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):

ultralytics/models/yolo/detect/train.py

@@ -57,7 +57,7 @@ class DetectionTrainer(BaseTrainer):
         return batch
 
     def set_model_attributes(self):
-        """nl = de_parallel(self.model).model[-1].nl  # number of detection layers (to scale hyps)."""
+        """Nl = de_parallel(self.model).model[-1].nl  # number of detection layers (to scale hyps)."""
         # self.args.box *= 3 / nl  # scale to layers
         # self.args.cls *= self.data["nc"] / 80 * 3 / nl  # scale to classes and layers
         # self.args.cls *= (self.args.imgsz / 640) ** 2 * 3 / nl  # scale to image size and layers
@@ -80,8 +80,9 @@ class DetectionTrainer(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 not None:

ultralytics/models/yolo/model.py

@@ -6,13 +6,11 @@ from ultralytics.nn.tasks import ClassificationModel, DetectionModel, PoseModel,
 
 
 class YOLO(Model):
-    """
-    YOLO (You Only Look Once) object detection model.
-    """
+    """YOLO (You Only Look Once) object detection model."""
 
     @property
     def task_map(self):
-        """Map head to model, trainer, validator, and predictor classes"""
+        """Map head to model, trainer, validator, and predictor classes."""
         return {
             'classify': {
                 'model': ClassificationModel,

ultralytics/models/yolo/pose/predict.py

@@ -21,6 +21,7 @@ class PosePredictor(DetectionPredictor):
     """
 
     def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        """Initializes PosePredictor, sets task to 'pose' and logs a warning for using 'mps' as device."""
         super().__init__(cfg, overrides, _callbacks)
         self.args.task = 'pose'
         if isinstance(self.args.device, str) and self.args.device.lower() == 'mps':

ultralytics/models/yolo/segment/predict.py

@@ -21,10 +21,12 @@ class SegmentationPredictor(DetectionPredictor):
     """
 
     def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        """Initializes the SegmentationPredictor with the provided configuration, overrides, and callbacks."""
         super().__init__(cfg, overrides, _callbacks)
         self.args.task = 'segment'
 
     def postprocess(self, preds, img, orig_imgs):
+        """Applies non-max suppression and processes detections for each image in an input batch."""
         p = ops.non_max_suppression(preds[0],
                                     self.args.conf,
                                     self.args.iou,

ultralytics/models/yolo/segment/val.py

@@ -144,7 +144,7 @@ class SegmentationValidator(DetectionValidator):
 
     def _process_batch(self, detections, labels, pred_masks=None, gt_masks=None, overlap=False, masks=False):
         """
-        Return correct prediction matrix
+        Return correct prediction matrix.
 
         Args:
             detections (array[N, 6]), x1, y1, x2, y2, conf, class