Apply ruff==0.9.0 formatting (#13486)

* Refactor code for speed and clarity

* Auto-format by https://ultralytics.com/actions

---------

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

utils/dataloaders.py

@@ -356,8 +356,7 @@ class LoadImages:
         else:
             self.cap = None
         assert self.nf > 0, (
-            f"No images or videos found in {p}. "
-            f"Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}"
+            f"No images or videos found in {p}. Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}"
         )
 
     def __iter__(self):
@@ -713,8 +712,8 @@ class LoadImagesAndLabels(Dataset):
         cache = mem_required * (1 + safety_margin) < mem.available  # to cache or not to cache, that is the question
         if not cache:
             LOGGER.info(
-                f'{prefix}{mem_required / gb:.1f}GB RAM required, '
-                f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, '
+                f"{prefix}{mem_required / gb:.1f}GB RAM required, "
+                f"{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, "
                 f"{'caching images ✅' if cache else 'not caching images ⚠️'}"
             )
         return cache
@@ -774,8 +773,7 @@ class LoadImagesAndLabels(Dataset):
         index = self.indices[index]  # linear, shuffled, or image_weights
 
         hyp = self.hyp
-        mosaic = self.mosaic and random.random() < hyp["mosaic"]
-        if mosaic:
+        if mosaic := self.mosaic and random.random() < hyp["mosaic"]:
             # Load mosaic
             img, labels = self.load_mosaic(index)
             shapes = None
@@ -1162,8 +1160,7 @@ def verify_image_label(args):
                     segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb]  # (cls, xy1...)
                     lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)
                 lb = np.array(lb, dtype=np.float32)
-            nl = len(lb)
-            if nl:
+            if nl := len(lb):
                 assert lb.shape[1] == 5, f"labels require 5 columns, {lb.shape[1]} columns detected"
                 assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}"
                 assert (lb[:, 1:] <= 1).all(), f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}"