ultralytics 8.0.133 add torchvision compatibility check (#3703)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/yolo/data/augment.py

@@ -642,7 +642,8 @@ class CopyPaste:
 
 
 class Albumentations:
-    # YOLOv8 Albumentations class (optional, only used if package is installed)
+    """YOLOv8 Albumentations class (optional, only used if package is installed)"""
+
     def __init__(self, p=1.0):
         """Initialize the transform object for YOLO bbox formatted params."""
         self.p = p
@@ -819,7 +820,7 @@ def classify_albumentations(
         std=(1.0, 1.0, 1.0),  # IMAGENET_STD
         auto_aug=False,
 ):
-    # YOLOv8 classification Albumentations (optional, only used if package is installed)
+    """YOLOv8 classification Albumentations (optional, only used if package is installed)."""
     prefix = colorstr('albumentations: ')
     try:
         import albumentations as A
@@ -851,7 +852,8 @@ def classify_albumentations(
 
 
 class ClassifyLetterBox:
-    # YOLOv8 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
+    """YOLOv8 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])"""
+
     def __init__(self, size=(640, 640), auto=False, stride=32):
         """Resizes image and crops it to center with max dimensions 'h' and 'w'."""
         super().__init__()
@@ -871,7 +873,8 @@ class ClassifyLetterBox:
 
 
 class CenterCrop:
-    # YOLOv8 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()])
+    """YOLOv8 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()])"""
+
     def __init__(self, size=640):
         """Converts an image from numpy array to PyTorch tensor."""
         super().__init__()
@@ -885,7 +888,8 @@ class CenterCrop:
 
 
 class ToTensor:
-    # YOLOv8 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
+    """YOLOv8 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])."""
+
     def __init__(self, half=False):
         """Initialize YOLOv8 ToTensor object with optional half-precision support."""
         super().__init__()

ultralytics/yolo/data/build.py

@@ -63,7 +63,7 @@ class _RepeatSampler:
 
 
 def seed_worker(worker_id):  # noqa
-    # Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader
+    """Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader."""
     worker_seed = torch.initial_seed() % 2 ** 32
     np.random.seed(worker_seed)
     random.seed(worker_seed)

ultralytics/yolo/data/dataloaders/stream_loaders.py

@@ -29,7 +29,8 @@ class SourceTypes:
 
 
 class LoadStreams:
-    # YOLOv8 streamloader, i.e. `yolo predict source='rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP streams`
+    """YOLOv8 streamloader, i.e. `yolo predict source='rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP streams`."""
+
     def __init__(self, sources='file.streams', imgsz=640, vid_stride=1):
         """Initialize instance variables and check for consistent input stream shapes."""
         torch.backends.cudnn.benchmark = True  # faster for fixed-size inference
@@ -116,7 +117,8 @@ class LoadStreams:
 
 
 class LoadScreenshots:
-    # YOLOv8 screenshot dataloader, i.e. `yolo predict source=screen`
+    """YOLOv8 screenshot dataloader, i.e. `yolo predict source=screen`."""
+
     def __init__(self, source, imgsz=640):
         """source = [screen_number left top width height] (pixels)."""
         check_requirements('mss')
@@ -158,7 +160,8 @@ class LoadScreenshots:
 
 
 class LoadImages:
-    # YOLOv8 image/video dataloader, i.e. `yolo predict source=image.jpg/vid.mp4`
+    """YOLOv8 image/video dataloader, i.e. `yolo predict source=image.jpg/vid.mp4`."""
+
     def __init__(self, path, imgsz=640, vid_stride=1):
         """Initialize the Dataloader and raise FileNotFoundError if file not found."""
         if isinstance(path, str) and Path(path).suffix == '.txt':  # *.txt file with img/vid/dir on each line

ultralytics/yolo/data/utils.py

@@ -278,7 +278,7 @@ def check_cls_dataset(dataset: str, split=''):
         split (str, optional): The split of the dataset. Either 'val', 'test', or ''. Defaults to ''.
 
     Returns:
-        dict: A dictionary containing the following keys:
+        (dict): A dictionary containing the following keys:
             - 'train' (Path): The directory path containing the training set of the dataset.
             - 'val' (Path): The directory path containing the validation set of the dataset.
             - 'test' (Path): The directory path containing the test set of the dataset.