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/models/yolo/classify/predict.py

@@ -26,6 +26,7 @@ class ClassificationPredictor(BasePredictor):
     """
 
     def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        """Initializes ClassificationPredictor setting the task to 'classify'."""
         super().__init__(cfg, overrides, _callbacks)
         self.args.task = 'classify'
 

ultralytics/models/yolo/classify/train.py

@@ -79,6 +79,7 @@ class ClassificationTrainer(BaseTrainer):
         return ckpt
 
     def build_dataset(self, img_path, mode='train', batch=None):
+        """Creates a ClassificationDataset instance given an image path, and mode (train/test etc.)."""
         return ClassificationDataset(root=img_path, args=self.args, augment=mode == 'train', prefix=mode)
 
     def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode='train'):
@@ -113,8 +114,9 @@ class ClassificationTrainer(BaseTrainer):
 
     def label_loss_items(self, loss_items=None, prefix='train'):
         """
-        Returns a loss dict with labelled training loss items tensor. Not needed for classification but necessary for
-        segmentation & detection
+        Returns a loss dict with labelled training loss items tensor.
+
+        Not needed for classification but necessary for segmentation & detection
         """
         keys = [f'{prefix}/{x}' for x in self.loss_names]
         if loss_items is None:

ultralytics/models/yolo/classify/val.py

@@ -78,6 +78,7 @@ class ClassificationValidator(BaseValidator):
         return self.metrics.results_dict
 
     def build_dataset(self, img_path):
+        """Creates and returns a ClassificationDataset instance using given image path and preprocessing parameters."""
         return ClassificationDataset(root=img_path, args=self.args, augment=False, prefix=self.args.split)
 
     def get_dataloader(self, dataset_path, batch_size):