[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -2,7 +2,7 @@ ci:
   skip: [mypy, pytest]
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.4
+    rev: v0.13.1
     hooks:
       - id: ruff
         args: [--fix]

ai_toolkit/datasets/__init__.py

@@ -12,7 +12,7 @@ def get_dataset_initializer(dataset_name: str) -> DatasetLoader:
     """Retrieves class initializer from its string name."""
     if not hasattr(sys.modules[__name__], dataset_name):
         raise RuntimeError(f"Dataset class {dataset_name} not found in datasets/")
-    return cast(DatasetLoader, getattr(sys.modules[__name__], dataset_name)())
+    return cast("DatasetLoader", getattr(sys.modules[__name__], dataset_name)())
 
 
 __all__ = (

ai_toolkit/metrics/__init__.py

@@ -15,7 +15,7 @@ def get_metric_initializer(metric_name: str) -> type[Metric]:
     """Retrieves class initializer from its string name."""
     if not hasattr(sys.modules[__name__], metric_name):
         raise RuntimeError(f"Metric {metric_name} not found in metrics folder.")
-    return cast(type[Metric], getattr(sys.modules[__name__], metric_name))
+    return cast("type[Metric]", getattr(sys.modules[__name__], metric_name))
 
 
 __all__ = (

ai_toolkit/metrics/accuracy.py

@@ -7,7 +7,7 @@
 
 class Accuracy(Metric):
     def __repr__(self) -> str:
-        return f"{self.name}: {100. * self.value:.2f}%"
+        return f"{self.name}: {100.0 * self.value:.2f}%"
 
     @staticmethod
     def calculate_accuracy(output: torch.Tensor, target: torch.Tensor) -> float:

ai_toolkit/metrics/f1_score.py

@@ -30,7 +30,7 @@ def calculate_f1_score(
         f1 = 2 * (precision * recall) / (precision + recall + eps)
         f1 = f1.clamp(min=eps, max=1 - eps)
         f1_score = 1 - f1.mean()
-        return cast(float, f1_score)
+        return cast("float", f1_score)
 
     def update(self, val_dict: SimpleNamespace) -> float:
         y_pred, y_true = val_dict.output, val_dict.target

ai_toolkit/metrics/loss.py

@@ -10,4 +10,4 @@ def update(self, val_dict: SimpleNamespace) -> float:
         self.epoch_avg += loss * val_dict.batch_size
         self.running_avg += loss * val_dict.batch_size
         self.num_examples += val_dict.batch_size
-        return cast(float, loss)
+        return cast("float", loss)

ai_toolkit/models/__init__.py

@@ -19,14 +19,14 @@ def get_model_initializer(model_name: str) -> type[nn.Module]:
     """Retrieves class initializer from its string name."""
     if not hasattr(sys.modules[__name__], model_name):
         raise RuntimeError(f"Model class {model_name} not found in models/")
-    return cast(type[nn.Module], getattr(sys.modules[__name__], model_name))
+    return cast("type[nn.Module]", getattr(sys.modules[__name__], model_name))
 
 
 __all__ = (
     "BasicCNN",
-    "DenseNet",
     "BasicLSTM",
-    "MaskRCNN",
     "BasicRNN",
+    "DenseNet",
+    "MaskRCNN",
     "get_model_initializer",
 )

ai_toolkit/test.py

@@ -46,7 +46,7 @@ def test_model(
     test_loss /= test_len
     print(
         f"\nTest set: Average loss: {test_loss:.4f},",
-        f"Accuracy: {correct}/{test_len} ({100. * correct / test_len:.2f}%)\n",
+        f"Accuracy: {correct}/{test_len} ({100.0 * correct / test_len:.2f}%)\n",
     )