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Source code for torchvision.datasets.fgvc_aircraft

from __future__ import annotations

import os
from typing import Any, Callable, Optional, Tuple

import PIL.Image

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


[docs]class FGVCAircraft(VisionDataset): """`FGVC Aircraft <https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/>`_ Dataset. The dataset contains 10,000 images of aircraft, with 100 images for each of 100 different aircraft model variants, most of which are airplanes. Aircraft models are organized in a three-levels hierarchy. The three levels, from finer to coarser, are: - ``variant``, e.g. Boeing 737-700. A variant collapses all the models that are visually indistinguishable into one class. The dataset comprises 100 different variants. - ``family``, e.g. Boeing 737. The dataset comprises 70 different families. - ``manufacturer``, e.g. Boeing. The dataset comprises 30 different manufacturers. Args: root (string): Root directory of the FGVC Aircraft dataset. split (string, optional): The dataset split, supports ``train``, ``val``, ``trainval`` and ``test``. annotation_level (str, optional): The annotation level, supports ``variant``, ``family`` and ``manufacturer``. 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. """ _URL = "https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/archives/fgvc-aircraft-2013b.tar.gz" def __init__( self, root: str, split: str = "trainval", annotation_level: str = "variant", 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", "trainval", "test")) self._annotation_level = verify_str_arg( annotation_level, "annotation_level", ("variant", "family", "manufacturer") ) self._data_path = os.path.join(self.root, "fgvc-aircraft-2013b") if download: self._download() if not self._check_exists(): raise RuntimeError("Dataset not found. You can use download=True to download it") annotation_file = os.path.join( self._data_path, "data", { "variant": "variants.txt", "family": "families.txt", "manufacturer": "manufacturers.txt", }[self._annotation_level], ) with open(annotation_file, "r") as f: self.classes = [line.strip() for line in f] self.class_to_idx = dict(zip(self.classes, range(len(self.classes)))) image_data_folder = os.path.join(self._data_path, "data", "images") labels_file = os.path.join(self._data_path, "data", f"images_{self._annotation_level}_{self._split}.txt") self._image_files = [] self._labels = [] with open(labels_file, "r") as f: for line in f: image_name, label_name = line.strip().split(" ", 1) self._image_files.append(os.path.join(image_data_folder, f"{image_name}.jpg")) self._labels.append(self.class_to_idx[label_name]) 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 = PIL.Image.open(image_file).convert("RGB") if self.transform: image = self.transform(image) if self.target_transform: label = self.target_transform(label) return image, label
def _download(self) -> None: """ Download the FGVC Aircraft dataset archive and extract it under root. """ if self._check_exists(): return download_and_extract_archive(self._URL, self.root) def _check_exists(self) -> bool: return os.path.exists(self._data_path) and os.path.isdir(self._data_path)

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