Ruff Docstring formatting (#15793)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

ultralytics/engine/exporter.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 """
-Export a YOLOv8 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
+Export a YOLOv8 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit.
 
 Format                  | `format=argument`         | Model
 ---                     | ---                       | ---
@@ -533,9 +533,7 @@ class Exporter:
 
     @try_export
     def export_ncnn(self, prefix=colorstr("NCNN:")):
-        """
-        YOLOv8 NCNN export using PNNX https://github.com/pnnx/pnnx.
-        """
+        """YOLOv8 NCNN export using PNNX https://github.com/pnnx/pnnx."""
         check_requirements("ncnn")
         import ncnn  # noqa
 

ultralytics/engine/predictor.py

@@ -384,7 +384,7 @@ class BasePredictor:
             cv2.imwrite(save_path, im)
 
     def show(self, p=""):
-        """Display an image in a window using OpenCV imshow()."""
+        """Display an image in a window using the OpenCV imshow function."""
         im = self.plotted_img
         if platform.system() == "Linux" and p not in self.windows:
             self.windows.append(p)

ultralytics/engine/trainer.py

@@ -228,7 +228,6 @@ class BaseTrainer:
 
     def _setup_train(self, world_size):
         """Builds dataloaders and optimizer on correct rank process."""
-
         # Model
         self.run_callbacks("on_pretrain_routine_start")
         ckpt = self.setup_model()
@@ -638,7 +637,7 @@ class BaseTrainer:
         pass
 
     def on_plot(self, name, data=None):
-        """Registers plots (e.g. to be consumed in callbacks)"""
+        """Registers plots (e.g. to be consumed in callbacks)."""
         path = Path(name)
         self.plots[path] = {"data": data, "timestamp": time.time()}
 
@@ -737,7 +736,6 @@ class BaseTrainer:
         Returns:
             (torch.optim.Optimizer): The constructed optimizer.
         """
-
         g = [], [], []  # optimizer parameter groups
         bn = tuple(v for k, v in nn.__dict__.items() if "Norm" in k)  # normalization layers, i.e. BatchNorm2d()
         if name == "auto":

ultralytics/engine/tuner.py

@@ -1,7 +1,7 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 """
-This module provides functionalities for hyperparameter tuning of the Ultralytics YOLO models for object detection,
-instance segmentation, image classification, pose estimation, and multi-object tracking.
+Module provides functionalities for hyperparameter tuning of the Ultralytics YOLO models for object detection, instance
+segmentation, image classification, pose estimation, and multi-object tracking.
 
 Hyperparameter tuning is the process of systematically searching for the optimal set of hyperparameters
 that yield the best model performance. This is particularly crucial in deep learning models like YOLO,
@@ -176,7 +176,6 @@ class Tuner:
            The method utilizes the `self.tune_csv` Path object to read and log hyperparameters and fitness scores.
            Ensure this path is set correctly in the Tuner instance.
         """
-
         t0 = time.time()
         best_save_dir, best_metrics = None, None
         (self.tune_dir / "weights").mkdir(parents=True, exist_ok=True)

ultralytics/engine/validator.py

@@ -104,9 +104,7 @@ class BaseValidator:
 
     @smart_inference_mode()
     def __call__(self, trainer=None, model=None):
-        """Supports validation of a pre-trained model if passed or a model being trained if trainer is passed (trainer
-        gets priority).
-        """
+        """Executes validation process, running inference on dataloader and computing performance metrics."""
         self.training = trainer is not None
         augment = self.args.augment and (not self.training)
         if self.training:
@@ -280,7 +278,7 @@ class BaseValidator:
         return batch
 
     def postprocess(self, preds):
-        """Describes and summarizes the purpose of 'postprocess()' but no details mentioned."""
+        """Preprocesses the predictions."""
         return preds
 
     def init_metrics(self, model):
@@ -317,7 +315,7 @@ class BaseValidator:
         return []
 
     def on_plot(self, name, data=None):
-        """Registers plots (e.g. to be consumed in callbacks)"""
+        """Registers plots (e.g. to be consumed in callbacks)."""
         self.plots[Path(name)] = {"data": data, "timestamp": time.time()}
 
     # TODO: may need to put these following functions into callback