precommit: yapf (#5494)

* precommit: yapf

* align isort

* fix

# Conflicts:
#	utils/plots.py

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* Update setup.cfg

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* Update setup.cfg

* Update setup.cfg

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* Update wandb_utils.py

* Update augmentations.py

* Update setup.cfg

* Update yolo.py

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* Update val.py

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* simplify colorstr

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* val run fix

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* PyTorch Hub tuple fix

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

utils/augmentations.py

@@ -21,15 +21,15 @@ class Albumentations:
             import albumentations as A
             check_version(A.__version__, '1.0.3', hard=True)  # version requirement
 
-            self.transform = A.Compose([
+            T = [
                 A.Blur(p=0.01),
                 A.MedianBlur(p=0.01),
                 A.ToGray(p=0.01),
                 A.CLAHE(p=0.01),
                 A.RandomBrightnessContrast(p=0.0),
                 A.RandomGamma(p=0.0),
-                A.ImageCompression(quality_lower=75, p=0.0)],
-                bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))
+                A.ImageCompression(quality_lower=75, p=0.0)]  # transforms
+            self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))
 
             LOGGER.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p))
         except ImportError:  # package not installed, skip
@@ -121,7 +121,14 @@ def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleF
     return im, ratio, (dw, dh)
 
 
-def random_perspective(im, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0,
+def random_perspective(im,
+                       targets=(),
+                       segments=(),
+                       degrees=10,
+                       translate=.1,
+                       scale=.1,
+                       shear=10,
+                       perspective=0.0,
                        border=(0, 0)):
     # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10))
     # targets = [cls, xyxy]