add typehints for torchvision.datasets.svhn (#2539)

torchvision/datasets/svhn.py

@@ -3,6 +3,7 @@ from PIL import Image
 import os
 import os.path
 import numpy as np
+from typing import Any, Callable, Optional, Tuple
 from .utils import download_url, check_integrity, verify_str_arg
 
 
@@ -39,8 +40,14 @@ class SVHN(VisionDataset):
         'extra': ["http://ufldl.stanford.edu/housenumbers/extra_32x32.mat",
                   "extra_32x32.mat", "a93ce644f1a588dc4d68dda5feec44a7"]}
 
-    def __init__(self, root, split='train', transform=None, target_transform=None,
-                 download=False):
+    def __init__(
+            self,
+            root: str,
+            split: str = "train",
+            transform: Optional[Callable] = None,
+            target_transform: Optional[Callable] = None,
+            download: bool = False,
+    ) -> None:
         super(SVHN, self).__init__(root, transform=transform,
                                    target_transform=target_transform)
         self.split = verify_str_arg(split, "split", tuple(self.split_list.keys()))
@@ -75,7 +82,7 @@ class SVHN(VisionDataset):
         np.place(self.labels, self.labels == 10, 0)
         self.data = np.transpose(self.data, (3, 2, 0, 1))
 
-    def __getitem__(self, index):
+    def __getitem__(self, index: int) -> Tuple[Any, Any]:
         """
         Args:
             index (int): Index
@@ -97,18 +104,18 @@ class SVHN(VisionDataset):
 
         return img, target
 
-    def __len__(self):
+    def __len__(self) -> int:
         return len(self.data)
 
-    def _check_integrity(self):
+    def _check_integrity(self) -> bool:
         root = self.root
         md5 = self.split_list[self.split][2]
         fpath = os.path.join(root, self.filename)
         return check_integrity(fpath, md5)
 
-    def download(self):
+    def download(self) -> None:
         md5 = self.split_list[self.split][2]
         download_url(self.url, self.root, self.filename, md5)
 
-    def extra_repr(self):
+    def extra_repr(self) -> str:
         return "Split: {split}".format(**self.__dict__)