Document that datasets support pathlib.Path (#8321)

Co-authored-by: Philip Meier <github.pmeier@posteo.de>

torchvision/datasets/_optical_flow.py

@@ -13,7 +13,6 @@ from ..io.image import _read_png_16
 from .utils import _read_pfm, verify_str_arg
 from .vision import VisionDataset
 
-
 T1 = Tuple[Image.Image, Image.Image, Optional[np.ndarray], Optional[np.ndarray]]
 T2 = Tuple[Image.Image, Image.Image, Optional[np.ndarray]]
 
@@ -33,7 +32,7 @@ class FlowDataset(ABC, VisionDataset):
     # and it's up to whatever consumes the dataset to decide what valid_flow_mask should be.
     _has_builtin_flow_mask = False
 
-    def __init__(self, root: str, transforms: Optional[Callable] = None) -> None:
+    def __init__(self, root: Union[str, Path], transforms: Optional[Callable] = None) -> None:
 
         super().__init__(root=root)
         self.transforms = transforms
@@ -113,7 +112,7 @@ class Sintel(FlowDataset):
                         ...
 
     Args:
-        root (string): Root directory of the Sintel Dataset.
+        root (str or ``pathlib.Path``): Root directory of the Sintel Dataset.
         split (string, optional): The dataset split, either "train" (default) or "test"
         pass_name (string, optional): The pass to use, either "clean" (default), "final", or "both". See link above for
             details on the different passes.
@@ -125,7 +124,7 @@ class Sintel(FlowDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, Path],
         split: str = "train",
         pass_name: str = "clean",
         transforms: Optional[Callable] = None,
@@ -183,7 +182,7 @@ class KittiFlow(FlowDataset):
                     flow_occ
 
     Args:
-        root (string): Root directory of the KittiFlow Dataset.
+        root (str or ``pathlib.Path``): Root directory of the KittiFlow Dataset.
         split (string, optional): The dataset split, either "train" (default) or "test"
         transforms (callable, optional): A function/transform that takes in
             ``img1, img2, flow, valid_flow_mask`` and returns a transformed version.
@@ -191,7 +190,7 @@ class KittiFlow(FlowDataset):
 
     _has_builtin_flow_mask = True
 
-    def __init__(self, root: str, split: str = "train", transforms: Optional[Callable] = None) -> None:
+    def __init__(self, root: Union[str, Path], split: str = "train", transforms: Optional[Callable] = None) -> None:
         super().__init__(root=root, transforms=transforms)
 
         verify_str_arg(split, "split", valid_values=("train", "test"))
@@ -248,7 +247,7 @@ class FlyingChairs(FlowDataset):
 
 
     Args:
-        root (string): Root directory of the FlyingChairs Dataset.
+        root (str or ``pathlib.Path``): Root directory of the FlyingChairs Dataset.
         split (string, optional): The dataset split, either "train" (default) or "val"
         transforms (callable, optional): A function/transform that takes in
             ``img1, img2, flow, valid_flow_mask`` and returns a transformed version.
@@ -256,7 +255,7 @@ class FlyingChairs(FlowDataset):
             return a built-in valid mask, such as :class:`~torchvision.datasets.KittiFlow`.
     """
 
-    def __init__(self, root: str, split: str = "train", transforms: Optional[Callable] = None) -> None:
+    def __init__(self, root: Union[str, Path], split: str = "train", transforms: Optional[Callable] = None) -> None:
         super().__init__(root=root, transforms=transforms)
 
         verify_str_arg(split, "split", valid_values=("train", "val"))
@@ -316,7 +315,7 @@ class FlyingThings3D(FlowDataset):
                     TRAIN
 
     Args:
-        root (string): Root directory of the intel FlyingThings3D Dataset.
+        root (str or ``pathlib.Path``): Root directory of the intel FlyingThings3D Dataset.
         split (string, optional): The dataset split, either "train" (default) or "test"
         pass_name (string, optional): The pass to use, either "clean" (default) or "final" or "both". See link above for
             details on the different passes.
@@ -329,7 +328,7 @@ class FlyingThings3D(FlowDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, Path],
         split: str = "train",
         pass_name: str = "clean",
         camera: str = "left",
@@ -411,7 +410,7 @@ class HD1K(FlowDataset):
                     image_2
 
     Args:
-        root (string): Root directory of the HD1K Dataset.
+        root (str or ``pathlib.Path``): Root directory of the HD1K Dataset.
         split (string, optional): The dataset split, either "train" (default) or "test"
         transforms (callable, optional): A function/transform that takes in
             ``img1, img2, flow, valid_flow_mask`` and returns a transformed version.
@@ -419,7 +418,7 @@ class HD1K(FlowDataset):
 
     _has_builtin_flow_mask = True
 
-    def __init__(self, root: str, split: str = "train", transforms: Optional[Callable] = None) -> None:
+    def __init__(self, root: Union[str, Path], split: str = "train", transforms: Optional[Callable] = None) -> None:
         super().__init__(root=root, transforms=transforms)
 
         verify_str_arg(split, "split", valid_values=("train", "test"))

torchvision/datasets/_stereo_matching.py

@@ -27,7 +27,7 @@ class StereoMatchingDataset(ABC, VisionDataset):
 
     _has_built_in_disparity_mask = False
 
-    def __init__(self, root: str, transforms: Optional[Callable] = None) -> None:
+    def __init__(self, root: Union[str, Path], transforms: Optional[Callable] = None) -> None:
         """
         Args:
             root(str): Root directory of the dataset.
@@ -159,11 +159,11 @@ class CarlaStereo(StereoMatchingDataset):
                     ...
 
     Args:
-        root (string): Root directory where `carla-highres` is located.
+        root (str or ``pathlib.Path``): Root directory where `carla-highres` is located.
         transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version.
     """
 
-    def __init__(self, root: str, transforms: Optional[Callable] = None) -> None:
+    def __init__(self, root: Union[str, Path], transforms: Optional[Callable] = None) -> None:
         super().__init__(root, transforms)
 
         root = Path(root) / "carla-highres"
@@ -233,14 +233,14 @@ class Kitti2012Stereo(StereoMatchingDataset):
                     calib
 
     Args:
-        root (string): Root directory where `Kitti2012` is located.
+        root (str or ``pathlib.Path``): Root directory where `Kitti2012` is located.
         split (string, optional): The dataset split of scenes, either "train" (default) or "test".
         transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version.
     """
 
     _has_built_in_disparity_mask = True
 
-    def __init__(self, root: str, split: str = "train", transforms: Optional[Callable] = None) -> None:
+    def __init__(self, root: Union[str, Path], split: str = "train", transforms: Optional[Callable] = None) -> None:
         super().__init__(root, transforms)
 
         verify_str_arg(split, "split", valid_values=("train", "test"))
@@ -321,14 +321,14 @@ class Kitti2015Stereo(StereoMatchingDataset):
                     calib
 
     Args:
-        root (string): Root directory where `Kitti2015` is located.
+        root (str or ``pathlib.Path``): Root directory where `Kitti2015` is located.
         split (string, optional): The dataset split of scenes, either "train" (default) or "test".
         transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version.
     """
 
     _has_built_in_disparity_mask = True
 
-    def __init__(self, root: str, split: str = "train", transforms: Optional[Callable] = None) -> None:
+    def __init__(self, root: Union[str, Path], split: str = "train", transforms: Optional[Callable] = None) -> None:
         super().__init__(root, transforms)
 
         verify_str_arg(split, "split", valid_values=("train", "test"))
@@ -420,7 +420,7 @@ class Middlebury2014Stereo(StereoMatchingDataset):
                     ...
 
     Args:
-        root (string): Root directory of the Middleburry 2014 Dataset.
+        root (str or ``pathlib.Path``): Root directory of the Middleburry 2014 Dataset.
         split (string, optional): The dataset split of scenes, either "train" (default), "test", or "additional"
         use_ambient_views (boolean, optional): Whether to use different expose or lightning views when possible.
             The dataset samples with equal probability between ``[im1.png, im1E.png, im1L.png]``.
@@ -480,7 +480,7 @@ class Middlebury2014Stereo(StereoMatchingDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, Path],
         split: str = "train",
         calibration: Optional[str] = "perfect",
         use_ambient_views: bool = False,
@@ -576,7 +576,7 @@ class Middlebury2014Stereo(StereoMatchingDataset):
         valid_mask = (disparity_map > 0).squeeze(0)  # mask out invalid disparities
         return disparity_map, valid_mask
 
-    def _download_dataset(self, root: str) -> None:
+    def _download_dataset(self, root: Union[str, Path]) -> None:
         base_url = "https://vision.middlebury.edu/stereo/data/scenes2014/zip"
         # train and additional splits have 2 different calibration settings
         root = Path(root) / "Middlebury2014"
@@ -675,7 +675,7 @@ class CREStereo(StereoMatchingDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, Path],
         transforms: Optional[Callable] = None,
     ) -> None:
         super().__init__(root, transforms)
@@ -757,12 +757,12 @@ class FallingThingsStereo(StereoMatchingDataset):
                     ...
 
     Args:
-        root (string): Root directory where FallingThings is located.
+        root (str or ``pathlib.Path``): Root directory where FallingThings is located.
         variant (string): Which variant to use. Either "single", "mixed", or "both".
         transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version.
     """
 
-    def __init__(self, root: str, variant: str = "single", transforms: Optional[Callable] = None) -> None:
+    def __init__(self, root: Union[str, Path], variant: str = "single", transforms: Optional[Callable] = None) -> None:
         super().__init__(root, transforms)
 
         root = Path(root) / "FallingThings"
@@ -868,7 +868,7 @@ class SceneFlowStereo(StereoMatchingDataset):
                     ...
 
     Args:
-        root (string): Root directory where SceneFlow is located.
+        root (str or ``pathlib.Path``): Root directory where SceneFlow is located.
         variant (string): Which dataset variant to user, "FlyingThings3D" (default), "Monkaa" or "Driving".
         pass_name (string): Which pass to use, "clean" (default), "final" or "both".
         transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version.
@@ -877,7 +877,7 @@ class SceneFlowStereo(StereoMatchingDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, Path],
         variant: str = "FlyingThings3D",
         pass_name: str = "clean",
         transforms: Optional[Callable] = None,
@@ -973,14 +973,14 @@ class SintelStereo(StereoMatchingDataset):
                         ...
 
     Args:
-        root (string): Root directory where Sintel Stereo is located.
+        root (str or ``pathlib.Path``): Root directory where Sintel Stereo is located.
         pass_name (string): The name of the pass to use, either "final", "clean" or "both".
         transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version.
     """
 
     _has_built_in_disparity_mask = True
 
-    def __init__(self, root: str, pass_name: str = "final", transforms: Optional[Callable] = None) -> None:
+    def __init__(self, root: Union[str, Path], pass_name: str = "final", transforms: Optional[Callable] = None) -> None:
         super().__init__(root, transforms)
 
         verify_str_arg(pass_name, "pass_name", valid_values=("final", "clean", "both"))
@@ -1082,12 +1082,12 @@ class InStereo2k(StereoMatchingDataset):
                     ...
 
     Args:
-        root (string): Root directory where InStereo2k is located.
+        root (str or ``pathlib.Path``): Root directory where InStereo2k is located.
         split (string): Either "train" or "test".
         transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version.
     """
 
-    def __init__(self, root: str, split: str = "train", transforms: Optional[Callable] = None) -> None:
+    def __init__(self, root: Union[str, Path], split: str = "train", transforms: Optional[Callable] = None) -> None:
         super().__init__(root, transforms)
 
         root = Path(root) / "InStereo2k" / split
@@ -1169,14 +1169,14 @@ class ETH3DStereo(StereoMatchingDataset):
                     ...
 
     Args:
-        root (string): Root directory of the ETH3D Dataset.
+        root (str or ``pathlib.Path``): Root directory of the ETH3D Dataset.
         split (string, optional): The dataset split of scenes, either "train" (default) or "test".
         transforms (callable, optional): A function/transform that takes in a sample and returns a transformed version.
     """
 
     _has_built_in_disparity_mask = True
 
-    def __init__(self, root: str, split: str = "train", transforms: Optional[Callable] = None) -> None:
+    def __init__(self, root: Union[str, Path], split: str = "train", transforms: Optional[Callable] = None) -> None:
         super().__init__(root, transforms)
 
         verify_str_arg(split, "split", valid_values=("train", "test"))

torchvision/datasets/caltech.py

 import os
 import os.path
+from pathlib import Path
 from typing import Any, Callable, List, Optional, Tuple, Union
 
 from PIL import Image
@@ -16,7 +17,7 @@ class Caltech101(VisionDataset):
         This class needs `scipy <https://docs.scipy.org/doc/>`_ to load target files from `.mat` format.
 
     Args:
-        root (string): Root directory of dataset where directory
+        root (str or ``pathlib.Path``): Root directory of dataset where directory
             ``caltech101`` exists or will be saved to if download is set to True.
         target_type (string or list, optional): Type of target to use, ``category`` or
             ``annotation``. Can also be a list to output a tuple with all specified
@@ -38,7 +39,7 @@ class Caltech101(VisionDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, Path],
         target_type: Union[List[str], str] = "category",
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
@@ -153,7 +154,7 @@ class Caltech256(VisionDataset):
     """`Caltech 256 <https://data.caltech.edu/records/20087>`_ Dataset.
 
     Args:
-        root (string): Root directory of dataset where directory
+        root (str or ``pathlib.Path``): Root directory of dataset where directory
             ``caltech256`` exists or will be saved to if download is set to True.
         transform (callable, optional): A function/transform that takes in a PIL image
             and returns a transformed version. E.g, ``transforms.RandomCrop``

torchvision/datasets/celeba.py

 import csv
 import os
 from collections import namedtuple
+from pathlib import Path
 from typing import Any, Callable, List, Optional, Tuple, Union
 
 import PIL
@@ -16,7 +17,7 @@ class CelebA(VisionDataset):
     """`Large-scale CelebFaces Attributes (CelebA) Dataset <http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html>`_ Dataset.
 
     Args:
-        root (string): Root directory where images are downloaded to.
+        root (str or ``pathlib.Path``): Root directory where images are downloaded to.
         split (string): One of {'train', 'valid', 'test', 'all'}.
             Accordingly dataset is selected.
         target_type (string or list, optional): Type of target to use, ``attr``, ``identity``, ``bbox``,
@@ -63,7 +64,7 @@ class CelebA(VisionDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, Path],
         split: str = "train",
         target_type: Union[List[str], str] = "attr",
         transform: Optional[Callable] = None,

torchvision/datasets/cifar.py

 import os.path
 import pickle
-from typing import Any, Callable, Optional, Tuple
+from pathlib import Path
+from typing import Any, Callable, Optional, Tuple, Union
 
 import numpy as np
 from PIL import Image
@@ -13,7 +14,7 @@ class CIFAR10(VisionDataset):
     """`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
 
     Args:
-        root (string): Root directory of dataset where directory
+        root (str or ``pathlib.Path``): Root directory of dataset where directory
             ``cifar-10-batches-py`` exists or will be saved to if download is set to True.
         train (bool, optional): If True, creates dataset from training set, otherwise
             creates from test set.
@@ -50,7 +51,7 @@ class CIFAR10(VisionDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, Path],
         train: bool = True,
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,

torchvision/datasets/cityscapes.py

 import json
 import os
 from collections import namedtuple
+from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 from PIL import Image
@@ -13,7 +14,7 @@ class Cityscapes(VisionDataset):
     """`Cityscapes <http://www.cityscapes-dataset.com/>`_ Dataset.
 
     Args:
-        root (string): Root directory of dataset where directory ``leftImg8bit``
+        root (str or ``pathlib.Path``): Root directory of dataset where directory ``leftImg8bit``
             and ``gtFine`` or ``gtCoarse`` are located.
         split (string, optional): The image split to use, ``train``, ``test`` or ``val`` if mode="fine"
             otherwise ``train``, ``train_extra`` or ``val``
@@ -103,7 +104,7 @@ class Cityscapes(VisionDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, Path],
         split: str = "train",
         mode: str = "fine",
         target_type: Union[List[str], str] = "instance",

torchvision/datasets/clevr.py

 import json
 import pathlib
-from typing import Any, Callable, List, Optional, Tuple
+from typing import Any, Callable, List, Optional, Tuple, Union
 from urllib.parse import urlparse
 
 from PIL import Image
@@ -15,7 +15,7 @@ class CLEVRClassification(VisionDataset):
     The number of objects in a scene are used as label.
 
     Args:
-        root (string): Root directory of dataset where directory ``root/clevr`` exists or will be saved to if download is
+        root (str or ``pathlib.Path``): Root directory of dataset where directory ``root/clevr`` exists or will be saved to if download is
             set to True.
         split (string, optional): The dataset split, supports ``"train"`` (default), ``"val"``, or ``"test"``.
         transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
@@ -30,7 +30,7 @@ class CLEVRClassification(VisionDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, pathlib.Path],
         split: str = "train",
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,

torchvision/datasets/coco.py

 import os.path
-from typing import Any, Callable, List, Optional, Tuple
+from pathlib import Path
+from typing import Any, Callable, List, Optional, Tuple, Union
 
 from PIL import Image
 
@@ -12,7 +13,7 @@ class CocoDetection(VisionDataset):
     It requires the `COCO API to be installed <https://github.com/pdollar/coco/tree/master/PythonAPI>`_.
 
     Args:
-        root (string): Root directory where images are downloaded to.
+        root (str or ``pathlib.Path``): Root directory where images are downloaded to.
         annFile (string): Path to json annotation file.
         transform (callable, optional): A function/transform that takes in a PIL image
             and returns a transformed version. E.g, ``transforms.PILToTensor``
@@ -24,7 +25,7 @@ class CocoDetection(VisionDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, Path],
         annFile: str,
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
@@ -67,7 +68,7 @@ class CocoCaptions(CocoDetection):
     It requires the `COCO API to be installed <https://github.com/pdollar/coco/tree/master/PythonAPI>`_.
 
     Args:
-        root (string): Root directory where images are downloaded to.
+        root (str or ``pathlib.Path``): Root directory where images are downloaded to.
         annFile (string): Path to json annotation file.
         transform (callable, optional): A function/transform that  takes in a PIL image
             and returns a transformed version. E.g, ``transforms.PILToTensor``

torchvision/datasets/country211.py

 from pathlib import Path
-from typing import Callable, Optional
+from typing import Callable, Optional, Union
 
 from .folder import ImageFolder
 from .utils import download_and_extract_archive, verify_str_arg
@@ -14,7 +14,7 @@ class Country211(ImageFolder):
     100 test images for each country.
 
     Args:
-        root (string): Root directory of the dataset.
+        root (str or ``pathlib.Path``): Root directory of the dataset.
         split (string, optional): The dataset split, supports ``"train"`` (default), ``"valid"`` and ``"test"``.
         transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
             version. E.g, ``transforms.RandomCrop``.
@@ -28,7 +28,7 @@ class Country211(ImageFolder):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, Path],
         split: str = "train",
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,

torchvision/datasets/dtd.py

 import os
 import pathlib
-from typing import Any, Callable, Optional, Tuple
+from typing import Any, Callable, Optional, Tuple, Union
 
 import PIL.Image
 
@@ -12,7 +12,7 @@ class DTD(VisionDataset):
     """`Describable Textures Dataset (DTD) <https://www.robots.ox.ac.uk/~vgg/data/dtd/>`_.
 
     Args:
-        root (string): Root directory of the dataset.
+        root (str or ``pathlib.Path``): Root directory of the dataset.
         split (string, optional): The dataset split, supports ``"train"`` (default), ``"val"``, or ``"test"``.
         partition (int, optional): The dataset partition. Should be ``1 <= partition <= 10``. Defaults to ``1``.
 
@@ -34,7 +34,7 @@ class DTD(VisionDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, pathlib.Path],
         split: str = "train",
         partition: int = 1,
         transform: Optional[Callable] = None,

torchvision/datasets/eurosat.py

 import os
-from typing import Callable, Optional
+from pathlib import Path
+from typing import Callable, Optional, Union
 
 from .folder import ImageFolder
 from .utils import download_and_extract_archive
@@ -9,7 +10,7 @@ class EuroSAT(ImageFolder):
     """RGB version of the `EuroSAT <https://github.com/phelber/eurosat>`_ Dataset.
 
     Args:
-        root (string): Root directory of dataset where ``root/eurosat`` exists.
+        root (str or ``pathlib.Path``): Root directory of dataset where ``root/eurosat`` exists.
         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
@@ -21,7 +22,7 @@ class EuroSAT(ImageFolder):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, Path],
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
         download: bool = False,

torchvision/datasets/fer2013.py

 import csv
 import pathlib
-from typing import Any, Callable, Optional, Tuple
+from typing import Any, Callable, Optional, Tuple, Union
 
 import torch
 from PIL import Image
@@ -14,7 +14,7 @@ class FER2013(VisionDataset):
     <https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge>`_ Dataset.
 
     Args:
-        root (string): Root directory of dataset where directory
+        root (str or ``pathlib.Path``): Root directory of dataset where directory
             ``root/fer2013`` exists.
         split (string, optional): The dataset split, supports ``"train"`` (default), or ``"test"``.
         transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
@@ -29,7 +29,7 @@ class FER2013(VisionDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, pathlib.Path],
         split: str = "train",
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,

torchvision/datasets/fgvc_aircraft.py

 from __future__ import annotations
 
 import os
-from typing import Any, Callable, Optional, Tuple
+from pathlib import Path
+from typing import Any, Callable, Optional, Tuple, Union
 
 import PIL.Image
 
@@ -23,7 +24,7 @@ class FGVCAircraft(VisionDataset):
     - ``manufacturer``, e.g. Boeing. The dataset comprises 30 different manufacturers.
 
     Args:
-        root (string): Root directory of the FGVC Aircraft dataset.
+        root (str or ``pathlib.Path``): 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``,
@@ -41,7 +42,7 @@ class FGVCAircraft(VisionDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, Path],
         split: str = "trainval",
         annotation_level: str = "variant",
         transform: Optional[Callable] = None,

torchvision/datasets/flickr.py

@@ -2,7 +2,8 @@ import glob
 import os
 from collections import defaultdict
 from html.parser import HTMLParser
-from typing import Any, Callable, Dict, List, Optional, Tuple
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 from PIL import Image
 
@@ -12,7 +13,7 @@ from .vision import VisionDataset
 class Flickr8kParser(HTMLParser):
     """Parser for extracting captions from the Flickr8k dataset web page."""
 
-    def __init__(self, root: str) -> None:
+    def __init__(self, root: Union[str, Path]) -> None:
         super().__init__()
 
         self.root = root
@@ -56,7 +57,7 @@ class Flickr8k(VisionDataset):
     """`Flickr8k Entities <http://hockenmaier.cs.illinois.edu/8k-pictures.html>`_ Dataset.
 
     Args:
-        root (string): Root directory where images are downloaded to.
+        root (str or ``pathlib.Path``): Root directory where images are downloaded to.
         ann_file (string): Path to annotation file.
         transform (callable, optional): A function/transform that takes in a PIL image
             and returns a transformed version. E.g, ``transforms.PILToTensor``
@@ -66,7 +67,7 @@ class Flickr8k(VisionDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, Path],
         ann_file: str,
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
@@ -112,7 +113,7 @@ class Flickr30k(VisionDataset):
     """`Flickr30k Entities <https://bryanplummer.com/Flickr30kEntities/>`_ Dataset.
 
     Args:
-        root (string): Root directory where images are downloaded to.
+        root (str or ``pathlib.Path``): Root directory where images are downloaded to.
         ann_file (string): Path to annotation file.
         transform (callable, optional): A function/transform that takes in a PIL image
             and returns a transformed version. E.g, ``transforms.PILToTensor``

torchvision/datasets/flowers102.py

 from pathlib import Path
-from typing import Any, Callable, Optional, Tuple
+from typing import Any, Callable, Optional, Tuple, Union
 
 import PIL.Image
 
@@ -22,7 +22,7 @@ class Flowers102(VisionDataset):
     have large variations within the category, and several very similar categories.
 
     Args:
-        root (string): Root directory of the dataset.
+        root (str or ``pathlib.Path``): Root directory of the dataset.
         split (string, optional): The dataset split, supports ``"train"`` (default), ``"val"``, or ``"test"``.
         transform (callable, optional): A function/transform that takes in a PIL image and returns a
             transformed version. E.g, ``transforms.RandomCrop``.
@@ -42,7 +42,7 @@ class Flowers102(VisionDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, Path],
         split: str = "train",
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,

torchvision/datasets/folder.py

 import os
 import os.path
+from pathlib import Path
 from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Union
 
 from PIL import Image
@@ -32,7 +33,7 @@ def is_image_file(filename: str) -> bool:
     return has_file_allowed_extension(filename, IMG_EXTENSIONS)
 
 
-def find_classes(directory: str) -> Tuple[List[str], Dict[str, int]]:
+def find_classes(directory: Union[str, Path]) -> Tuple[List[str], Dict[str, int]]:
     """Finds the class folders in a dataset.
 
     See :class:`DatasetFolder` for details.
@@ -46,7 +47,7 @@ def find_classes(directory: str) -> Tuple[List[str], Dict[str, int]]:
 
 
 def make_dataset(
-    directory: str,
+    directory: Union[str, Path],
     class_to_idx: Optional[Dict[str, int]] = None,
     extensions: Optional[Union[str, Tuple[str, ...]]] = None,
     is_valid_file: Optional[Callable[[str], bool]] = None,
@@ -112,7 +113,7 @@ class DatasetFolder(VisionDataset):
     :meth:`find_classes` method.
 
     Args:
-        root (string): Root directory path.
+        root (str or ``pathlib.Path``): Root directory path.
         loader (callable): A function to load a sample given its path.
         extensions (tuple[string]): A list of allowed extensions.
             both extensions and is_valid_file should not be passed.
@@ -136,7 +137,7 @@ class DatasetFolder(VisionDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, Path],
         loader: Callable[[str], Any],
         extensions: Optional[Tuple[str, ...]] = None,
         transform: Optional[Callable] = None,
@@ -164,7 +165,7 @@ class DatasetFolder(VisionDataset):
 
     @staticmethod
     def make_dataset(
-        directory: str,
+        directory: Union[str, Path],
         class_to_idx: Dict[str, int],
         extensions: Optional[Tuple[str, ...]] = None,
         is_valid_file: Optional[Callable[[str], bool]] = None,
@@ -203,7 +204,7 @@ class DatasetFolder(VisionDataset):
             directory, class_to_idx, extensions=extensions, is_valid_file=is_valid_file, allow_empty=allow_empty
         )
 
-    def find_classes(self, directory: str) -> Tuple[List[str], Dict[str, int]]:
+    def find_classes(self, directory: Union[str, Path]) -> Tuple[List[str], Dict[str, int]]:
         """Find the class folders in a dataset structured as follows::
 
             directory/
@@ -298,7 +299,7 @@ class ImageFolder(DatasetFolder):
     the same methods can be overridden to customize the dataset.
 
     Args:
-        root (string): Root directory path.
+        root (str or ``pathlib.Path``): Root directory path.
         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

torchvision/datasets/food101.py

 import json
 from pathlib import Path
-from typing import Any, Callable, Optional, Tuple
+from typing import Any, Callable, Optional, Tuple, Union
 
 import PIL.Image
 
@@ -19,7 +19,7 @@ class Food101(VisionDataset):
 
 
     Args:
-        root (string): Root directory of the dataset.
+        root (str or ``pathlib.Path``): Root directory of the dataset.
         split (string, optional): The dataset split, supports ``"train"`` (default) and ``"test"``.
         transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
             version. E.g, ``transforms.RandomCrop``.
@@ -34,7 +34,7 @@ class Food101(VisionDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, Path],
         split: str = "train",
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,

torchvision/datasets/gtsrb.py

 import csv
 import pathlib
-from typing import Any, Callable, Optional, Tuple
+from typing import Any, Callable, Optional, Tuple, Union
 
 import PIL
 
@@ -13,7 +13,7 @@ class GTSRB(VisionDataset):
     """`German Traffic Sign Recognition Benchmark (GTSRB) <https://benchmark.ini.rub.de/>`_ Dataset.
 
     Args:
-        root (string): Root directory of the dataset.
+        root (str or ``pathlib.Path``): 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 a PIL image and returns a transformed
             version. E.g, ``transforms.RandomCrop``.
@@ -25,7 +25,7 @@ class GTSRB(VisionDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, pathlib.Path],
         split: str = "train",
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,

torchvision/datasets/hmdb51.py

 import glob
 import os
-from typing import Any, Callable, Dict, List, Optional, Tuple
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 from torch import Tensor
 
@@ -28,7 +29,7 @@ class HMDB51(VisionDataset):
     Internally, it uses a VideoClips object to handle clip creation.
 
     Args:
-        root (string): Root directory of the HMDB51 Dataset.
+        root (str or ``pathlib.Path``): Root directory of the HMDB51 Dataset.
         annotation_path (str): Path to the folder containing the split files.
         frames_per_clip (int): Number of frames in a clip.
         step_between_clips (int): Number of frames between each clip.
@@ -59,7 +60,7 @@ class HMDB51(VisionDataset):
 
     def __init__(
         self,
-        root: str,
+        root: Union[str, Path],
         annotation_path: str,
         frames_per_clip: int,
         step_between_clips: int = 1,

torchvision/datasets/imagenet.py

@@ -2,7 +2,8 @@ import os
 import shutil
 import tempfile
 from contextlib import contextmanager
-from typing import Any, Dict, Iterator, List, Optional, Tuple
+from pathlib import Path
+from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
 
 import torch
 
@@ -28,7 +29,7 @@ class ImageNet(ImageFolder):
         or ``ILSVRC2012_img_val.tar`` based on ``split`` in the root directory.
 
     Args:
-        root (string): Root directory of the ImageNet Dataset.
+        root (str or ``pathlib.Path``): Root directory of the ImageNet Dataset.
         split (string, optional): The dataset split, supports ``train``, or ``val``.
         transform (callable, optional): A function/transform that takes in a PIL image
             and returns a transformed version. E.g, ``transforms.RandomCrop``
@@ -45,7 +46,7 @@ class ImageNet(ImageFolder):
         targets (list): The class_index value for each image in the dataset
     """
 
-    def __init__(self, root: str, split: str = "train", **kwargs: Any) -> None:
+    def __init__(self, root: Union[str, Path], split: str = "train", **kwargs: Any) -> None:
         root = self.root = os.path.expanduser(root)
         self.split = verify_str_arg(split, "split", ("train", "val"))
 
@@ -78,7 +79,7 @@ class ImageNet(ImageFolder):
         return "Split: {split}".format(**self.__dict__)
 
 
-def load_meta_file(root: str, file: Optional[str] = None) -> Tuple[Dict[str, str], List[str]]:
+def load_meta_file(root: Union[str, Path], file: Optional[str] = None) -> Tuple[Dict[str, str], List[str]]:
     if file is None:
         file = META_FILE
     file = os.path.join(root, file)
@@ -93,7 +94,7 @@ def load_meta_file(root: str, file: Optional[str] = None) -> Tuple[Dict[str, str
         raise RuntimeError(msg.format(file, root))
 
 
-def _verify_archive(root: str, file: str, md5: str) -> None:
+def _verify_archive(root: Union[str, Path], file: str, md5: str) -> None:
     if not check_integrity(os.path.join(root, file), md5):
         msg = (
             "The archive {} is not present in the root directory or is corrupted. "
@@ -102,12 +103,12 @@ def _verify_archive(root: str, file: str, md5: str) -> None:
         raise RuntimeError(msg.format(file, root))
 
 
-def parse_devkit_archive(root: str, file: Optional[str] = None) -> None:
+def parse_devkit_archive(root: Union[str, Path], file: Optional[str] = None) -> None:
     """Parse the devkit archive of the ImageNet2012 classification dataset and save
     the meta information in a binary file.
 
     Args:
-        root (str): Root directory containing the devkit archive
+        root (str or ``pathlib.Path``): Root directory containing the devkit archive
         file (str, optional): Name of devkit archive. Defaults to
             'ILSVRC2012_devkit_t12.tar.gz'
     """
@@ -156,12 +157,12 @@ def parse_devkit_archive(root: str, file: Optional[str] = None) -> None:
         torch.save((wnid_to_classes, val_wnids), os.path.join(root, META_FILE))
 
 
-def parse_train_archive(root: str, file: Optional[str] = None, folder: str = "train") -> None:
+def parse_train_archive(root: Union[str, Path], file: Optional[str] = None, folder: str = "train") -> None:
     """Parse the train images archive of the ImageNet2012 classification dataset and
     prepare it for usage with the ImageNet dataset.
 
     Args:
-        root (str): Root directory containing the train images archive
+        root (str or ``pathlib.Path``): Root directory containing the train images archive
         file (str, optional): Name of train images archive. Defaults to
             'ILSVRC2012_img_train.tar'
         folder (str, optional): Optional name for train images folder. Defaults to
@@ -183,13 +184,13 @@ def parse_train_archive(root: str, file: Optional[str] = None, folder: str = "tr
 
 
 def parse_val_archive(
-    root: str, file: Optional[str] = None, wnids: Optional[List[str]] = None, folder: str = "val"
+    root: Union[str, Path], file: Optional[str] = None, wnids: Optional[List[str]] = None, folder: str = "val"
 ) -> None:
     """Parse the validation images archive of the ImageNet2012 classification dataset
     and prepare it for usage with the ImageNet dataset.
 
     Args:
-        root (str): Root directory containing the validation images archive
+        root (str or ``pathlib.Path``): Root directory containing the validation images archive
         file (str, optional): Name of validation images archive. Defaults to
             'ILSVRC2012_img_val.tar'
         wnids (list, optional): List of WordNet IDs of the validation images. If None