Source code for torchvision.datasets.flowers102
from pathlib import Path
from typing import Any, Callable, Optional, Union
from .folder import default_loader
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 <http://www.robots.ox.ac.uk/~vgg/data/flowers/102/>`_ Dataset.
.. warning::
This class needs `scipy <http://docs.scipy.org/doc/>`_ 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 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.
"""
_download_url_prefix = "http://www.robots.ox.ac.uk/~vgg/data/flowers/102/"
_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,
loader: Callable[[Union[str, Path]], Any] = default_loader,
) -> 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:
self.download()
if not self._check_integrity():
raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")
from scipy.io 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")
self.loader = loader
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 = self.loader(image_file)
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)
classes = [
"pink primrose",
"hard-leaved pocket orchid",
"canterbury bells",
"sweet pea",
"english marigold",
"tiger lily",
"moon orchid",
"bird of paradise",
"monkshood",
"globe thistle",
"snapdragon",
"colt's foot",
"king protea",
"spear thistle",
"yellow iris",
"globe-flower",
"purple coneflower",
"peruvian lily",
"balloon flower",
"giant white arum lily",
"fire lily",
"pincushion flower",
"fritillary",
"red ginger",
"grape hyacinth",
"corn poppy",
"prince of wales feathers",
"stemless gentian",
"artichoke",
"sweet william",
"carnation",
"garden phlox",
"love in the mist",
"mexican aster",
"alpine sea holly",
"ruby-lipped cattleya",
"cape flower",
"great masterwort",
"siam tulip",
"lenten rose",
"barbeton daisy",
"daffodil",
"sword lily",
"poinsettia",
"bolero deep blue",
"wallflower",
"marigold",
"buttercup",
"oxeye daisy",
"common dandelion",
"petunia",
"wild pansy",
"primula",
"sunflower",
"pelargonium",
"bishop of llandaff",
"gaura",
"geranium",
"orange dahlia",
"pink-yellow dahlia?",
"cautleya spicata",
"japanese anemone",
"black-eyed susan",
"silverbush",
"californian poppy",
"osteospermum",
"spring crocus",
"bearded iris",
"windflower",
"tree poppy",
"gazania",
"azalea",
"water lily",
"rose",
"thorn apple",
"morning glory",
"passion flower",
"lotus",
"toad lily",
"anthurium",
"frangipani",
"clematis",
"hibiscus",
"columbine",
"desert-rose",
"tree mallow",
"magnolia",
"cyclamen",
"watercress",
"canna lily",
"hippeastrum",
"bee balm",
"ball moss",
"foxglove",
"bougainvillea",
"camellia",
"mallow",
"mexican petunia",
"bromelia",
"blanket flower",
"trumpet creeper",
"blackberry lily",
]
