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from pathlib import Path |
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from typing import Any, Callable, Optional, Tuple, Union |
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from .folder import default_loader |
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from .utils import check_integrity, download_and_extract_archive, download_url, verify_str_arg |
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from .vision import VisionDataset |
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class Flowers102(VisionDataset): |
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"""`Oxford 102 Flower <https://www.robots.ox.ac.uk/~vgg/data/flowers/102/>`_ Dataset. |
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.. warning:: |
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This class needs `scipy <https://docs.scipy.org/doc/>`_ to load target files from `.mat` format. |
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Oxford 102 Flower is an image classification dataset consisting of 102 flower categories. The |
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flowers were chosen to be flowers commonly occurring in the United Kingdom. Each class consists of |
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between 40 and 258 images. |
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The images have large scale, pose and light variations. In addition, there are categories that |
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have large variations within the category, and several very similar categories. |
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Args: |
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root (str or ``pathlib.Path``): Root directory of the dataset. |
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split (string, optional): The dataset split, supports ``"train"`` (default), ``"val"``, or ``"test"``. |
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transform (callable, optional): A function/transform that takes in a PIL image or torch.Tensor, depends on the given loader, |
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and returns a transformed version. E.g, ``transforms.RandomCrop`` |
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target_transform (callable, optional): A function/transform that takes in the 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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loader (callable, optional): A function to load an image given its path. |
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By default, it uses PIL as its image loader, but users could also pass in |
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``torchvision.io.decode_image`` for decoding image data into tensors directly. |
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""" |
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_download_url_prefix = "https://www.robots.ox.ac.uk/~vgg/data/flowers/102/" |
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_file_dict = { |
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"image": ("102flowers.tgz", "52808999861908f626f3c1f4e79d11fa"), |
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"label": ("imagelabels.mat", "e0620be6f572b9609742df49c70aed4d"), |
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"setid": ("setid.mat", "a5357ecc9cb78c4bef273ce3793fc85c"), |
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} |
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_splits_map = {"train": "trnid", "val": "valid", "test": "tstid"} |
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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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transform: Optional[Callable] = None, |
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target_transform: Optional[Callable] = None, |
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download: bool = False, |
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loader: Callable[[Union[str, Path]], Any] = default_loader, |
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) -> None: |
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super().__init__(root, transform=transform, target_transform=target_transform) |
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self._split = verify_str_arg(split, "split", ("train", "val", "test")) |
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self._base_folder = Path(self.root) / "flowers-102" |
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self._images_folder = self._base_folder / "jpg" |
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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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from scipy.io import loadmat |
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set_ids = loadmat(self._base_folder / self._file_dict["setid"][0], squeeze_me=True) |
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image_ids = set_ids[self._splits_map[self._split]].tolist() |
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labels = loadmat(self._base_folder / self._file_dict["label"][0], squeeze_me=True) |
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image_id_to_label = dict(enumerate((labels["labels"] - 1).tolist(), 1)) |
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self._labels = [] |
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self._image_files = [] |
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for image_id in image_ids: |
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self._labels.append(image_id_to_label[image_id]) |
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self._image_files.append(self._images_folder / f"image_{image_id:05d}.jpg") |
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self.loader = loader |
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def __len__(self) -> int: |
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return len(self._image_files) |
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def __getitem__(self, idx: int) -> Tuple[Any, Any]: |
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image_file, label = self._image_files[idx], self._labels[idx] |
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image = self.loader(image_file) |
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if self.transform: |
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image = self.transform(image) |
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if self.target_transform: |
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label = self.target_transform(label) |
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return image, label |
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def extra_repr(self) -> str: |
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return f"split={self._split}" |
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def _check_integrity(self): |
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if not (self._images_folder.exists() and self._images_folder.is_dir()): |
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return False |
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for id in ["label", "setid"]: |
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filename, md5 = self._file_dict[id] |
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if not check_integrity(str(self._base_folder / filename), md5): |
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return False |
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return True |
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def download(self): |
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if self._check_integrity(): |
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return |
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download_and_extract_archive( |
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f"{self._download_url_prefix}{self._file_dict['image'][0]}", |
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str(self._base_folder), |
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md5=self._file_dict["image"][1], |
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) |
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for id in ["label", "setid"]: |
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filename, md5 = self._file_dict[id] |
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download_url(self._download_url_prefix + filename, str(self._base_folder), md5=md5) |
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classes = [ |
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"pink primrose", |
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"hard-leaved pocket orchid", |
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"canterbury bells", |
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"sweet pea", |
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"english marigold", |
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"tiger lily", |
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"moon orchid", |
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"bird of paradise", |
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"monkshood", |
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"globe thistle", |
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"snapdragon", |
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"colt's foot", |
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"king protea", |
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"spear thistle", |
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"yellow iris", |
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"globe-flower", |
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"purple coneflower", |
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"peruvian lily", |
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"balloon flower", |
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"giant white arum lily", |
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"fire lily", |
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"pincushion flower", |
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"fritillary", |
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"red ginger", |
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"grape hyacinth", |
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"corn poppy", |
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"prince of wales feathers", |
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"stemless gentian", |
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"artichoke", |
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"sweet william", |
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"carnation", |
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"garden phlox", |
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"love in the mist", |
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"mexican aster", |
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"alpine sea holly", |
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"ruby-lipped cattleya", |
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"cape flower", |
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"great masterwort", |
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"siam tulip", |
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"lenten rose", |
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"barbeton daisy", |
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"daffodil", |
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"sword lily", |
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"poinsettia", |
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"bolero deep blue", |
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"wallflower", |
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"marigold", |
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"buttercup", |
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"oxeye daisy", |
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"common dandelion", |
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"petunia", |
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"wild pansy", |
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"primula", |
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"sunflower", |
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"pelargonium", |
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"bishop of llandaff", |
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"gaura", |
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"geranium", |
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"orange dahlia", |
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"pink-yellow dahlia?", |
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"cautleya spicata", |
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"japanese anemone", |
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"black-eyed susan", |
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"silverbush", |
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"californian poppy", |
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"osteospermum", |
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"spring crocus", |
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"bearded iris", |
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"windflower", |
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"tree poppy", |
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"gazania", |
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"azalea", |
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"water lily", |
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"rose", |
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"thorn apple", |
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"morning glory", |
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"passion flower", |
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"lotus", |
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"toad lily", |
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"anthurium", |
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"frangipani", |
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"clematis", |
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"hibiscus", |
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"columbine", |
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"desert-rose", |
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"tree mallow", |
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"magnolia", |
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"cyclamen", |
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"watercress", |
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"canna lily", |
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"hippeastrum", |
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"bee balm", |
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"ball moss", |
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"foxglove", |
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"bougainvillea", |
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"camellia", |
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"mallow", |
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"mexican petunia", |
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"bromelia", |
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"blanket flower", |
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"trumpet creeper", |
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"blackberry lily", |
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] |
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