lint

imaginairy/modules/distributions.py

@@ -24,23 +24,24 @@ class DiagonalGaussianDistribution:
     def kl(self, other=None):
         if self.deterministic:
             return torch.Tensor([0.0])
-        else:
-            if other is None:
-                return 0.5 * torch.sum(
-                    torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
-                    dim=[1, 2, 3],
-                )
-            else:
-                return 0.5 * torch.sum(
-                    torch.pow(self.mean - other.mean, 2) / other.var
-                    + self.var / other.var
-                    - 1.0
-                    - self.logvar
-                    + other.logvar,
-                    dim=[1, 2, 3],
-                )
-
-    def nll(self, sample, dims=[1, 2, 3]):
+
+        if other is None:
+            return 0.5 * torch.sum(
+                torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
+                dim=[1, 2, 3],
+            )
+
+        return 0.5 * torch.sum(
+            torch.pow(self.mean - other.mean, 2) / other.var
+            + self.var / other.var
+            - 1.0
+            - self.logvar
+            + other.logvar,
+            dim=[1, 2, 3],
+        )
+
+    def nll(self, sample, dims=None):
+        dims = dims if dims is None else [1, 2, 3]
         if self.deterministic:
             return torch.Tensor([0.0])
         logtwopi = np.log(2.0 * np.pi)