Add HUB-SDK Docs reference section (#7781)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>

ultralytics/utils/__init__.py

@@ -116,8 +116,11 @@ class TQDM(tqdm_original):
     """
 
     def __init__(self, *args, **kwargs):
-        """Initialize custom Ultralytics tqdm class with different default arguments."""
-        # Set new default values (these can still be overridden when calling TQDM)
+        """
+        Initialize custom Ultralytics tqdm class with different default arguments.
+
+        Note these can still be overridden when calling TQDM.
+        """
         kwargs["disable"] = not VERBOSE or kwargs.get("disable", False)  # logical 'and' with default value if passed
         kwargs.setdefault("bar_format", TQDM_BAR_FORMAT)  # override default value if passed
         super().__init__(*args, **kwargs)
@@ -377,7 +380,7 @@ def yaml_print(yaml_file: Union[str, Path, dict]) -> None:
         yaml_file: The file path of the YAML file or a YAML-formatted dictionary.
 
     Returns:
-        None
+        (None)
     """
     yaml_dict = yaml_load(yaml_file) if isinstance(yaml_file, (str, Path)) else yaml_file
     dump = yaml.dump(yaml_dict, sort_keys=False, allow_unicode=True)
@@ -610,7 +613,7 @@ def get_ubuntu_version():
 
 def get_user_config_dir(sub_dir="Ultralytics"):
     """
-    Get the user config directory.
+    Return the appropriate config directory based on the environment operating system.
 
     Args:
         sub_dir (str): The name of the subdirectory to create.
@@ -618,7 +621,6 @@ def get_user_config_dir(sub_dir="Ultralytics"):
     Returns:
         (Path): The path to the user config directory.
     """
-    # Return the appropriate config directory for each operating system
     if WINDOWS:
         path = Path.home() / "AppData" / "Roaming" / sub_dir
     elif MACOS:  # macOS

ultralytics/utils/benchmarks.py

@@ -258,8 +258,7 @@ class ProfileModels:
         """Retrieves the information including number of layers, parameters, gradients and FLOPs for an ONNX model
         file.
         """
-        # return (num_layers, num_params, num_gradients, num_flops)
-        return 0.0, 0.0, 0.0, 0.0
+        return 0.0, 0.0, 0.0, 0.0  # return (num_layers, num_params, num_gradients, num_flops)
 
     def iterative_sigma_clipping(self, data, sigma=2, max_iters=3):
         """Applies an iterative sigma clipping algorithm to the given data times number of iterations."""

ultralytics/utils/checks.py

@@ -109,7 +109,7 @@ def is_ascii(s) -> bool:
         s (str): String to be checked.
 
     Returns:
-        bool: True if the string is composed only of ASCII characters, False otherwise.
+        (bool): True if the string is composed only of ASCII characters, False otherwise.
     """
     # Convert list, tuple, None, etc. to string
     s = str(s)
@@ -327,7 +327,7 @@ def check_python(minimum: str = "3.8.0") -> bool:
         minimum (str): Required minimum version of python.
 
     Returns:
-        None
+        (bool): Whether the installed Python version meets the minimum constraints.
     """
     return check_version(platform.python_version(), minimum, name="Python ", hard=True)
 

ultralytics/utils/loss.py

@@ -87,8 +87,12 @@ class BboxLoss(nn.Module):
 
     @staticmethod
     def _df_loss(pred_dist, target):
-        """Return sum of left and right DFL losses."""
-        # Distribution Focal Loss (DFL) proposed in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391
+        """
+        Return sum of left and right DFL losses.
+
+        Distribution Focal Loss (DFL) proposed in Generalized Focal Loss
+        https://ieeexplore.ieee.org/document/9792391
+        """
         tl = target.long()  # target left
         tr = tl + 1  # target right
         wl = tr - target  # weight left
@@ -696,6 +700,7 @@ class v8OBBLoss(v8DetectionLoss):
             anchor_points (torch.Tensor): Anchor points, (h*w, 2).
             pred_dist (torch.Tensor): Predicted rotated distance, (bs, h*w, 4).
             pred_angle (torch.Tensor): Predicted angle, (bs, h*w, 1).
+
         Returns:
             (torch.Tensor): Predicted rotated bounding boxes with angles, (bs, h*w, 5).
         """

ultralytics/utils/metrics.py

@@ -180,7 +180,7 @@ def _get_covariance_matrix(boxes):
     Returns:
         (torch.Tensor): Covariance metrixs corresponding to original rotated bounding boxes.
     """
-    # Gaussian bounding boxes, ignored the center points(the first two columns) cause it's not needed here.
+    # Gaussian bounding boxes, ignore the center points (the first two columns) because they are not needed here.
     gbbs = torch.cat((torch.pow(boxes[:, 2:4], 2) / 12, boxes[:, 4:]), dim=-1)
     a, b, c = gbbs.split(1, dim=-1)
     return (

ultralytics/utils/ops.py

@@ -75,7 +75,6 @@ def segment2box(segment, width=640, height=640):
     Returns:
         (np.ndarray): the minimum and maximum x and y values of the segment.
     """
-    # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
     x, y = segment.T  # segment xy
     inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
     x = x[inside]

ultralytics/utils/plotting.py

@@ -388,6 +388,7 @@ class Annotator:
             a (float) : The value of pose point a
             b (float): The value of pose point b
             c (float): The value o pose point c
+
         Returns:
             angle (degree): Degree value of angle between three points
         """

ultralytics/utils/triton.py

@@ -75,7 +75,7 @@ class TritonRemoteModel:
             *inputs (List[np.ndarray]): Input data to the model.
 
         Returns:
-            List[np.ndarray]: Model outputs.
+            (List[np.ndarray]): Model outputs.
         """
         infer_inputs = []
         input_format = inputs[0].dtype

ultralytics/utils/tuner.py

@@ -94,7 +94,7 @@ def run_ray_tune(
             config (dict): A dictionary of hyperparameters to use for training.
 
         Returns:
-            None.
+            None
         """
         model_to_train = ray.get(model_in_store)  # get the model from ray store for tuning
         model_to_train.reset_callbacks()