ultralytics 8.0.80 single-line docstring fixes (#2060)

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

ultralytics/yolo/v8/classify/train.py

@@ -46,7 +46,7 @@ class ClassificationTrainer(BaseTrainer):
         """
         load/create/download model for any task
         """
-        # classification models require special handling
+        # Classification models require special handling
 
         if isinstance(self.model, torch.nn.Module):  # if model is loaded beforehand. No setup needed
             return

ultralytics/yolo/v8/detect/train.py

@@ -22,8 +22,8 @@ from ultralytics.yolo.utils.torch_utils import de_parallel
 class DetectionTrainer(BaseTrainer):
 
     def get_dataloader(self, dataset_path, batch_size, rank=0, mode='train'):
-        # TODO: manage splits differently
-        # calculate stride - check if model is initialized
+        """TODO: manage splits differently."""
+        # Calculate stride - check if model is initialized
         gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
         return create_dataloader(path=dataset_path,
                                  imgsz=self.args.imgsz,
@@ -48,7 +48,7 @@ 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

ultralytics/yolo/v8/detect/val.py

@@ -67,7 +67,7 @@ class DetectionValidator(BaseValidator):
         return preds
 
     def update_metrics(self, preds, batch):
-        # Metrics
+        """Metrics."""
         for si, pred in enumerate(preds):
             idx = batch['batch_idx'] == si
             cls = batch['cls'][idx]
@@ -164,8 +164,8 @@ class DetectionValidator(BaseValidator):
         return torch.tensor(correct, dtype=torch.bool, device=detections.device)
 
     def get_dataloader(self, dataset_path, batch_size):
-        # TODO: manage splits differently
-        # calculate stride - check if model is initialized
+        """TODO: manage splits differently."""
+        # Calculate stride - check if model is initialized
         gs = max(int(de_parallel(self.model).stride if self.model else 0), 32)
         return create_dataloader(path=dataset_path,
                                  imgsz=self.args.imgsz,

ultralytics/yolo/v8/pose/val.py

@@ -47,7 +47,7 @@ class PoseValidator(DetectionValidator):
         self.sigma = OKS_SIGMA if is_pose else np.ones(nkpt) / nkpt
 
     def update_metrics(self, preds, batch):
-        # Metrics
+        """Metrics."""
         for si, pred in enumerate(preds):
             idx = batch['batch_idx'] == si
             cls = batch['cls'][idx]

ultralytics/yolo/v8/segment/predict.py

@@ -10,7 +10,7 @@ from ultralytics.yolo.v8.detect.predict import DetectionPredictor
 class SegmentationPredictor(DetectionPredictor):
 
     def postprocess(self, preds, img, orig_imgs):
-        # TODO: filter by classes
+        """TODO: filter by classes."""
         p = ops.non_max_suppression(preds[0],
                                     self.args.conf,
                                     self.args.iou,

ultralytics/yolo/v8/segment/train.py

@@ -140,7 +140,7 @@ class SegLoss(Loss):
         return loss.sum() * batch_size, loss.detach()  # loss(box, cls, dfl)
 
     def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
-        # Mask loss for one image
+        """Mask loss for one image."""
         pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:])  # (n, 32) @ (32,80,80) -> (n,80,80)
         loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction='none')
         return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()

ultralytics/yolo/v8/segment/val.py

@@ -52,7 +52,7 @@ class SegmentationValidator(DetectionValidator):
         return p, proto
 
     def update_metrics(self, preds, batch):
-        # Metrics
+        """Metrics."""
         for si, (pred, proto) in enumerate(zip(preds[0], preds[1])):
             idx = batch['batch_idx'] == si
             cls = batch['cls'][idx]
@@ -179,7 +179,7 @@ class SegmentationValidator(DetectionValidator):
         self.plot_masks.clear()
 
     def pred_to_json(self, predn, filename, pred_masks):
-        # Save one JSON result
+        """Save one JSON result."""
         # Example result = {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
         from pycocotools.mask import encode  # noqa