Fix print messages for MS-SSIM (#230)

* Fix print messages for MS-SSIM

* Fix f-strings print (#230)

Author: JessyD

generative/metrics/ms_ssim.py

@@ -80,10 +80,7 @@ def _compute_metric(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
         """
 
         if not x.shape == y.shape:
-            raise ValueError(
-                f"Input images should have the same dimensions, \
-             but got {x.shape} and {y.shape}."
-            )
+            raise ValueError(f"Input images should have the same dimensions, but got {x.shape} and {y.shape}.")
 
         for d in range(len(x.shape) - 1, 1, -1):
             x = x.squeeze(dim=d)
@@ -94,10 +91,7 @@ def _compute_metric(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
         elif len(x.shape) == 5:
             avg_pool = F.avg_pool3d
         else:
-            raise ValueError(
-                f"Input images should be 4-d or 5-d tensors, but \
-                got {x.shape}"
-            )
+            raise ValueError(f"Input images should be 4-d or 5-d tensors, but got {x.shape}")
 
         if self.weights is None:
             # as per Ref 1 - Sec 3.2.
@@ -109,14 +103,14 @@ def _compute_metric(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
         for idx, shape_size in enumerate(x.shape[2:]):
             if shape_size % divisible_by != 0:
                 raise ValueError(
-                    f"Image size needs to be divisible by {divisible_by} but \
-                        dimension {idx + 2} has size {shape_size}"
+                    f"Image size needs to be divisible by {divisible_by} but "
+                    f"dimension {idx + 2} has size {shape_size}"
                 )
 
             if shape_size < bigger_than:
                 raise ValueError(
-                    f"Image size should be larger than {bigger_than} due to \
-                        the {len(self.weights) - 1} downsamplings in MS-SSIM."
+                    f"Image size should be larger than {bigger_than} due to "
+                    f"the {len(self.weights) - 1} downsamplings in MS-SSIM."
                 )
 
         levels = self.weights.shape[0]