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torch_uncertainty/post_processing/abnn.py

@@ -85,9 +85,7 @@ def __init__(
         self.weights = []
         for _ in range(num_models):
             weight = torch.ones([num_classes])
-            weight[torch.randperm(num_classes)[:num_rp_classes]] += (
-                random_prior - 1
-            )
+            weight[torch.randperm(num_classes)[:num_rp_classes]] += random_prior - 1
             self.weights.append(weight)
 
     def fit(self, dataset: Dataset) -> None:
@@ -104,9 +102,7 @@ def fit(self, dataset: Dataset) -> None:
             ClassificationRoutine(
                 num_classes=self.num_classes,
                 model=mod,
-                loss=nn.CrossEntropyLoss(
-                    weight=self.weights[i].to(device=self.device)
-                ),
+                loss=nn.CrossEntropyLoss(weight=self.weights[i].to(device=self.device)),
                 optim_recipe=optim_abnn(mod, lr=self.base_lr),
                 eval_ood=True,
             )
@@ -133,9 +129,7 @@ def fit(self, dataset: Dataset) -> None:
         for baseline in baselines:
             model = copy.deepcopy(source_model)
             model.load_state_dict(baseline.model.state_dict())
-            final_models.extend(
-                [copy.deepcopy(model) for _ in range(self.num_samples)]
-            )
+            final_models.extend([copy.deepcopy(model) for _ in range(self.num_samples)])
 
         self.final_model = deep_ensembles(final_models)
 
@@ -161,31 +155,21 @@ def _abnn_checks(
     batch_size,
 ) -> None:
     if random_prior < 0:
-        raise ValueError(
-            f"random_prior must be greater than 0. Got {random_prior}."
-        )
+        raise ValueError(f"random_prior must be greater than 0. Got {random_prior}.")
     if batch_size < 1:
-        raise ValueError(
-            f"batch_size must be greater than 0. Got {batch_size}."
-        )
+        raise ValueError(f"batch_size must be greater than 0. Got {batch_size}.")
     if max_epochs < 1:
         raise ValueError(f"epoch must be greater than 0. Got {max_epochs}.")
     if num_models < 1:
-        raise ValueError(
-            f"num_models must be greater than 0. Got {num_models}."
-        )
+        raise ValueError(f"num_models must be greater than 0. Got {num_models}.")
     if num_samples < 1:
-        raise ValueError(
-            f"num_samples must be greater than 0. Got {num_samples}."
-        )
+        raise ValueError(f"num_samples must be greater than 0. Got {num_samples}.")
     if alpha < 0:
         raise ValueError(f"alpha must be greater than 0. Got {alpha}.")
     if base_lr < 0:
         raise ValueError(f"base_lr must be greater than 0. Got {base_lr}.")
     if num_classes < 1:
-        raise ValueError(
-            f"num_classes must be greater than 0. Got {num_classes}."
-        )
+        raise ValueError(f"num_classes must be greater than 0. Got {num_classes}.")
 
 
 def _replace_bn_layers(model: nn.Module, alpha: float) -> None: