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/segment/train.py

@@ -18,12 +18,14 @@ from ultralytics.yolo.v8.detect.train import Loss
 class SegmentationTrainer(v8.detect.DetectionTrainer):
 
     def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        """Initialize a SegmentationTrainer object with given arguments."""
         if overrides is None:
             overrides = {}
         overrides['task'] = 'segment'
         super().__init__(cfg, overrides, _callbacks)
 
     def get_model(self, cfg=None, weights=None, verbose=True):
+        """Return SegmentationModel initialized with specified config and weights."""
         model = SegmentationModel(cfg, ch=3, nc=self.data['nc'], verbose=verbose and RANK == -1)
         if weights:
             model.load(weights)
@@ -31,15 +33,18 @@ class SegmentationTrainer(v8.detect.DetectionTrainer):
         return model
 
     def get_validator(self):
+        """Return an instance of SegmentationValidator for validation of YOLO model."""
         self.loss_names = 'box_loss', 'seg_loss', 'cls_loss', 'dfl_loss'
         return v8.segment.SegmentationValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))
 
     def criterion(self, preds, batch):
+        """Returns the computed loss using the SegLoss class on the given predictions and batch."""
         if not hasattr(self, 'compute_loss'):
             self.compute_loss = SegLoss(de_parallel(self.model), overlap=self.args.overlap_mask)
         return self.compute_loss(preds, batch)
 
     def plot_training_samples(self, batch, ni):
+        """Creates a plot of training sample images with labels and box coordinates."""
         images = batch['img']
         masks = batch['masks']
         cls = batch['cls'].squeeze(-1)
@@ -49,6 +54,7 @@ class SegmentationTrainer(v8.detect.DetectionTrainer):
         plot_images(images, batch_idx, cls, bboxes, masks, paths=paths, fname=self.save_dir / f'train_batch{ni}.jpg')
 
     def plot_metrics(self):
+        """Plots training/val metrics."""
         plot_results(file=self.csv, segment=True)  # save results.png
 
 
@@ -61,6 +67,7 @@ class SegLoss(Loss):
         self.overlap = overlap
 
     def __call__(self, preds, batch):
+        """Calculate and return the loss for the YOLO model."""
         loss = torch.zeros(4, device=self.device)  # box, cls, dfl
         feats, pred_masks, proto = preds if len(preds) == 3 else preds[1]
         batch_size, _, mask_h, mask_w = proto.shape  # batch size, number of masks, mask height, mask width
@@ -147,6 +154,7 @@ class SegLoss(Loss):
 
 
 def train(cfg=DEFAULT_CFG, use_python=False):
+    """Train a YOLO segmentation model based on passed arguments."""
     model = cfg.model or 'yolov8n-seg.pt'
     data = cfg.data or 'coco128-seg.yaml'  # or yolo.ClassificationDataset("mnist")
     device = cfg.device if cfg.device is not None else ''