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/classify/val.py

@@ -9,14 +9,17 @@ from ultralytics.yolo.utils.metrics import ClassifyMetrics, ConfusionMatrix
 class ClassificationValidator(BaseValidator):
 
     def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
+        """Initializes ClassificationValidator instance with args, dataloader, save_dir, and progress bar."""
         super().__init__(dataloader, save_dir, pbar, args, _callbacks)
         self.args.task = 'classify'
         self.metrics = ClassifyMetrics()
 
     def get_desc(self):
+        """Returns a formatted string summarizing classification metrics."""
         return ('%22s' + '%11s' * 2) % ('classes', 'top1_acc', 'top5_acc')
 
     def init_metrics(self, model):
+        """Initialize confusion matrix, class names, and top-1 and top-5 accuracy."""
         self.names = model.names
         self.nc = len(model.names)
         self.confusion_matrix = ConfusionMatrix(nc=self.nc, task='classify')
@@ -24,17 +27,20 @@ class ClassificationValidator(BaseValidator):
         self.targets = []
 
     def preprocess(self, batch):
+        """Preprocesses input batch and returns it."""
         batch['img'] = batch['img'].to(self.device, non_blocking=True)
         batch['img'] = batch['img'].half() if self.args.half else batch['img'].float()
         batch['cls'] = batch['cls'].to(self.device)
         return batch
 
     def update_metrics(self, preds, batch):
+        """Updates running metrics with model predictions and batch targets."""
         n5 = min(len(self.model.names), 5)
         self.pred.append(preds.argsort(1, descending=True)[:, :n5])
         self.targets.append(batch['cls'])
 
     def finalize_metrics(self, *args, **kwargs):
+        """Finalizes metrics of the model such as confusion_matrix and speed."""
         self.confusion_matrix.process_cls_preds(self.pred, self.targets)
         if self.args.plots:
             self.confusion_matrix.plot(save_dir=self.save_dir, names=list(self.names.values()))
@@ -42,10 +48,12 @@ class ClassificationValidator(BaseValidator):
         self.metrics.confusion_matrix = self.confusion_matrix
 
     def get_stats(self):
+        """Returns a dictionary of metrics obtained by processing targets and predictions."""
         self.metrics.process(self.targets, self.pred)
         return self.metrics.results_dict
 
     def get_dataloader(self, dataset_path, batch_size):
+        """Builds and returns a data loader for classification tasks with given parameters."""
         return build_classification_dataloader(path=dataset_path,
                                                imgsz=self.args.imgsz,
                                                batch_size=batch_size,
@@ -54,11 +62,13 @@ class ClassificationValidator(BaseValidator):
                                                workers=self.args.workers)
 
     def print_results(self):
+        """Prints evaluation metrics for YOLO object detection model."""
         pf = '%22s' + '%11.3g' * len(self.metrics.keys)  # print format
         LOGGER.info(pf % ('all', self.metrics.top1, self.metrics.top5))
 
 
 def val(cfg=DEFAULT_CFG, use_python=False):
+    """Validate YOLO model using custom data."""
     model = cfg.model or 'yolov8n-cls.pt'  # or "resnet18"
     data = cfg.data or 'mnist160'