Add GTSRB dataset to the list of supporting datasets

docs/source/datasets.rst

@@ -48,6 +48,7 @@ You can also create your own datasets using the provided :ref:`base classes <bas
     FlyingChairs
     FlyingThings3D
     Food101
+    GTSRB
     HD1K
     HMDB51
     ImageNet

test/test_datasets.py

@@ -2275,5 +2275,55 @@ def inject_fake_data(self, tmpdir, config):
         return num_samples
 
 
+class GTSRBTestCase(datasets_utils.ImageDatasetTestCase):
+    DATASET_CLASS = datasets.GTSRB
+    FEATURE_TYPES = (PIL.Image.Image, int)
+
+    ADDITIONAL_CONFIGS = datasets_utils.combinations_grid(train=(True, False))
+
+    def inject_fake_data(self, tmpdir: str, config):
+        root_folder = os.path.join(tmpdir, "GTSRB")
+        os.makedirs(root_folder, exist_ok=True)
+
+        # Train data
+        train_folder = os.path.join(root_folder, "Training")
+        os.makedirs(train_folder, exist_ok=True)
+
+        num_examples = 3
+        classes = ("00000", "00042", "00012")
+        for class_idx in classes:
+            datasets_utils.create_image_folder(
+                train_folder,
+                name=class_idx,
+                file_name_fn=lambda image_idx: f"{class_idx}_{image_idx:05d}.ppm",
+                num_examples=num_examples,
+            )
+
+        total_number_of_examples = num_examples * len(classes)
+        # Test data
+        test_folder = os.path.join(root_folder, "Final_Test", "Images")
+        os.makedirs(test_folder, exist_ok=True)
+
+        with open(os.path.join(root_folder, "GT-final_test.csv"), "w") as csv_file:
+            csv_file.write("Filename;Width;Height;Roi.X1;Roi.Y1;Roi.X2;Roi.Y2;ClassId\n")
+
+            for _ in range(total_number_of_examples):
+                image_file = datasets_utils.create_random_string(5, string.digits) + ".ppm"
+                datasets_utils.create_image_file(test_folder, image_file)
+                row = [
+                    image_file,
+                    torch.randint(1, 100, size=()).item(),
+                    torch.randint(1, 100, size=()).item(),
+                    torch.randint(1, 100, size=()).item(),
+                    torch.randint(1, 100, size=()).item(),
+                    torch.randint(1, 100, size=()).item(),
+                    torch.randint(1, 100, size=()).item(),
+                    torch.randint(0, 43, size=()).item(),
+                ]
+                csv_file.write(";".join(map(str, row)) + "\n")
+
+        return total_number_of_examples
+
+
 if __name__ == "__main__":
     unittest.main()

torchvision/datasets/__init__.py

@@ -10,6 +10,7 @@
 from .flickr import Flickr8k, Flickr30k
 from .folder import ImageFolder, DatasetFolder
 from .food101 import Food101
+from .gtsrb import GTSRB
 from .hmdb51 import HMDB51
 from .imagenet import ImageNet
 from .inaturalist import INaturalist
@@ -83,4 +84,5 @@
     "Food101",
     "DTD",
     "FER2013",
+    "GTSRB",
 )

torchvision/datasets/gtsrb.py

@@ -0,0 +1,106 @@
+import csv
+import os
+from typing import Any, Callable, Optional, Tuple
+
+import PIL
+
+from .folder import make_dataset
+from .utils import download_and_extract_archive
+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.
+        train (bool, optional): If True, creates dataset from training set, otherwise
+            creates from test set.
+        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.
+    """
+
+    # Ground Truth for the test set
+    _gt_url = "https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/GTSRB_Final_Test_GT.zip"
+    _gt_csv = "GT-final_test.csv"
+    _gt_md5 = "fe31e9c9270bbcd7b84b7f21a9d9d9e5"
+
+    # URLs for the test and train set
+    _urls = (
+        "https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/GTSRB_Final_Test_Images.zip",
+        "https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/GTSRB-Training_fixed.zip",
+    )
+
+    _md5s = ("c7e4e6327067d32654124b0fe9e82185", "513f3c79a4c5141765e10e952eaa2478")
+
+    def __init__(
+        self,
+        root: str,
+        train: bool = True,
+        transform: Optional[Callable] = None,
+        target_transform: Optional[Callable] = None,
+        download: bool = False,
+    ) -> None:
+
+        super().__init__(root, transform=transform, target_transform=target_transform)
+
+        self.root = os.path.expanduser(root)
+
+        self.train = train
+
+        self._base_folder = os.path.join(self.root, type(self).__name__)
+        self._target_folder = os.path.join(self._base_folder, "Training" if self.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 train:
+            samples = make_dataset(self._target_folder, extensions=(".ppm",))
+        else:
+            with open(os.path.join(self._base_folder, self._gt_csv)) as csv_file:
+                samples = [
+                    (os.path.join(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 os.path.exists(self._target_folder) and os.path.isdir(self._target_folder)
+
+    def download(self) -> None:
+        if self._check_exists():
+            return
+
+        download_and_extract_archive(self._urls[self.train], download_root=self.root, md5=self._md5s[self.train])
+
+        if not self.train:
+            # Download Ground Truth for the test set
+            download_and_extract_archive(
+                self._gt_url, download_root=self.root, extract_root=self._base_folder, md5=self._gt_md5
+            )