ultralytics 8.1.17 fix ClassificationDataset caching (#8358)

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

mkdocs.yml

@@ -197,7 +197,7 @@ nav:
           - Python: usage/python.md
           - Callbacks: usage/callbacks.md
           - Configuration: usage/cfg.md
-          - Simple-Utilities: usage/simple-utilities.md
+          - Simple Utilities: usage/simple-utilities.md
           - Advanced Customization: usage/engine.md
   - Modes:
       - modes/index.md

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.1.16"
+__version__ = "8.1.17"
 
 from ultralytics.data.explorer.explorer import Explorer
 from ultralytics.models import RTDETR, SAM, YOLO, YOLOWorld

ultralytics/data/dataset.py

@@ -226,35 +226,42 @@ class YOLODataset(BaseDataset):
 # Classification dataloaders -------------------------------------------------------------------------------------------
 class ClassificationDataset(torchvision.datasets.ImageFolder):
     """
-    YOLO Classification Dataset.
+    Extends torchvision ImageFolder to support YOLO classification tasks, offering functionalities like image
+    augmentation, caching, and verification. It's designed to efficiently handle large datasets for training deep
+    learning models, with optional image transformations and caching mechanisms to speed up training.
 
-    Args:
-        root (str): Dataset path.
+    This class allows for augmentations using both torchvision and Albumentations libraries, and supports caching images
+    in RAM or on disk to reduce IO overhead during training. Additionally, it implements a robust verification process
+    to ensure data integrity and consistency.
 
     Attributes:
-        cache_ram (bool): True if images should be cached in RAM, False otherwise.
-        cache_disk (bool): True if images should be cached on disk, False otherwise.
-        samples (list): List of samples containing file, index, npy, and im.
-        torch_transforms (callable): torchvision transforms applied to the dataset.
-        album_transforms (callable, optional): Albumentations transforms applied to the dataset if augment is True.
+        cache_ram (bool): Indicates if caching in RAM is enabled.
+        cache_disk (bool): Indicates if caching on disk is enabled.
+        samples (list): A list of tuples, each containing the path to an image, its class index, path to its .npy cache
+                        file (if caching on disk), and optionally the loaded image array (if caching in RAM).
+        torch_transforms (callable): PyTorch transforms to be applied to the images.
     """
 
-    def __init__(self, root, args, augment=False, cache=False, prefix=""):
+    def __init__(self, root, args, augment=False, prefix=""):
         """
         Initialize YOLO object with root, image size, augmentations, and cache settings.
 
         Args:
-            root (str): Dataset path.
-            args (Namespace): Argument parser containing dataset related settings.
-            augment (bool, optional): True if dataset should be augmented, False otherwise. Defaults to False.
-            cache (bool | str | optional): Cache setting, can be True, False, 'ram' or 'disk'. Defaults to False.
+            root (str): Path to the dataset directory where images are stored in a class-specific folder structure.
+            args (Namespace): Configuration containing dataset-related settings such as image size, augmentation
+                parameters, and cache settings. It includes attributes like `imgsz` (image size), `fraction` (fraction
+                of data to use), `scale`, `fliplr`, `flipud`, `cache` (disk or RAM caching for faster training),
+                `auto_augment`, `hsv_h`, `hsv_s`, `hsv_v`, and `crop_fraction`.
+            augment (bool, optional): Whether to apply augmentations to the dataset. Default is False.
+            prefix (str, optional): Prefix for logging and cache filenames, aiding in dataset identification and
+                debugging. Default is an empty string.
         """
         super().__init__(root=root)
         if augment and args.fraction < 1.0:  # reduce training fraction
             self.samples = self.samples[: round(len(self.samples) * args.fraction)]
         self.prefix = colorstr(f"{prefix}: ") if prefix else ""
-        self.cache_ram = cache is True or cache == "ram"
-        self.cache_disk = cache == "disk"
+        self.cache_ram = args.cache is True or args.cache == "ram"  # cache images into RAM
+        self.cache_disk = args.cache == "disk"  # cache images on hard drive as uncompressed *.npy files
         self.samples = self.verify_images()  # filter out bad images
         self.samples = [list(x) + [Path(x[0]).with_suffix(".npy"), None] for x in self.samples]  # file, index, npy, im
         scale = (1.0 - args.scale, 1.0)  # (0.08, 1.0)