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/segment/augmentations.py

@@ -69,16 +69,8 @@ def random_perspective(
         else:  # affine
             im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
 
-    # Visualize
-    # import matplotlib.pyplot as plt
-    # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
-    # ax[0].imshow(im[:, :, ::-1])  # base
-    # ax[1].imshow(im2[:, :, ::-1])  # warped
-
-    # Transform label coordinates
-    n = len(targets)
     new_segments = []
-    if n:
+    if n := len(targets):
         new = np.zeros((n, 4))
         segments = resample_segments(segments)  # upsample
         for i, segment in enumerate(segments):

utils/segment/dataloaders.py

@@ -132,9 +132,7 @@ class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels):  # for training/testing
         index = self.indices[index]  # linear, shuffled, or image_weights
 
         hyp = self.hyp
-        mosaic = self.mosaic and random.random() < hyp["mosaic"]
-        masks = []
-        if mosaic:
+        if mosaic := self.mosaic and random.random() < hyp["mosaic"]:
             # Load mosaic
             img, labels, segments = self.load_mosaic(index)
             shapes = None
@@ -180,6 +178,7 @@ class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels):  # for training/testing
                 )
 
         nl = len(labels)  # number of labels
+        masks = []
         if nl:
             labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3)
             if self.overlap:

utils/segment/loss.py

@@ -61,8 +61,7 @@ class ComputeLoss:
             b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
             tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device)  # target obj
 
-            n = b.shape[0]  # number of targets
-            if n:
+            if n := b.shape[0]:
                 pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, self.nc, nm), 1)  # subset of predictions
 
                 # Box regression