ultralytics 8.0.81 single-line docstring updates (#2061)

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

ultralytics/yolo/v8/pose/val.py

@@ -15,20 +15,24 @@ from ultralytics.yolo.v8.detect import DetectionValidator
 class PoseValidator(DetectionValidator):
 
     def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
+        """Initialize a 'PoseValidator' object with custom parameters and assigned attributes."""
         super().__init__(dataloader, save_dir, pbar, args, _callbacks)
         self.args.task = 'pose'
         self.metrics = PoseMetrics(save_dir=self.save_dir)
 
     def preprocess(self, batch):
+        """Preprocesses the batch by converting the 'keypoints' data into a float and moving it to the device."""
         batch = super().preprocess(batch)
         batch['keypoints'] = batch['keypoints'].to(self.device).float()
         return batch
 
     def get_desc(self):
+        """Returns description of evaluation metrics in string format."""
         return ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', 'R', 'mAP50', 'mAP50-95)', 'Pose(P',
                                          'R', 'mAP50', 'mAP50-95)')
 
     def postprocess(self, preds):
+        """Apply non-maximum suppression and return detections with high confidence scores."""
         preds = ops.non_max_suppression(preds,
                                         self.args.conf,
                                         self.args.iou,
@@ -40,6 +44,7 @@ class PoseValidator(DetectionValidator):
         return preds
 
     def init_metrics(self, model):
+        """Initiate pose estimation metrics for YOLO model."""
         super().init_metrics(model)
         self.kpt_shape = self.data['kpt_shape']
         is_pose = self.kpt_shape == [17, 3]
@@ -137,6 +142,7 @@ class PoseValidator(DetectionValidator):
         return torch.tensor(correct, dtype=torch.bool, device=detections.device)
 
     def plot_val_samples(self, batch, ni):
+        """Plots and saves validation set samples with predicted bounding boxes and keypoints."""
         plot_images(batch['img'],
                     batch['batch_idx'],
                     batch['cls'].squeeze(-1),
@@ -147,6 +153,7 @@ class PoseValidator(DetectionValidator):
                     names=self.names)
 
     def plot_predictions(self, batch, preds, ni):
+        """Plots predictions for YOLO model."""
         pred_kpts = torch.cat([p[:, 6:].view(-1, *self.kpt_shape)[:15] for p in preds], 0)
         plot_images(batch['img'],
                     *output_to_target(preds, max_det=15),
@@ -156,6 +163,7 @@ class PoseValidator(DetectionValidator):
                     names=self.names)  # pred
 
     def pred_to_json(self, predn, filename):
+        """Converts YOLO predictions to COCO JSON format."""
         stem = Path(filename).stem
         image_id = int(stem) if stem.isnumeric() else stem
         box = ops.xyxy2xywh(predn[:, :4])  # xywh
@@ -169,6 +177,7 @@ class PoseValidator(DetectionValidator):
                 'score': round(p[4], 5)})
 
     def eval_json(self, stats):
+        """Evaluates object detection model using COCO JSON format."""
         if self.args.save_json and self.is_coco and len(self.jdict):
             anno_json = self.data['path'] / 'annotations/person_keypoints_val2017.json'  # annotations
             pred_json = self.save_dir / 'predictions.json'  # predictions
@@ -197,6 +206,7 @@ class PoseValidator(DetectionValidator):
 
 
 def val(cfg=DEFAULT_CFG, use_python=False):
+    """Performs validation on YOLO model using given data."""
     model = cfg.model or 'yolov8n-pose.pt'
     data = cfg.data or 'coco8-pose.yaml'