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/nn/tasks.py

@@ -167,7 +167,8 @@ class BaseModel(nn.Module):
 
 
 class DetectionModel(BaseModel):
-    # YOLOv8 detection model
+    """YOLOv8 detection model."""
+
     def __init__(self, cfg='yolov8n.yaml', ch=3, nc=None, verbose=True):  # model, input channels, number of classes
         super().__init__()
         self.yaml = cfg if isinstance(cfg, dict) else yaml_model_load(cfg)  # cfg dict
@@ -218,7 +219,7 @@ class DetectionModel(BaseModel):
 
     @staticmethod
     def _descale_pred(p, flips, scale, img_size, dim=1):
-        # de-scale predictions following augmented inference (inverse operation)
+        """De-scale predictions following augmented inference (inverse operation)."""
         p[:, :4] /= scale  # de-scale
         x, y, wh, cls = p.split((1, 1, 2, p.shape[dim] - 4), dim)
         if flips == 2:
@@ -228,7 +229,7 @@ class DetectionModel(BaseModel):
         return torch.cat((x, y, wh, cls), dim)
 
     def _clip_augmented(self, y):
-        # Clip YOLOv5 augmented inference tails
+        """Clip YOLOv5 augmented inference tails."""
         nl = self.model[-1].nl  # number of detection layers (P3-P5)
         g = sum(4 ** x for x in range(nl))  # grid points
         e = 1  # exclude layer count
@@ -240,7 +241,8 @@ class DetectionModel(BaseModel):
 
 
 class SegmentationModel(DetectionModel):
-    # YOLOv8 segmentation model
+    """YOLOv8 segmentation model."""
+
     def __init__(self, cfg='yolov8n-seg.yaml', ch=3, nc=None, verbose=True):
         super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
 
@@ -249,7 +251,8 @@ class SegmentationModel(DetectionModel):
 
 
 class PoseModel(DetectionModel):
-    # YOLOv8 pose model
+    """YOLOv8 pose model."""
+
     def __init__(self, cfg='yolov8n-pose.yaml', ch=3, nc=None, data_kpt_shape=(None, None), verbose=True):
         if not isinstance(cfg, dict):
             cfg = yaml_model_load(cfg)  # load model YAML
@@ -260,7 +263,8 @@ class PoseModel(DetectionModel):
 
 
 class ClassificationModel(BaseModel):
-    # YOLOv8 classification model
+    """YOLOv8 classification model."""
+
     def __init__(self,
                  cfg=None,
                  model=None,
@@ -272,7 +276,7 @@ class ClassificationModel(BaseModel):
         self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg, ch, nc, verbose)
 
     def _from_detection_model(self, model, nc=1000, cutoff=10):
-        # Create a YOLOv5 classification model from a YOLOv5 detection model
+        """Create a YOLOv5 classification model from a YOLOv5 detection model."""
         from ultralytics.nn.autobackend import AutoBackend
         if isinstance(model, AutoBackend):
             model = model.model  # unwrap DetectMultiBackend
@@ -304,7 +308,7 @@ class ClassificationModel(BaseModel):
 
     @staticmethod
     def reshape_outputs(model, nc):
-        # Update a TorchVision classification model to class count 'n' if required
+        """Update a TorchVision classification model to class count 'n' if required."""
         name, m = list((model.model if hasattr(model, 'model') else model).named_children())[-1]  # last module
         if isinstance(m, Classify):  # YOLO Classify() head
             if m.linear.out_features != nc:
@@ -363,7 +367,7 @@ def torch_safe_load(weight):
 
 
 def attempt_load_weights(weights, device=None, inplace=True, fuse=False):
-    # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
+    """Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a."""
 
     ensemble = Ensemble()
     for w in weights if isinstance(weights, list) else [weights]:
@@ -403,7 +407,7 @@ def attempt_load_weights(weights, device=None, inplace=True, fuse=False):
 
 
 def attempt_load_one_weight(weight, device=None, inplace=True, fuse=False):
-    # Loads a single model weights
+    """Loads a single model weights."""
     ckpt, weight = torch_safe_load(weight)  # load ckpt
     args = {**DEFAULT_CFG_DICT, **ckpt['train_args']}  # combine model and default args, preferring model args
     model = (ckpt.get('ema') or ckpt['model']).to(device).float()  # FP32 model
@@ -546,7 +550,7 @@ def guess_model_task(model):
     """
 
     def cfg2task(cfg):
-        # Guess from YAML dictionary
+        """Guess from YAML dictionary."""
         m = cfg['head'][-1][-2].lower()  # output module name
         if m in ('classify', 'classifier', 'cls', 'fc'):
             return 'classify'