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

* export.py last comma

* Update export.py

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* Update hubconf.py

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* PyTorch Hub tuple fix

* PyTorch Hub tuple fix2

* PyTorch Hub tuple fix3

* Update setup

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>

val.py

@@ -62,10 +62,11 @@ def save_one_json(predn, jdict, path, class_map):
     box = xyxy2xywh(predn[:, :4])  # xywh
     box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
     for p, b in zip(predn.tolist(), box.tolist()):
-        jdict.append({'image_id': image_id,
-                      'category_id': class_map[int(p[5])],
-                      'bbox': [round(x, 3) for x in b],
-                      'score': round(p[4], 5)})
+        jdict.append({
+            'image_id': image_id,
+            'category_id': class_map[int(p[5])],
+            'bbox': [round(x, 3) for x in b],
+            'score': round(p[4], 5)})
 
 
 def process_batch(detections, labels, iouv):
@@ -93,7 +94,8 @@ def process_batch(detections, labels, iouv):
 
 
 @torch.no_grad()
-def run(data,
+def run(
+        data,
         weights=None,  # model.pt path(s)
         batch_size=32,  # batch size
         imgsz=640,  # inference size (pixels)
@@ -120,7 +122,7 @@ def run(data,
         plots=True,
         callbacks=Callbacks(),
         compute_loss=None,
-        ):
+):
     # Initialize/load model and set device
     training = model is not None
     if training:  # called by train.py
@@ -164,8 +166,15 @@ def run(data,
         pad = 0.0 if task in ('speed', 'benchmark') else 0.5
         rect = False if task == 'benchmark' else pt  # square inference for benchmarks
         task = task if task in ('train', 'val', 'test') else 'val'  # path to train/val/test images
-        dataloader = create_dataloader(data[task], imgsz, batch_size, stride, single_cls, pad=pad, rect=rect,
-                                       workers=workers, prefix=colorstr(f'{task}: '))[0]
+        dataloader = create_dataloader(data[task],
+                                       imgsz,
+                                       batch_size,
+                                       stride,
+                                       single_cls,
+                                       pad=pad,
+                                       rect=rect,
+                                       workers=workers,
+                                       prefix=colorstr(f'{task}: '))[0]
 
     seen = 0
     confusion_matrix = ConfusionMatrix(nc=nc)