ultralytics 8.0.81 single-line docstring updates (#2061)

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

ultralytics/yolo/data/dataset.py

@@ -127,6 +127,7 @@ class YOLODataset(BaseDataset):
         return x
 
     def get_labels(self):
+        """Returns dictionary of labels for YOLO training."""
         self.label_files = img2label_paths(self.im_files)
         cache_path = Path(self.label_files[0]).parent.with_suffix('.cache')
         try:
@@ -170,6 +171,7 @@ class YOLODataset(BaseDataset):
 
     # TODO: use hyp config to set all these augmentations
     def build_transforms(self, hyp=None):
+        """Builds and appends transforms to the list."""
         if self.augment:
             hyp.mosaic = hyp.mosaic if self.augment and not self.rect else 0.0
             hyp.mixup = hyp.mixup if self.augment and not self.rect else 0.0
@@ -187,6 +189,7 @@ class YOLODataset(BaseDataset):
         return transforms
 
     def close_mosaic(self, hyp):
+        """Sets mosaic, copy_paste and mixup options to 0.0 and builds transformations."""
         hyp.mosaic = 0.0  # set mosaic ratio=0.0
         hyp.copy_paste = 0.0  # keep the same behavior as previous v8 close-mosaic
         hyp.mixup = 0.0  # keep the same behavior as previous v8 close-mosaic
@@ -206,6 +209,7 @@ class YOLODataset(BaseDataset):
 
     @staticmethod
     def collate_fn(batch):
+        """Collates data samples into batches."""
         new_batch = {}
         keys = batch[0].keys()
         values = list(zip(*[list(b.values()) for b in batch]))
@@ -234,6 +238,7 @@ class ClassificationDataset(torchvision.datasets.ImageFolder):
     """
 
     def __init__(self, root, augment, imgsz, cache=False):
+        """Initialize YOLO object with root, image size, augmentations, and cache settings"""
         super().__init__(root=root)
         self.torch_transforms = classify_transforms(imgsz)
         self.album_transforms = classify_albumentations(augment, imgsz) if augment else None
@@ -242,6 +247,7 @@ class ClassificationDataset(torchvision.datasets.ImageFolder):
         self.samples = [list(x) + [Path(x[0]).with_suffix('.npy'), None] for x in self.samples]  # file, index, npy, im
 
     def __getitem__(self, i):
+        """Returns subset of data and targets corresponding to given indices."""
         f, j, fn, im = self.samples[i]  # filename, index, filename.with_suffix('.npy'), image
         if self.cache_ram and im is None:
             im = self.samples[i][3] = cv2.imread(f)
@@ -265,4 +271,5 @@ class ClassificationDataset(torchvision.datasets.ImageFolder):
 class SemanticDataset(BaseDataset):
 
     def __init__(self):
+        """Initialize a SemanticDataset object."""
         pass