Ultralytics Code Refactor https://ultralytics.com/actions (#13153)

* Refactor code for speed and clarity

* Update dataloaders.py

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

utils/augmentations.py

@@ -157,6 +157,7 @@ def random_perspective(
     # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10))
     # targets = [cls, xyxy]
 
+    """Applies random perspective transformation to an image, modifying the image and corresponding labels."""
     height = im.shape[0] + border[0] * 2  # shape(h,w,c)
     width = im.shape[1] + border[1] * 2
 
@@ -336,6 +337,9 @@ def classify_albumentations(
     auto_aug=False,
 ):
     # YOLOv5 classification Albumentations (optional, only used if package is installed)
+    """Sets up and returns Albumentations transforms for YOLOv5 classification tasks depending on augmentation
+    settings.
+    """
     prefix = colorstr("albumentations: ")
     try:
         import albumentations as A

utils/dataloaders.py

@@ -174,6 +174,7 @@ def create_dataloader(
     shuffle=False,
     seed=0,
 ):
+    """Creates and returns a configured DataLoader instance for loading and processing image datasets."""
     if rect and shuffle:
         LOGGER.warning("WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False")
         shuffle = False
@@ -552,6 +553,7 @@ class LoadImagesAndLabels(Dataset):
         rank=-1,
         seed=0,
     ):
+        """Initializes the YOLOv5 dataset loader, handling images and their labels, caching, and preprocessing."""
         self.img_size = img_size
         self.augment = augment
         self.hyp = hyp
@@ -1351,6 +1353,7 @@ def create_classification_dataloader(
     path, imgsz=224, batch_size=16, augment=True, cache=False, rank=-1, workers=8, shuffle=True
 ):
     # Returns Dataloader object to be used with YOLOv5 Classifier
+    """Creates a DataLoader for image classification, supporting caching, augmentation, and distributed training."""
     with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
         dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache)
     batch_size = min(batch_size, len(dataset))

utils/segment/augmentations.py

@@ -30,6 +30,7 @@ def random_perspective(
     # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
     # targets = [cls, xyxy]
 
+    """Applies random perspective, rotation, scale, shear, and translation augmentations to an image and targets."""
     height = im.shape[0] + border[0] * 2  # shape(h,w,c)
     width = im.shape[1] + border[1] * 2
 

utils/segment/dataloaders.py

@@ -39,6 +39,7 @@ def create_dataloader(
     overlap_mask=False,
     seed=0,
 ):
+    """Creates a dataloader for training, validating, or testing YOLO models with various dataset options."""
     if rect and shuffle:
         LOGGER.warning("WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False")
         shuffle = False
@@ -102,6 +103,7 @@ class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels):  # for training/testing
         rank=-1,
         seed=0,
     ):
+        """Initializes the dataset with image, label, and mask loading capabilities for training/testing."""
         super().__init__(
             path,
             img_size,