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
[docs]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)
[docs] 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",
)