Fix augmentation space to be uint8 compatible (#4806)

* change range into uint8

* Same typo in TrivialAugmentWide and RandAugment

Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>
Co-authored-by: Prabhat Roy <prabhatroy@fb.com>

torchvision/transforms/autoaugment.py

@@ -194,7 +194,7 @@ def _augmentation_space(self, num_bins: int, image_size: List[int]) -> Dict[str,
             "Contrast": (torch.linspace(0.0, 0.9, num_bins), True),
             "Sharpness": (torch.linspace(0.0, 0.9, num_bins), True),
             "Posterize": (8 - (torch.arange(num_bins) / ((num_bins - 1) / 4)).round().int(), False),
-            "Solarize": (torch.linspace(256.0, 0.0, num_bins), False),
+            "Solarize": (torch.linspace(255.0, 0.0, num_bins), False),
             "AutoContrast": (torch.tensor(0.0), False),
             "Equalize": (torch.tensor(0.0), False),
             "Invert": (torch.tensor(0.0), False),
@@ -287,7 +287,7 @@ def _augmentation_space(self, num_bins: int, image_size: List[int]) -> Dict[str,
             "Contrast": (torch.linspace(0.0, 0.9, num_bins), True),
             "Sharpness": (torch.linspace(0.0, 0.9, num_bins), True),
             "Posterize": (8 - (torch.arange(num_bins) / ((num_bins - 1) / 4)).round().int(), False),
-            "Solarize": (torch.linspace(256.0, 0.0, num_bins), False),
+            "Solarize": (torch.linspace(255.0, 0.0, num_bins), False),
             "AutoContrast": (torch.tensor(0.0), False),
             "Equalize": (torch.tensor(0.0), False),
         }
@@ -366,7 +366,7 @@ def _augmentation_space(self, num_bins: int) -> Dict[str, Tuple[Tensor, bool]]:
             "Contrast": (torch.linspace(0.0, 0.99, num_bins), True),
             "Sharpness": (torch.linspace(0.0, 0.99, num_bins), True),
             "Posterize": (8 - (torch.arange(num_bins) / ((num_bins - 1) / 6)).round().int(), False),
-            "Solarize": (torch.linspace(256.0, 0.0, num_bins), False),
+            "Solarize": (torch.linspace(255.0, 0.0, num_bins), False),
             "AutoContrast": (torch.tensor(0.0), False),
             "Equalize": (torch.tensor(0.0), False),
         }