Source code for torchvision.datasets.gtsrb

import csv
import pathlib
from typing import Any, Callable, Optional, Tuple

import PIL

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


class GTSRB(VisionDataset):
    """`German Traffic Sign Recognition Benchmark (GTSRB) <https://benchmark.ini.rub.de/>`_ Dataset.

    Args:
        root (string): Root directory of the dataset.
        split (string, optional): The dataset split, supports ``"train"`` (default), or ``"test"``.
        transform (callable, optional): A function/transform that  takes in an 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.
    """

    def __init__(
        self,
        root: str,
        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", "test"))
        self._base_folder = pathlib.Path(root) / "gtsrb"
        self._target_folder = (
            self._base_folder / "GTSRB" / ("Training" if self._split == "train" else "Final_Test/Images")
        )

        if download:
            self.download()

        if not self._check_exists():
            raise RuntimeError("Dataset not found. You can use download=True to download it")

        if self._split == "train":
            samples = make_dataset(str(self._target_folder), extensions=(".ppm",))
        else:
            with open(self._base_folder / "GT-final_test.csv") as csv_file:
                samples = [
                    (str(self._target_folder / row["Filename"]), int(row["ClassId"]))
                    for row in csv.DictReader(csv_file, delimiter=";", skipinitialspace=True)
                ]

        self._samples = samples
        self.transform = transform
        self.target_transform = target_transform

    def __len__(self) -> int:
        return len(self._samples)

    def __getitem__(self, index: int) -> Tuple[Any, Any]:

        path, target = self._samples[index]
        sample = PIL.Image.open(path).convert("RGB")

        if self.transform is not None:
            sample = self.transform(sample)

        if self.target_transform is not None:
            target = self.target_transform(target)

        return sample, target

    def _check_exists(self) -> bool:
        return self._target_folder.is_dir()

    def download(self) -> None:
        if self._check_exists():
            return

        base_url = "https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/"

        if self._split == "train":
            download_and_extract_archive(
                f"{base_url}GTSRB-Training_fixed.zip",
                download_root=str(self._base_folder),
                md5="513f3c79a4c5141765e10e952eaa2478",
            )
        else:
            download_and_extract_archive(
                f"{base_url}GTSRB_Final_Test_Images.zip",
                download_root=str(self._base_folder),
                md5="c7e4e6327067d32654124b0fe9e82185",
            )
            download_and_extract_archive(
                f"{base_url}GTSRB_Final_Test_GT.zip",
                download_root=str(self._base_folder),
                md5="fe31e9c9270bbcd7b84b7f21a9d9d9e5",
            )