is not recognized.')
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if self.transform is not None:
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X = self.transform(X)
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if target:
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target = tuple(target) if len(target) > 1 else target[0]
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if self.target_transform is not None:
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target = self.target_transform(target)
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else:
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target = None
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return X, target
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def __len__(self) -> int:
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return len(self.attr)
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def extra_repr(self) -> str:
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lines = ["Target type: {target_type}", "Split: {split}"]
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return "\n".join(lines).format(**self.__dict__)
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