Add docformatter to pre-commit (#5279)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>

ultralytics/utils/callbacks/base.py

@@ -1,7 +1,5 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
-"""
-Base callbacks
-"""
+"""Base callbacks."""
 
 from collections import defaultdict
 from copy import deepcopy

ultralytics/utils/callbacks/comet.py

@@ -26,31 +26,38 @@ except (ImportError, AssertionError):
 
 
 def _get_comet_mode():
+    """Returns the mode of comet set in the environment variables, defaults to 'online' if not set."""
     return os.getenv('COMET_MODE', 'online')
 
 
 def _get_comet_model_name():
+    """Returns the model name for Comet from the environment variable 'COMET_MODEL_NAME' or defaults to 'YOLOv8'."""
     return os.getenv('COMET_MODEL_NAME', 'YOLOv8')
 
 
 def _get_eval_batch_logging_interval():
+    """Get the evaluation batch logging interval from environment variable or use default value 1."""
     return int(os.getenv('COMET_EVAL_BATCH_LOGGING_INTERVAL', 1))
 
 
 def _get_max_image_predictions_to_log():
+    """Get the maximum number of image predictions to log from the environment variables."""
     return int(os.getenv('COMET_MAX_IMAGE_PREDICTIONS', 100))
 
 
 def _scale_confidence_score(score):
+    """Scales the given confidence score by a factor specified in an environment variable."""
     scale = float(os.getenv('COMET_MAX_CONFIDENCE_SCORE', 100.0))
     return score * scale
 
 
 def _should_log_confusion_matrix():
+    """Determines if the confusion matrix should be logged based on the environment variable settings."""
     return os.getenv('COMET_EVAL_LOG_CONFUSION_MATRIX', 'false').lower() == 'true'
 
 
 def _should_log_image_predictions():
+    """Determines whether to log image predictions based on a specified environment variable."""
     return os.getenv('COMET_EVAL_LOG_IMAGE_PREDICTIONS', 'true').lower() == 'true'
 
 
@@ -104,9 +111,10 @@ def _fetch_trainer_metadata(trainer):
 
 
 def _scale_bounding_box_to_original_image_shape(box, resized_image_shape, original_image_shape, ratio_pad):
-    """YOLOv8 resizes images during training and the label values
-    are normalized based on this resized shape. This function rescales the
-    bounding box labels to the original image shape.
+    """
+    YOLOv8 resizes images during training and the label values are normalized based on this resized shape.
+
+    This function rescales the bounding box labels to the original image shape.
     """
 
     resized_image_height, resized_image_width = resized_image_shape

ultralytics/utils/callbacks/dvc.py

@@ -25,6 +25,7 @@ except (ImportError, AssertionError, TypeError):
 
 
 def _log_images(path, prefix=''):
+    """Logs images at specified path with an optional prefix using DVCLive."""
     if live:
         name = path.name
 
@@ -38,6 +39,7 @@ def _log_images(path, prefix=''):
 
 
 def _log_plots(plots, prefix=''):
+    """Logs plot images for training progress if they have not been previously processed."""
     for name, params in plots.items():
         timestamp = params['timestamp']
         if _processed_plots.get(name) != timestamp:
@@ -46,6 +48,7 @@ def _log_plots(plots, prefix=''):
 
 
 def _log_confusion_matrix(validator):
+    """Logs the confusion matrix for the given validator using DVCLive."""
     targets = []
     preds = []
     matrix = validator.confusion_matrix.matrix
@@ -62,6 +65,7 @@ def _log_confusion_matrix(validator):
 
 
 def on_pretrain_routine_start(trainer):
+    """Initializes DVCLive logger for training metadata during pre-training routine."""
     try:
         global live
         live = dvclive.Live(save_dvc_exp=True, cache_images=True)
@@ -71,20 +75,24 @@ def on_pretrain_routine_start(trainer):
 
 
 def on_pretrain_routine_end(trainer):
+    """Logs plots related to the training process at the end of the pretraining routine."""
     _log_plots(trainer.plots, 'train')
 
 
 def on_train_start(trainer):
+    """Logs the training parameters if DVCLive logging is active."""
     if live:
         live.log_params(trainer.args)
 
 
 def on_train_epoch_start(trainer):
+    """Sets the global variable _training_epoch value to True at the start of training each epoch."""
     global _training_epoch
     _training_epoch = True
 
 
 def on_fit_epoch_end(trainer):
+    """Logs training metrics and model info, and advances to next step on the end of each fit epoch."""
     global _training_epoch
     if live and _training_epoch:
         all_metrics = {**trainer.label_loss_items(trainer.tloss, prefix='train'), **trainer.metrics, **trainer.lr}
@@ -104,6 +112,7 @@ def on_fit_epoch_end(trainer):
 
 
 def on_train_end(trainer):
+    """Logs the best metrics, plots, and confusion matrix at the end of training if DVCLive is active."""
     if live:
         # At the end log the best metrics. It runs validator on the best model internally.
         all_metrics = {**trainer.label_loss_items(trainer.tloss, prefix='train'), **trainer.metrics, **trainer.lr}

ultralytics/utils/callbacks/neptune.py

@@ -31,14 +31,13 @@ def _log_images(imgs_dict, group=''):
 
 
 def _log_plot(title, plot_path):
-    """Log plots to the NeptuneAI experiment logger."""
     """
-        Log image as plot in the plot section of NeptuneAI
+    Log plots to the NeptuneAI experiment logger.
 
-        arguments:
-        title (str) Title of the plot
-        plot_path (PosixPath or str) Path to the saved image file
-        """
+    Args:
+        title (str): Title of the plot.
+        plot_path (PosixPath | str): Path to the saved image file.
+    """
     import matplotlib.image as mpimg
     import matplotlib.pyplot as plt
 

ultralytics/utils/callbacks/wb.py

@@ -17,6 +17,7 @@ except (ImportError, AssertionError):
 
 
 def _log_plots(plots, step):
+    """Logs plots from the input dictionary if they haven't been logged already at the specified step."""
     for name, params in plots.items():
         timestamp = params['timestamp']
         if _processed_plots.get(name) != timestamp: