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

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* Update wandb_utils.py

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* simplify colorstr

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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/metrics.py

@@ -184,7 +184,14 @@ class ConfusionMatrix:
             labels = (0 < nn < 99) and (nn == nc)  # apply names to ticklabels
             with warnings.catch_warnings():
                 warnings.simplefilter('ignore')  # suppress empty matrix RuntimeWarning: All-NaN slice encountered
-                sn.heatmap(array, annot=nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True, vmin=0.0,
+                sn.heatmap(array,
+                           annot=nc < 30,
+                           annot_kws={
+                               "size": 8},
+                           cmap='Blues',
+                           fmt='.2f',
+                           square=True,
+                           vmin=0.0,
                            xticklabels=names + ['background FP'] if labels else "auto",
                            yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1))
             fig.axes[0].set_xlabel('True')
@@ -253,7 +260,6 @@ def box_iou(box1, box2):
         iou (Tensor[N, M]): the NxM matrix containing the pairwise
             IoU values for every element in boxes1 and boxes2
     """
-
     def box_area(box):
         # box = 4xn
         return (box[2] - box[0]) * (box[3] - box[1])
@@ -300,6 +306,7 @@ def wh_iou(wh1, wh2):
 
 # Plots ----------------------------------------------------------------------------------------------------------------
 
+
 def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()):
     # Precision-recall curve
     fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)