Use f-strings almost everywhere, and other cleanups by applying pyupgrade (#4585)

Co-authored-by: Nicolas Hug <nicolashug@fb.com>

test/test_transforms_tensor.py

@@ -371,7 +371,7 @@ def test_x_crop_save(method, tmpdir):
         ]
     )
     scripted_fn = torch.jit.script(fn)
-    scripted_fn.save(os.path.join(tmpdir, "t_op_list_{}.pt".format(method)))
+    scripted_fn.save(os.path.join(tmpdir, f"t_op_list_{method}.pt"))
 
 
 class TestResize:
@@ -816,7 +816,7 @@ def test_compose(device):
     transformed_tensor = transforms(tensor)
     torch.manual_seed(12)
     transformed_tensor_script = scripted_fn(tensor)
-    assert_equal(transformed_tensor, transformed_tensor_script, msg="{}".format(transforms))
+    assert_equal(transformed_tensor, transformed_tensor_script, msg=f"{transforms}")
 
     t = T.Compose(
         [
@@ -854,7 +854,7 @@ def test_random_apply(device):
     transformed_tensor = transforms(tensor)
     torch.manual_seed(12)
     transformed_tensor_script = scripted_fn(tensor)
-    assert_equal(transformed_tensor, transformed_tensor_script, msg="{}".format(transforms))
+    assert_equal(transformed_tensor, transformed_tensor_script, msg=f"{transforms}")
 
     if device == "cpu":
         # Can't check this twice, otherwise

test/test_transforms_video.py

@@ -163,7 +163,7 @@ class TestVideoTransforms:
     @pytest.mark.parametrize("p", (0, 1))
     def test_random_horizontal_flip_video(self, p):
         clip = torch.rand((3, 4, 112, 112), dtype=torch.float)
-        hclip = clip.flip((-1))
+        hclip = clip.flip(-1)
 
         out = transforms.RandomHorizontalFlipVideo(p=p)(clip)
         if p == 0:

torchvision/__init__.py

@@ -43,7 +43,7 @@ def set_image_backend(backend):
     """
     global _image_backend
     if backend not in ["PIL", "accimage"]:
-        raise ValueError("Invalid backend '{}'. Options are 'PIL' and 'accimage'".format(backend))
+        raise ValueError(f"Invalid backend '{backend}'. Options are 'PIL' and 'accimage'")
     _image_backend = backend
 
 
@@ -74,7 +74,7 @@ def set_video_backend(backend):
     if backend not in ["pyav", "video_reader"]:
         raise ValueError("Invalid video backend '%s'. Options are 'pyav' and 'video_reader'" % backend)
     if backend == "video_reader" and not io._HAS_VIDEO_OPT:
-        message = "video_reader video backend is not available." " Please compile torchvision from source and try again"
+        message = "video_reader video backend is not available. Please compile torchvision from source and try again"
         warnings.warn(message)
     else:
         _video_backend = backend

torchvision/datasets/caltech.py

@@ -40,9 +40,7 @@ class Caltech101(VisionDataset):
         target_transform: Optional[Callable] = None,
         download: bool = False,
     ) -> None:
-        super(Caltech101, self).__init__(
-            os.path.join(root, "caltech101"), transform=transform, target_transform=target_transform
-        )
+        super().__init__(os.path.join(root, "caltech101"), transform=transform, target_transform=target_transform)
         os.makedirs(self.root, exist_ok=True)
         if isinstance(target_type, str):
             target_type = [target_type]
@@ -52,7 +50,7 @@ class Caltech101(VisionDataset):
             self.download()
 
         if not self._check_integrity():
-            raise RuntimeError("Dataset not found or corrupted." + " You can use download=True to download it")
+            raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")
 
         self.categories = sorted(os.listdir(os.path.join(self.root, "101_ObjectCategories")))
         self.categories.remove("BACKGROUND_Google")  # this is not a real class
@@ -90,7 +88,7 @@ class Caltech101(VisionDataset):
                 self.root,
                 "101_ObjectCategories",
                 self.categories[self.y[index]],
-                "image_{:04d}.jpg".format(self.index[index]),
+                f"image_{self.index[index]:04d}.jpg",
             )
         )
 
@@ -104,7 +102,7 @@ class Caltech101(VisionDataset):
                         self.root,
                         "Annotations",
                         self.annotation_categories[self.y[index]],
-                        "annotation_{:04d}.mat".format(self.index[index]),
+                        f"annotation_{self.index[index]:04d}.mat",
                     )
                 )
                 target.append(data["obj_contour"])
@@ -167,16 +165,14 @@ class Caltech256(VisionDataset):
         target_transform: Optional[Callable] = None,
         download: bool = False,
     ) -> None:
-        super(Caltech256, self).__init__(
-            os.path.join(root, "caltech256"), transform=transform, target_transform=target_transform
-        )
+        super().__init__(os.path.join(root, "caltech256"), transform=transform, target_transform=target_transform)
         os.makedirs(self.root, exist_ok=True)
 
         if download:
             self.download()
 
         if not self._check_integrity():
-            raise RuntimeError("Dataset not found or corrupted." + " You can use download=True to download it")
+            raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")
 
         self.categories = sorted(os.listdir(os.path.join(self.root, "256_ObjectCategories")))
         self.index: List[int] = []
@@ -205,7 +201,7 @@ class Caltech256(VisionDataset):
                 self.root,
                 "256_ObjectCategories",
                 self.categories[self.y[index]],
-                "{:03d}_{:04d}.jpg".format(self.y[index] + 1, self.index[index]),
+                f"{self.y[index] + 1:03d}_{self.index[index]:04d}.jpg",
             )
         )
 

torchvision/datasets/celeba.py

@@ -66,7 +66,7 @@ class CelebA(VisionDataset):
         target_transform: Optional[Callable] = None,
         download: bool = False,
     ) -> None:
-        super(CelebA, self).__init__(root, transform=transform, target_transform=target_transform)
+        super().__init__(root, transform=transform, target_transform=target_transform)
         self.split = split
         if isinstance(target_type, list):
             self.target_type = target_type
@@ -80,7 +80,7 @@ class CelebA(VisionDataset):
             self.download()
 
         if not self._check_integrity():
-            raise RuntimeError("Dataset not found or corrupted." + " You can use download=True to download it")
+            raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")
 
         split_map = {
             "train": 0,
@@ -166,7 +166,7 @@ class CelebA(VisionDataset):
                 target.append(self.landmarks_align[index, :])
             else:
                 # TODO: refactor with utils.verify_str_arg
-                raise ValueError('Target type "{}" is not recognized.'.format(t))
+                raise ValueError(f'Target type "{t}" is not recognized.')
 
         if self.transform is not None:
             X = self.transform(X)

torchvision/datasets/cifar.py

@@ -58,7 +58,7 @@ class CIFAR10(VisionDataset):
         download: bool = False,
     ) -> None:
 
-        super(CIFAR10, self).__init__(root, transform=transform, target_transform=target_transform)
+        super().__init__(root, transform=transform, target_transform=target_transform)
 
         self.train = train  # training set or test set
 
@@ -66,7 +66,7 @@ class CIFAR10(VisionDataset):
             self.download()
 
         if not self._check_integrity():
-            raise RuntimeError("Dataset not found or corrupted." + " You can use download=True to download it")
+            raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")
 
         if self.train:
             downloaded_list = self.train_list
@@ -95,9 +95,7 @@ class CIFAR10(VisionDataset):
     def _load_meta(self) -> None:
         path = os.path.join(self.root, self.base_folder, self.meta["filename"])
         if not check_integrity(path, self.meta["md5"]):
-            raise RuntimeError(
-                "Dataset metadata file not found or corrupted." + " You can use download=True to download it"
-            )
+            raise RuntimeError("Dataset metadata file not found or corrupted. You can use download=True to download it")
         with open(path, "rb") as infile:
             data = pickle.load(infile, encoding="latin1")
             self.classes = data[self.meta["key"]]
@@ -144,7 +142,8 @@ class CIFAR10(VisionDataset):
         download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.tgz_md5)
 
     def extra_repr(self) -> str:
-        return "Split: {}".format("Train" if self.train is True else "Test")
+        split = "Train" if self.train is True else "Test"
+        return f"Split: {split}"
 
 
 class CIFAR100(CIFAR10):

torchvision/datasets/cityscapes.py

@@ -111,7 +111,7 @@ class Cityscapes(VisionDataset):
         target_transform: Optional[Callable] = None,
         transforms: Optional[Callable] = None,
     ) -> None:
-        super(Cityscapes, self).__init__(root, transforms, transform, target_transform)
+        super().__init__(root, transforms, transform, target_transform)
         self.mode = "gtFine" if mode == "fine" else "gtCoarse"
         self.images_dir = os.path.join(self.root, "leftImg8bit", split)
         self.targets_dir = os.path.join(self.root, self.mode, split)
@@ -125,7 +125,7 @@ class Cityscapes(VisionDataset):
             valid_modes = ("train", "test", "val")
         else:
             valid_modes = ("train", "train_extra", "val")
-        msg = "Unknown value '{}' for argument split if mode is '{}'. " "Valid values are {{{}}}."
+        msg = "Unknown value '{}' for argument split if mode is '{}'. Valid values are {{{}}}."
         msg = msg.format(split, mode, iterable_to_str(valid_modes))
         verify_str_arg(split, "split", valid_modes, msg)
 
@@ -139,14 +139,14 @@ class Cityscapes(VisionDataset):
         if not os.path.isdir(self.images_dir) or not os.path.isdir(self.targets_dir):
 
             if split == "train_extra":
-                image_dir_zip = os.path.join(self.root, "leftImg8bit{}".format("_trainextra.zip"))
+                image_dir_zip = os.path.join(self.root, "leftImg8bit_trainextra.zip")
             else:
-                image_dir_zip = os.path.join(self.root, "leftImg8bit{}".format("_trainvaltest.zip"))
+                image_dir_zip = os.path.join(self.root, "leftImg8bit_trainvaltest.zip")
 
             if self.mode == "gtFine":
-                target_dir_zip = os.path.join(self.root, "{}{}".format(self.mode, "_trainvaltest.zip"))
+                target_dir_zip = os.path.join(self.root, f"{self.mode}_trainvaltest.zip")
             elif self.mode == "gtCoarse":
-                target_dir_zip = os.path.join(self.root, "{}{}".format(self.mode, ".zip"))
+                target_dir_zip = os.path.join(self.root, f"{self.mode}.zip")
 
             if os.path.isfile(image_dir_zip) and os.path.isfile(target_dir_zip):
                 extract_archive(from_path=image_dir_zip, to_path=self.root)
@@ -206,16 +206,16 @@ class Cityscapes(VisionDataset):
         return "\n".join(lines).format(**self.__dict__)
 
     def _load_json(self, path: str) -> Dict[str, Any]:
-        with open(path, "r") as file:
+        with open(path) as file:
             data = json.load(file)
         return data
 
     def _get_target_suffix(self, mode: str, target_type: str) -> str:
         if target_type == "instance":
-            return "{}_instanceIds.png".format(mode)
+            return f"{mode}_instanceIds.png"
         elif target_type == "semantic":
-            return "{}_labelIds.png".format(mode)
+            return f"{mode}_labelIds.png"
         elif target_type == "color":
-            return "{}_color.png".format(mode)
+            return f"{mode}_color.png"
         else:
-            return "{}_polygons.json".format(mode)
+            return f"{mode}_polygons.json"

torchvision/datasets/fakedata.py

@@ -31,9 +31,7 @@ class FakeData(VisionDataset):
         target_transform: Optional[Callable] = None,
         random_offset: int = 0,
     ) -> None:
-        super(FakeData, self).__init__(
-            None, transform=transform, target_transform=target_transform  # type: ignore[arg-type]
-        )
+        super().__init__(None, transform=transform, target_transform=target_transform)  # type: ignore[arg-type]
         self.size = size
         self.num_classes = num_classes
         self.image_size = image_size
@@ -49,7 +47,7 @@ class FakeData(VisionDataset):
         """
         # create random image that is consistent with the index id
         if index >= len(self):
-            raise IndexError("{} index out of range".format(self.__class__.__name__))
+            raise IndexError(f"{self.__class__.__name__} index out of range")
         rng_state = torch.get_rng_state()
         torch.manual_seed(index + self.random_offset)
         img = torch.randn(*self.image_size)

torchvision/datasets/flickr.py

@@ -13,7 +13,7 @@ class Flickr8kParser(HTMLParser):
     """Parser for extracting captions from the Flickr8k dataset web page."""
 
     def __init__(self, root: str) -> None:
-        super(Flickr8kParser, self).__init__()
+        super().__init__()
 
         self.root = root
 
@@ -71,7 +71,7 @@ class Flickr8k(VisionDataset):
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
     ) -> None:
-        super(Flickr8k, self).__init__(root, transform=transform, target_transform=target_transform)
+        super().__init__(root, transform=transform, target_transform=target_transform)
         self.ann_file = os.path.expanduser(ann_file)
 
         # Read annotations and store in a dict
@@ -127,7 +127,7 @@ class Flickr30k(VisionDataset):
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
     ) -> None:
-        super(Flickr30k, self).__init__(root, transform=transform, target_transform=target_transform)
+        super().__init__(root, transform=transform, target_transform=target_transform)
         self.ann_file = os.path.expanduser(ann_file)
 
         # Read annotations and store in a dict

torchvision/datasets/folder.py

@@ -140,7 +140,7 @@ class DatasetFolder(VisionDataset):
         target_transform: Optional[Callable] = None,
         is_valid_file: Optional[Callable[[str], bool]] = None,
     ) -> None:
-        super(DatasetFolder, self).__init__(root, transform=transform, target_transform=target_transform)
+        super().__init__(root, transform=transform, target_transform=target_transform)
         classes, class_to_idx = self.find_classes(self.root)
         samples = self.make_dataset(self.root, class_to_idx, extensions, is_valid_file)
 
@@ -254,7 +254,7 @@ def accimage_loader(path: str) -> Any:
 
     try:
         return accimage.Image(path)
-    except IOError:
+    except OSError:
         # Potentially a decoding problem, fall back to PIL.Image
         return pil_loader(path)
 
@@ -306,7 +306,7 @@ class ImageFolder(DatasetFolder):
         loader: Callable[[str], Any] = default_loader,
         is_valid_file: Optional[Callable[[str], bool]] = None,
     ):
-        super(ImageFolder, self).__init__(
+        super().__init__(
             root,
             loader,
             IMG_EXTENSIONS if is_valid_file is None else None,

torchvision/datasets/hmdb51.py

@@ -72,9 +72,9 @@ class HMDB51(VisionDataset):
         _video_min_dimension: int = 0,
         _audio_samples: int = 0,
     ) -> None:
-        super(HMDB51, self).__init__(root)
+        super().__init__(root)
         if fold not in (1, 2, 3):
-            raise ValueError("fold should be between 1 and 3, got {}".format(fold))
+            raise ValueError(f"fold should be between 1 and 3, got {fold}")
 
         extensions = ("avi",)
         self.classes, class_to_idx = find_classes(self.root)
@@ -113,7 +113,7 @@ class HMDB51(VisionDataset):
 
     def _select_fold(self, video_list: List[str], annotations_dir: str, fold: int, train: bool) -> List[int]:
         target_tag = self.TRAIN_TAG if train else self.TEST_TAG
-        split_pattern_name = "*test_split{}.txt".format(fold)
+        split_pattern_name = f"*test_split{fold}.txt"
         split_pattern_path = os.path.join(annotations_dir, split_pattern_name)
         annotation_paths = glob.glob(split_pattern_path)
         selected_files = set()

torchvision/datasets/imagenet.py

@@ -49,7 +49,7 @@ class ImageNet(ImageFolder):
             )
             raise RuntimeError(msg)
         elif download is False:
-            msg = "The use of the download flag is deprecated, since the dataset " "is no longer publicly accessible."
+            msg = "The use of the download flag is deprecated, since the dataset is no longer publicly accessible."
             warnings.warn(msg, RuntimeWarning)
 
         root = self.root = os.path.expanduser(root)
@@ -58,7 +58,7 @@ class ImageNet(ImageFolder):
         self.parse_archives()
         wnid_to_classes = load_meta_file(self.root)[0]
 
-        super(ImageNet, self).__init__(self.split_folder, **kwargs)
+        super().__init__(self.split_folder, **kwargs)
         self.root = root
 
         self.wnids = self.classes
@@ -132,7 +132,7 @@ def parse_devkit_archive(root: str, file: Optional[str] = None) -> None:
 
     def parse_val_groundtruth_txt(devkit_root: str) -> List[int]:
         file = os.path.join(devkit_root, "data", "ILSVRC2012_validation_ground_truth.txt")
-        with open(file, "r") as txtfh:
+        with open(file) as txtfh:
             val_idcs = txtfh.readlines()
         return [int(val_idx) for val_idx in val_idcs]
 
@@ -215,7 +215,7 @@ def parse_val_archive(
     val_root = os.path.join(root, folder)
     extract_archive(os.path.join(root, file), val_root)
 
-    images = sorted([os.path.join(val_root, image) for image in os.listdir(val_root)])
+    images = sorted(os.path.join(val_root, image) for image in os.listdir(val_root))
 
     for wnid in set(wnids):
         os.mkdir(os.path.join(val_root, wnid))

torchvision/datasets/inaturalist.py

@@ -74,16 +74,14 @@ class INaturalist(VisionDataset):
     ) -> None:
         self.version = verify_str_arg(version, "version", DATASET_URLS.keys())
 
-        super(INaturalist, self).__init__(
-            os.path.join(root, version), transform=transform, target_transform=target_transform
-        )
+        super().__init__(os.path.join(root, version), transform=transform, target_transform=target_transform)
 
         os.makedirs(root, exist_ok=True)
         if download:
             self.download()
 
         if not self._check_integrity():
-            raise RuntimeError("Dataset not found or corrupted." + " You can use download=True to download it")
+            raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")
 
         self.all_categories: List[str] = []
 

torchvision/datasets/kinetics.py

@@ -175,7 +175,7 @@ class Kinetics(VisionDataset):
         split_url_filepath = path.join(file_list_path, path.basename(split_url))
         if not check_integrity(split_url_filepath):
             download_url(split_url, file_list_path)
-        list_video_urls = open(split_url_filepath, "r")
+        list_video_urls = open(split_url_filepath)
 
         if self.num_download_workers == 1:
             for line in list_video_urls.readlines():
@@ -309,7 +309,7 @@ class Kinetics400(Kinetics):
                 "Kinetics400. Please use Kinetics instead."
             )
 
-        super(Kinetics400, self).__init__(
+        super().__init__(
             root=root,
             frames_per_clip=frames_per_clip,
             _legacy=True,

torchvision/datasets/lfw.py

@@ -39,9 +39,7 @@ class _LFW(VisionDataset):
         target_transform: Optional[Callable] = None,
         download: bool = False,
     ):
-        super(_LFW, self).__init__(
-            os.path.join(root, self.base_folder), transform=transform, target_transform=target_transform
-        )
+        super().__init__(os.path.join(root, self.base_folder), transform=transform, target_transform=target_transform)
 
         self.image_set = verify_str_arg(image_set.lower(), "image_set", self.file_dict.keys())
         images_dir, self.filename, self.md5 = self.file_dict[self.image_set]
@@ -55,7 +53,7 @@ class _LFW(VisionDataset):
             self.download()
 
         if not self._check_integrity():
-            raise RuntimeError("Dataset not found or corrupted." + " You can use download=True to download it")
+            raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")
 
         self.images_dir = os.path.join(self.root, images_dir)
 
@@ -122,14 +120,14 @@ class LFWPeople(_LFW):
         target_transform: Optional[Callable] = None,
         download: bool = False,
     ):
-        super(LFWPeople, self).__init__(root, split, image_set, "people", transform, target_transform, download)
+        super().__init__(root, split, image_set, "people", transform, target_transform, download)
 
         self.class_to_idx = self._get_classes()
         self.data, self.targets = self._get_people()
 
     def _get_people(self):
         data, targets = [], []
-        with open(os.path.join(self.root, self.labels_file), "r") as f:
+        with open(os.path.join(self.root, self.labels_file)) as f:
             lines = f.readlines()
             n_folds, s = (int(lines[0]), 1) if self.split == "10fold" else (1, 0)
 
@@ -146,7 +144,7 @@ class LFWPeople(_LFW):
         return data, targets
 
     def _get_classes(self):
-        with open(os.path.join(self.root, self.names), "r") as f:
+        with open(os.path.join(self.root, self.names)) as f:
             lines = f.readlines()
             names = [line.strip().split()[0] for line in lines]
         class_to_idx = {name: i for i, name in enumerate(names)}
@@ -172,7 +170,7 @@ class LFWPeople(_LFW):
         return img, target
 
     def extra_repr(self) -> str:
-        return super().extra_repr() + "\nClasses (identities): {}".format(len(self.class_to_idx))
+        return super().extra_repr() + f"\nClasses (identities): {len(self.class_to_idx)}"
 
 
 class LFWPairs(_LFW):
@@ -204,13 +202,13 @@ class LFWPairs(_LFW):
         target_transform: Optional[Callable] = None,
         download: bool = False,
     ):
-        super(LFWPairs, self).__init__(root, split, image_set, "pairs", transform, target_transform, download)
+        super().__init__(root, split, image_set, "pairs", transform, target_transform, download)
 
         self.pair_names, self.data, self.targets = self._get_pairs(self.images_dir)
 
     def _get_pairs(self, images_dir):
         pair_names, data, targets = [], [], []
-        with open(os.path.join(self.root, self.labels_file), "r") as f:
+        with open(os.path.join(self.root, self.labels_file)) as f:
             lines = f.readlines()
             if self.split == "10fold":
                 n_folds, n_pairs = lines[0].split("\t")

torchvision/datasets/lsun.py

@@ -18,7 +18,7 @@ class LSUNClass(VisionDataset):
     ) -> None:
         import lmdb
 
-        super(LSUNClass, self).__init__(root, transform=transform, target_transform=target_transform)
+        super().__init__(root, transform=transform, target_transform=target_transform)
 
         self.env = lmdb.open(root, max_readers=1, readonly=True, lock=False, readahead=False, meminit=False)
         with self.env.begin(write=False) as txn:
@@ -77,7 +77,7 @@ class LSUN(VisionDataset):
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
     ) -> None:
-        super(LSUN, self).__init__(root, transform=transform, target_transform=target_transform)
+        super().__init__(root, transform=transform, target_transform=target_transform)
         self.classes = self._verify_classes(classes)
 
         # for each class, create an LSUNClassDataset
@@ -117,11 +117,11 @@ class LSUN(VisionDataset):
                 classes = [c + "_" + classes for c in categories]
         except ValueError:
             if not isinstance(classes, Iterable):
-                msg = "Expected type str or Iterable for argument classes, " "but got type {}."
+                msg = "Expected type str or Iterable for argument classes, but got type {}."
                 raise ValueError(msg.format(type(classes)))
 
             classes = list(classes)
-            msg_fmtstr_type = "Expected type str for elements in argument classes, " "but got type {}."
+            msg_fmtstr_type = "Expected type str for elements in argument classes, but got type {}."
             for c in classes:
                 verify_str_arg(c, custom_msg=msg_fmtstr_type.format(type(c)))
                 c_short = c.split("_")

torchvision/datasets/mnist.py

@@ -88,7 +88,7 @@ class MNIST(VisionDataset):
         target_transform: Optional[Callable] = None,
         download: bool = False,
     ) -> None:
-        super(MNIST, self).__init__(root, transform=transform, target_transform=target_transform)
+        super().__init__(root, transform=transform, target_transform=target_transform)
         self.train = train  # training set or test set
 
         if self._check_legacy_exist():
@@ -99,7 +99,7 @@ class MNIST(VisionDataset):
             self.download()
 
         if not self._check_exists():
-            raise RuntimeError("Dataset not found." + " You can use download=True to download it")
+            raise RuntimeError("Dataset not found. You can use download=True to download it")
 
         self.data, self.targets = self._load_data()
 
@@ -181,21 +181,22 @@ class MNIST(VisionDataset):
         # download files
         for filename, md5 in self.resources:
             for mirror in self.mirrors:
-                url = "{}{}".format(mirror, filename)
+                url = f"{mirror}{filename}"
                 try:
-                    print("Downloading {}".format(url))
+                    print(f"Downloading {url}")
                     download_and_extract_archive(url, download_root=self.raw_folder, filename=filename, md5=md5)
                 except URLError as error:
-                    print("Failed to download (trying next):\n{}".format(error))
+                    print(f"Failed to download (trying next):\n{error}")
                     continue
                 finally:
                     print()
                 break
             else:
-                raise RuntimeError("Error downloading {}".format(filename))
+                raise RuntimeError(f"Error downloading {filename}")
 
     def extra_repr(self) -> str:
-        return "Split: {}".format("Train" if self.train is True else "Test")
+        split = "Train" if self.train is True else "Test"
+        return f"Split: {split}"
 
 
 class FashionMNIST(MNIST):
@@ -293,16 +294,16 @@ class EMNIST(MNIST):
         self.split = verify_str_arg(split, "split", self.splits)
         self.training_file = self._training_file(split)
         self.test_file = self._test_file(split)
-        super(EMNIST, self).__init__(root, **kwargs)
+        super().__init__(root, **kwargs)
         self.classes = self.classes_split_dict[self.split]
 
     @staticmethod
     def _training_file(split) -> str:
-        return "training_{}.pt".format(split)
+        return f"training_{split}.pt"
 
     @staticmethod
     def _test_file(split) -> str:
-        return "test_{}.pt".format(split)
+        return f"test_{split}.pt"
 
     @property
     def _file_prefix(self) -> str:
@@ -424,7 +425,7 @@ class QMNIST(MNIST):
         self.data_file = what + ".pt"
         self.training_file = self.data_file
         self.test_file = self.data_file
-        super(QMNIST, self).__init__(root, train, **kwargs)
+        super().__init__(root, train, **kwargs)
 
     @property
     def images_file(self) -> str:
@@ -482,7 +483,7 @@ class QMNIST(MNIST):
         return img, target
 
     def extra_repr(self) -> str:
-        return "Split: {}".format(self.what)
+        return f"Split: {self.what}"
 
 
 def get_int(b: bytes) -> int:

torchvision/datasets/omniglot.py

@@ -39,19 +39,19 @@ class Omniglot(VisionDataset):
         target_transform: Optional[Callable] = None,
         download: bool = False,
     ) -> None:
-        super(Omniglot, self).__init__(join(root, self.folder), transform=transform, target_transform=target_transform)
+        super().__init__(join(root, self.folder), transform=transform, target_transform=target_transform)
         self.background = background
 
         if download:
             self.download()
 
         if not self._check_integrity():
-            raise RuntimeError("Dataset not found or corrupted." + " You can use download=True to download it")
+            raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")
 
         self.target_folder = join(self.root, self._get_target_folder())
         self._alphabets = list_dir(self.target_folder)
         self._characters: List[str] = sum(
-            [[join(a, c) for c in list_dir(join(self.target_folder, a))] for a in self._alphabets], []
+            ([join(a, c) for c in list_dir(join(self.target_folder, a))] for a in self._alphabets), []
         )
         self._character_images = [
             [(image, idx) for image in list_files(join(self.target_folder, character), ".png")]

torchvision/datasets/phototour.py

@@ -89,11 +89,11 @@ class PhotoTour(VisionDataset):
     def __init__(
         self, root: str, name: str, train: bool = True, transform: Optional[Callable] = None, download: bool = False
     ) -> None:
-        super(PhotoTour, self).__init__(root, transform=transform)
+        super().__init__(root, transform=transform)
         self.name = name
         self.data_dir = os.path.join(self.root, name)
-        self.data_down = os.path.join(self.root, "{}.zip".format(name))
-        self.data_file = os.path.join(self.root, "{}.pt".format(name))
+        self.data_down = os.path.join(self.root, f"{name}.zip")
+        self.data_file = os.path.join(self.root, f"{name}.pt")
 
         self.train = train
         self.mean = self.means[name]
@@ -139,7 +139,7 @@ class PhotoTour(VisionDataset):
 
     def download(self) -> None:
         if self._check_datafile_exists():
-            print("# Found cached data {}".format(self.data_file))
+            print(f"# Found cached data {self.data_file}")
             return
 
         if not self._check_downloaded():
@@ -151,7 +151,7 @@ class PhotoTour(VisionDataset):
 
             download_url(url, self.root, filename, md5)
 
-            print("# Extracting data {}\n".format(self.data_down))
+            print(f"# Extracting data {self.data_down}\n")
 
             import zipfile
 
@@ -162,7 +162,7 @@ class PhotoTour(VisionDataset):
 
     def cache(self) -> None:
         # process and save as torch files
-        print("# Caching data {}".format(self.data_file))
+        print(f"# Caching data {self.data_file}")
 
         dataset = (
             read_image_file(self.data_dir, self.image_ext, self.lens[self.name]),
@@ -174,7 +174,8 @@ class PhotoTour(VisionDataset):
             torch.save(dataset, f)
 
     def extra_repr(self) -> str:
-        return "Split: {}".format("Train" if self.train is True else "Test")
+        split = "Train" if self.train is True else "Test"
+        return f"Split: {split}"
 
 
 def read_image_file(data_dir: str, image_ext: str, n: int) -> torch.Tensor:
@@ -209,7 +210,7 @@ def read_info_file(data_dir: str, info_file: str) -> torch.Tensor:
     """Return a Tensor containing the list of labels
     Read the file and keep only the ID of the 3D point.
     """
-    with open(os.path.join(data_dir, info_file), "r") as f:
+    with open(os.path.join(data_dir, info_file)) as f:
         labels = [int(line.split()[0]) for line in f]
     return torch.LongTensor(labels)
 
@@ -220,7 +221,7 @@ def read_matches_files(data_dir: str, matches_file: str) -> torch.Tensor:
     Matches are represented with a 1, non matches with a 0.
     """
     matches = []
-    with open(os.path.join(data_dir, matches_file), "r") as f:
+    with open(os.path.join(data_dir, matches_file)) as f:
         for line in f:
             line_split = line.split()
             matches.append([int(line_split[0]), int(line_split[3]), int(line_split[1] == line_split[4])])

torchvision/datasets/places365.py

@@ -117,7 +117,7 @@ class Places365(VisionDataset):
         if not self._check_integrity(file, md5, download):
             self.download_devkit()
 
-        with open(file, "r") as fh:
+        with open(file) as fh:
             class_to_idx = dict(process(line) for line in fh)
 
         return sorted(class_to_idx.keys()), class_to_idx
@@ -132,7 +132,7 @@ class Places365(VisionDataset):
         if not self._check_integrity(file, md5, download):
             self.download_devkit()
 
-        with open(file, "r") as fh:
+        with open(file) as fh:
             images = [process(line) for line in fh]
 
         _, targets = zip(*images)