diff --git a/classify/train.py b/classify/train.py
index 5baa5fc9be4..7f0ec41ceaa 100644
--- a/classify/train.py
+++ b/classify/train.py
@@ -229,7 +229,7 @@ def lf(x):
                 amp_autocast = torch.cuda.amp.autocast(enabled=device.type != "cpu")
             else:
                 amp_autocast = torch.amp.autocast("cuda", enabled=device.type != "cpu")
-            with amp_autocast: 
+            with amp_autocast:
                 loss = criterion(model(images), labels)
 
             # Backward
diff --git a/classify/val.py b/classify/val.py
index 8cb61ab35c7..388aaf6981b 100644
--- a/classify/val.py
+++ b/classify/val.py
@@ -115,7 +115,7 @@ def run(
         amp_autocast = torch.cuda.amp.autocast(enabled=device.type != "cpu")
     else:
         amp_autocast = torch.amp.autocast("cuda", enabled=device.type != "cpu")
-        
+
     with amp_autocast:
         for images, labels in bar:
             with dt[0]:
diff --git a/models/common.py b/models/common.py
index 4a20c4b459c..f7192219ced 100644
--- a/models/common.py
+++ b/models/common.py
@@ -861,7 +861,6 @@ def forward(self, ims, size=640, augment=False, profile=False):
             p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device)  # param
             autocast = self.amp and (p.device.type != "cpu")  # Automatic Mixed Precision (AMP) inference
             if isinstance(ims, torch.Tensor):  # torch
-
                 amp_autocast = None
                 if torch.__version__.startswith("1.8"):
                     amp_autocast = torch.cuda.amp.autocast(enabled=autocast)
diff --git a/segment/train.py b/segment/train.py
index a3cb39525c8..e0c91713db2 100644
--- a/segment/train.py
+++ b/segment/train.py
@@ -320,7 +320,7 @@ def lf(x):
     maps = np.zeros(nc)  # mAP per class
     results = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
     scheduler.last_epoch = start_epoch - 1  # do not move
-    
+
     scaler = None
     if torch.__version__.startswith("1.8"):
         scaler = torch.cuda.amp.GradScaler(enabled=amp)
@@ -391,7 +391,7 @@ def lf(x):
             else:
                 amp_autocast = torch.amp.autocast("cuda", enabled=amp)
             # Forward
-            with amp_autocast: 
+            with amp_autocast:
                 pred = model(imgs)  # forward
                 loss, loss_items = compute_loss(pred, targets.to(device), masks=masks.to(device).float())
                 if RANK != -1:
diff --git a/train.py b/train.py
index bf172148330..4f07a37ee40 100644
--- a/train.py
+++ b/train.py
@@ -418,7 +418,7 @@ def lf(x):
                 amp_autocast = torch.cuda.amp.autocast(enabled=amp)
             else:
                 amp_autocast = torch.amp.autocast("cuda", enabled=amp)
-            with amp_autocast: 
+            with amp_autocast:
                 pred = model(imgs)  # forward
                 loss, loss_items = compute_loss(pred, targets.to(device))  # loss scaled by batch_size
                 if RANK != -1:
diff --git a/utils/autobatch.py b/utils/autobatch.py
index 4e784e8c2bf..81bdc2c513e 100644
--- a/utils/autobatch.py
+++ b/utils/autobatch.py
@@ -12,7 +12,6 @@
 
 def check_train_batch_size(model, imgsz=640, amp=True):
     """Checks and computes optimal training batch size for YOLOv5 model, given image size and AMP setting."""
-
     if torch.__version__.startswith("1.8"):
         with torch.cuda.amp.autocast(enabled=amp):
             return autobatch(deepcopy(model).train(), imgsz)