[Lint] Fix remaining lint manually

test/test_transforms.py

@@ -155,5 +155,6 @@ class Tester(unittest.TestCase):
         l, = img.split()
         assert np.allclose(l, img_data[:, :, 0])
 
+
 if __name__ == '__main__':
     unittest.main()

torchvision/datasets/cifar.py

@@ -39,8 +39,8 @@ class CIFAR10(data.Dataset):
             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')
 
         # now load the picked numpy arrays
         if self.train:

torchvision/datasets/lsun.py

@@ -128,9 +128,10 @@ class LSUN(data.Dataset):
     def __repr__(self):
         return self.__class__.__name__ + ' (' + self.db_path + ')'
 
+
 if __name__ == '__main__':
-    #lsun = LSUNClass(db_path='/home/soumith/local/lsun/train/bedroom_train_lmdb')
-    #a = lsun[0]
+    # lsun = LSUNClass(db_path='/home/soumith/local/lsun/train/bedroom_train_lmdb')
+    # a = lsun[0]
     lsun = LSUN(db_path='/home/soumith/local/lsun/train',
                 classes=['bedroom_train', 'church_outdoor_train'])
     print(lsun.classes)

torchvision/datasets/mnist.py

@@ -32,8 +32,8 @@ class MNIST(data.Dataset):
             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')
 
         if self.train:
             self.train_data, self.train_labels = torch.load(

torchvision/transforms.py

@@ -155,7 +155,7 @@ class Lambda(object):
     """Applies a lambda as a transform."""
 
     def __init__(self, lambd):
-        assert type(lambd) is types.LambdaType
+        assert isinstance(lambd, types.LambdaType)
         self.lambd = lambd
 
     def __call__(self, img):