Fixing mypy errors (#3335)

Summary:
* Fixing mypy errors.

* Fixing typing issue.

Reviewed By: datumbox

Differential Revision: D26226616

fbshipit-source-id: 5ebd98d70d9ae6edc2b8e960c6cea38279fd60c1

Author: NicolasHug

torchvision/datasets/semeion.py

@@ -41,8 +41,6 @@ def __init__(
             raise RuntimeError('Dataset not found or corrupted.' +
                                ' You can use download=True to download it')
 
-        self.data = []
-        self.labels = []
         fp = os.path.join(self.root, self.filename)
         data = np.loadtxt(fp)
         # convert value to 8 bit unsigned integer

torchvision/datasets/stl10.py

@@ -67,7 +67,7 @@ def __init__(
                 'You can use download=True to download it')
 
         # now load the picked numpy arrays
-        self.labels: np.ndarray
+        self.labels: Optional[np.ndarray]
         if self.split == 'train':
             self.data, self.labels = self.__loadfile(
                 self.train_list[0][0], self.train_list[1][0])
@@ -182,4 +182,6 @@ def __load_folds(self, folds: Optional[int]) -> None:
         with open(path_to_folds, 'r') as f:
             str_idx = f.read().splitlines()[folds]
             list_idx = np.fromstring(str_idx, dtype=np.uint8, sep=' ')
-            self.data, self.labels = self.data[list_idx, :, :, :], self.labels[list_idx]
+            self.data = self.data[list_idx, :, :, :]
+            if self.labels is not None:
+                self.labels = self.labels[list_idx]

torchvision/datasets/usps.py

@@ -57,8 +57,8 @@ def __init__(
         import bz2
         with bz2.open(full_path) as fp:
             raw_data = [line.decode().split() for line in fp.readlines()]
-            imgs = [[x.split(':')[-1] for x in data[1:]] for data in raw_data]
-            imgs = np.asarray(imgs, dtype=np.float32).reshape((-1, 16, 16))
+            tmp_list = [[x.split(':')[-1] for x in data[1:]] for data in raw_data]
+            imgs = np.asarray(tmp_list, dtype=np.float32).reshape((-1, 16, 16))
             imgs = ((cast(np.ndarray, imgs) + 1) / 2 * 255).astype(dtype=np.uint8)
             targets = [int(d[0]) - 1 for d in raw_data]