Update .pre-commit-config.yaml (#1026)

ultralytics/yolo/data/build.py

@@ -28,7 +28,7 @@ class InfiniteDataLoader(dataloader.DataLoader):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        object.__setattr__(self, "batch_sampler", _RepeatSampler(self.batch_sampler))
+        object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler))
         self.iterator = super().__iter__()
 
     def __len__(self):
@@ -61,9 +61,9 @@ def seed_worker(worker_id):
     random.seed(worker_seed)
 
 
-def build_dataloader(cfg, batch, img_path, stride=32, rect=False, names=None, rank=-1, mode="train"):
-    assert mode in ["train", "val"]
-    shuffle = mode == "train"
+def build_dataloader(cfg, batch, img_path, stride=32, rect=False, names=None, rank=-1, mode='train'):
+    assert mode in ['train', 'val']
+    shuffle = mode == 'train'
     if cfg.rect and shuffle:
         LOGGER.warning("WARNING ⚠️ 'rect=True' is incompatible with DataLoader shuffle, setting shuffle=False")
         shuffle = False
@@ -72,21 +72,21 @@ def build_dataloader(cfg, batch, img_path, stride=32, rect=False, names=None, ra
             img_path=img_path,
             imgsz=cfg.imgsz,
             batch_size=batch,
-            augment=mode == "train",  # augmentation
+            augment=mode == 'train',  # augmentation
             hyp=cfg,  # TODO: probably add a get_hyps_from_cfg function
             rect=cfg.rect or rect,  # rectangular batches
             cache=cfg.cache or None,
             single_cls=cfg.single_cls or False,
             stride=int(stride),
-            pad=0.0 if mode == "train" else 0.5,
-            prefix=colorstr(f"{mode}: "),
-            use_segments=cfg.task == "segment",
-            use_keypoints=cfg.task == "keypoint",
+            pad=0.0 if mode == 'train' else 0.5,
+            prefix=colorstr(f'{mode}: '),
+            use_segments=cfg.task == 'segment',
+            use_keypoints=cfg.task == 'keypoint',
             names=names)
 
     batch = min(batch, len(dataset))
     nd = torch.cuda.device_count()  # number of CUDA devices
-    workers = cfg.workers if mode == "train" else cfg.workers * 2
+    workers = cfg.workers if mode == 'train' else cfg.workers * 2
     nw = min([os.cpu_count() // max(nd, 1), batch if batch > 1 else 0, workers])  # number of workers
     sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
     loader = DataLoader if cfg.image_weights or cfg.close_mosaic else InfiniteDataLoader  # allow attribute updates
@@ -98,7 +98,7 @@ def build_dataloader(cfg, batch, img_path, stride=32, rect=False, names=None, ra
                   num_workers=nw,
                   sampler=sampler,
                   pin_memory=PIN_MEMORY,
-                  collate_fn=getattr(dataset, "collate_fn", None),
+                  collate_fn=getattr(dataset, 'collate_fn', None),
                   worker_init_fn=seed_worker,
                   generator=generator), dataset
 
@@ -151,7 +151,7 @@ def check_source(source):
         from_img = True
     else:
         raise Exception(
-            "Unsupported type encountered! See docs for supported types https://docs.ultralytics.com/predict")
+            'Unsupported type encountered! See docs for supported types https://docs.ultralytics.com/predict')
 
     return source, webcam, screenshot, from_img, in_memory