Replace `+=` with faster list `.append()` (#13849)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/data/augment.py

@@ -1223,16 +1223,13 @@ def classify_transforms(
     else:
         # Resize the shortest edge to matching target dim for non-square target
         tfl = [T.Resize(scale_size)]
-    tfl += [T.CenterCrop(size)]
-
-    tfl += [
-        T.ToTensor(),
-        T.Normalize(
-            mean=torch.tensor(mean),
-            std=torch.tensor(std),
-        ),
-    ]
-
+    tfl.extend(
+        [
+            T.CenterCrop(size),
+            T.ToTensor(),
+            T.Normalize(mean=torch.tensor(mean), std=torch.tensor(std)),
+        ]
+    )
     return T.Compose(tfl)
 
 
@@ -1284,9 +1281,9 @@ def classify_augmentations(
     ratio = tuple(ratio or (3.0 / 4.0, 4.0 / 3.0))  # default imagenet ratio range
     primary_tfl = [T.RandomResizedCrop(size, scale=scale, ratio=ratio, interpolation=interpolation)]
     if hflip > 0.0:
-        primary_tfl += [T.RandomHorizontalFlip(p=hflip)]
+        primary_tfl.append(T.RandomHorizontalFlip(p=hflip))
     if vflip > 0.0:
-        primary_tfl += [T.RandomVerticalFlip(p=vflip)]
+        primary_tfl.append(T.RandomVerticalFlip(p=vflip))
 
     secondary_tfl = []
     disable_color_jitter = False
@@ -1298,19 +1295,19 @@ def classify_augmentations(
 
         if auto_augment == "randaugment":
             if TORCHVISION_0_11:
-                secondary_tfl += [T.RandAugment(interpolation=interpolation)]
+                secondary_tfl.append(T.RandAugment(interpolation=interpolation))
             else:
                 LOGGER.warning('"auto_augment=randaugment" requires torchvision >= 0.11.0. Disabling it.')
 
         elif auto_augment == "augmix":
             if TORCHVISION_0_13:
-                secondary_tfl += [T.AugMix(interpolation=interpolation)]
+                secondary_tfl.append(T.AugMix(interpolation=interpolation))
             else:
                 LOGGER.warning('"auto_augment=augmix" requires torchvision >= 0.13.0. Disabling it.')
 
         elif auto_augment == "autoaugment":
             if TORCHVISION_0_10:
-                secondary_tfl += [T.AutoAugment(interpolation=interpolation)]
+                secondary_tfl.append(T.AutoAugment(interpolation=interpolation))
             else:
                 LOGGER.warning('"auto_augment=autoaugment" requires torchvision >= 0.10.0. Disabling it.')
 
@@ -1321,7 +1318,7 @@ def classify_augmentations(
             )
 
     if not disable_color_jitter:
-        secondary_tfl += [T.ColorJitter(brightness=hsv_v, contrast=hsv_v, saturation=hsv_s, hue=hsv_h)]
+        secondary_tfl.append(T.ColorJitter(brightness=hsv_v, contrast=hsv_v, saturation=hsv_s, hue=hsv_h))
 
     final_tfl = [
         T.ToTensor(),