Source code for torchvision.datasets.flowers102

from pathlib import Path
from typing import Any, Callable, Optional, Tuple, Union

import PIL.Image

from .utils import check_integrity, download_and_extract_archive, download_url, verify_str_arg
from .vision import VisionDataset

[docs]class Flowers102(VisionDataset): """`Oxford 102 Flower <>`_ Dataset. .. warning:: This class needs `scipy <>`_ to load target files from `.mat` format. Oxford 102 Flower is an image classification dataset consisting of 102 flower categories. The flowers were chosen to be flowers commonly occurring in the United Kingdom. Each class consists of between 40 and 258 images. The images have large scale, pose and light variations. In addition, there are categories that have large variations within the category, and several very similar categories. Args: root (str or ``pathlib.Path``): Root directory of the dataset. split (string, optional): The dataset split, supports ``"train"`` (default), ``"val"``, or ``"test"``. transform (callable, optional): A function/transform that takes in a PIL image 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. """ _download_url_prefix = "" _file_dict = { # filename, md5 "image": ("102flowers.tgz", "52808999861908f626f3c1f4e79d11fa"), "label": ("imagelabels.mat", "e0620be6f572b9609742df49c70aed4d"), "setid": ("setid.mat", "a5357ecc9cb78c4bef273ce3793fc85c"), } _splits_map = {"train": "trnid", "val": "valid", "test": "tstid"} def __init__( self, root: Union[str, Path], split: str = "train", transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False, ) -> None: super().__init__(root, transform=transform, target_transform=target_transform) self._split = verify_str_arg(split, "split", ("train", "val", "test")) self._base_folder = Path(self.root) / "flowers-102" self._images_folder = self._base_folder / "jpg" if download: if not self._check_integrity(): raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it") from import loadmat set_ids = loadmat(self._base_folder / self._file_dict["setid"][0], squeeze_me=True) image_ids = set_ids[self._splits_map[self._split]].tolist() labels = loadmat(self._base_folder / self._file_dict["label"][0], squeeze_me=True) image_id_to_label = dict(enumerate((labels["labels"] - 1).tolist(), 1)) self._labels = [] self._image_files = [] for image_id in image_ids: self._labels.append(image_id_to_label[image_id]) self._image_files.append(self._images_folder / f"image_{image_id:05d}.jpg") def __len__(self) -> int: return len(self._image_files)
[docs] def __getitem__(self, idx: int) -> Tuple[Any, Any]: image_file, label = self._image_files[idx], self._labels[idx] image ="RGB") if self.transform: image = self.transform(image) if self.target_transform: label = self.target_transform(label) return image, label
def extra_repr(self) -> str: return f"split={self._split}" def _check_integrity(self): if not (self._images_folder.exists() and self._images_folder.is_dir()): return False for id in ["label", "setid"]: filename, md5 = self._file_dict[id] if not check_integrity(str(self._base_folder / filename), md5): return False return True def download(self): if self._check_integrity(): return download_and_extract_archive( f"{self._download_url_prefix}{self._file_dict['image'][0]}", str(self._base_folder), md5=self._file_dict["image"][1], ) for id in ["label", "setid"]: filename, md5 = self._file_dict[id] download_url(self._download_url_prefix + filename, str(self._base_folder), md5=md5)


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources