ultralytics 8.1.43 40% faster ultralytics imports

tests/test_python.py

@@ -10,7 +10,6 @@
 import torch
 import yaml
 from PIL import Image
-from torchvision.transforms import ToTensor
 
 from ultralytics import RTDETR, YOLO
 from ultralytics.cfg import TASK2DATA
@@ -108,20 +107,17 @@ def test_predict_img():
     assert len(model(batch, imgsz=32)) == len(batch)  # multiple sources in a batch
 
     # Test tensor inference
-    im = cv2.imread(str(SOURCE))  # OpenCV
-    t = cv2.resize(im, (32, 32))
-    t = ToTensor()(t)
-    t = torch.stack([t, t, t, t])
-    results = model(t, imgsz=32)
-    assert len(results) == t.shape[0]
-    results = seg_model(t, imgsz=32)
-    assert len(results) == t.shape[0]
-    results = cls_model(t, imgsz=32)
-    assert len(results) == t.shape[0]
-    results = pose_model(t, imgsz=32)
-    assert len(results) == t.shape[0]
-    results = obb_model(t, imgsz=32)
-    assert len(results) == t.shape[0]
+    im = torch.rand((4, 3, 32, 32))  # batch-size 4, FP32 0.0-1.0 RGB order
+    results = model(im, imgsz=32)
+    assert len(results) == im.shape[0]
+    results = seg_model(im, imgsz=32)
+    assert len(results) == im.shape[0]
+    results = cls_model(im, imgsz=32)
+    assert len(results) == im.shape[0]
+    results = pose_model(im, imgsz=32)
+    assert len(results) == im.shape[0]
+    results = obb_model(im, imgsz=32)
+    assert len(results) == im.shape[0]
 
 
 def test_predict_grey_and_4ch():
@@ -592,8 +588,6 @@ def image():
 )
 def test_classify_transforms_train(image, auto_augment, erasing, force_color_jitter):
     """Tests classification transforms during training with various augmentation settings."""
-    import torchvision.transforms as T
-
     from ultralytics.data.augment import classify_augmentations
 
     transform = classify_augmentations(
@@ -610,7 +604,6 @@ def test_classify_transforms_train(image, auto_augment, erasing, force_color_jit
         hsv_v=0.4,
         force_color_jitter=force_color_jitter,
         erasing=erasing,
-        interpolation=T.InterpolationMode.BILINEAR,
     )
 
     transformed_image = transform(Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)))

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.1.42"
+__version__ = "8.1.43"
 
 from ultralytics.data.explorer.explorer import Explorer
 from ultralytics.models import RTDETR, SAM, YOLO, YOLOWorld

ultralytics/data/augment.py

@@ -8,7 +8,7 @@
 import cv2
 import numpy as np
 import torch
-import torchvision.transforms as T
+from PIL import Image
 
 from ultralytics.utils import LOGGER, colorstr
 from ultralytics.utils.checks import check_version
@@ -167,8 +167,8 @@ def _update_label_text(self, labels):
         text2id = {text: i for i, text in enumerate(mix_texts)}
 
         for label in [labels] + labels["mix_labels"]:
-            for i, l in enumerate(label["cls"].squeeze(-1).tolist()):
-                text = label["texts"][int(l)]
+            for i, cls in enumerate(label["cls"].squeeze(-1).tolist()):
+                text = label["texts"][int(cls)]
                 label["cls"][i] = text2id[tuple(text)]
             label["texts"] = mix_texts
         return labels
@@ -1133,7 +1133,7 @@ def classify_transforms(
     size=224,
     mean=DEFAULT_MEAN,
     std=DEFAULT_STD,
-    interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR,
+    interpolation=Image.BILINEAR,
     crop_fraction: float = DEFAULT_CROP_FTACTION,
 ):
     """
@@ -1149,6 +1149,7 @@ def classify_transforms(
     Returns:
         (T.Compose): torchvision transforms
     """
+    import torchvision.transforms as T  # scope for faster 'import ultralytics'
 
     if isinstance(size, (tuple, list)):
         assert len(size) == 2
@@ -1157,12 +1158,12 @@ def classify_transforms(
         scale_size = math.floor(size / crop_fraction)
         scale_size = (scale_size, scale_size)
 
-    # aspect ratio is preserved, crops center within image, no borders are added, image is lost
+    # Aspect ratio is preserved, crops center within image, no borders are added, image is lost
     if scale_size[0] == scale_size[1]:
-        # simple case, use torchvision built-in Resize w/ shortest edge mode (scalar size arg)
+        # Simple case, use torchvision built-in Resize with the shortest edge mode (scalar size arg)
         tfl = [T.Resize(scale_size[0], interpolation=interpolation)]
     else:
-        # resize shortest edge to matching target dim for non-square target
+        # Resize the shortest edge to matching target dim for non-square target
         tfl = [T.Resize(scale_size)]
     tfl += [T.CenterCrop(size)]
 
@@ -1192,7 +1193,7 @@ def classify_augmentations(
     hsv_v=0.4,  # image HSV-Value augmentation (fraction)
     force_color_jitter=False,
     erasing=0.0,
-    interpolation: T.InterpolationMode = T.InterpolationMode.BILINEAR,
+    interpolation=Image.BILINEAR,
 ):
     """
     Classification transforms with augmentation for training. Inspired by timm/data/transforms_factory.py.
@@ -1216,7 +1217,9 @@ def classify_augmentations(
     Returns:
         (T.Compose): torchvision transforms
     """
-    # Transforms to apply if albumentations not installed
+    # Transforms to apply if Albumentations not installed
+    import torchvision.transforms as T  # scope for faster 'import ultralytics'
+
     if not isinstance(size, int):
         raise TypeError(f"classify_transforms() size {size} must be integer, not (list, tuple)")
     scale = tuple(scale or (0.08, 1.0))  # default imagenet scale range