Ultralytics Code Refactor https://ultralytics.com/actions (#2251)

* Refactor code for speed and clarity * Auto-format by https://ultralytics.com/actions --------- Co-authored-by: UltralyticsAssistant <[email protected]>
ultralytics · Jul 15, 2024 · af56149 · af56149
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diff --git a/benchmarks.py b/benchmarks.py
@@ -78,9 +78,9 @@ def run(
         None
 
     Notes:
-        This function iterates over multiple export formats, performs the export, and then validates the model's
-        performance using appropriate validation functions for detection and segmentation models. The results are logged,
-        and optionally, benchmarks can be configured to raise errors on failures using the `hard_fail` argument.
+        This function iterates over multiple export formats, performs the export, and then validates the model's performance
+        using appropriate validation functions for detection and segmentation models. The results are logged, and optionally,
+        benchmarks can be configured to raise errors on failures using the `hard_fail` argument.
 
     Examples:
         ```python
@@ -173,7 +173,7 @@ def test(
         hard_fail (bool): Raise an error on benchmark failure. Defaults to False.
 
     Returns:
-        pd.DataFrame: A DataFrame containing the export formats and their success status. (pd.DataFrame)
+        pd.DataFrame: A DataFrame containing the export formats and their success status.
 
     Examples:
         ```python
@@ -223,7 +223,7 @@ def test(
 
 def parse_opt():
     """
-    Parses command line arguments for YOLOv3 inference and export configuration.
+    Parses command line arguments for YOLOv3 inference and export configurations.
 
     Args:
         --weights (str): Path to the weights file. Default is 'ROOT / "yolov3-tiny.pt"'.
@@ -233,10 +233,10 @@ def parse_opt():
         --device (str): CUDA device identifier, e.g., '0' for single GPU, '0,1,2,3' for multiple GPUs, or 'cpu' for CPU
             inference. Default is "".
         --half (bool): If set, use FP16 half-precision inference. Default is False.
-        --test (bool): If set, only test exports without running inference. Default is False.
+        --test (bool): If set, test only exports without running inference. Default is False.
         --pt-only (bool): If set, test only the PyTorch model without exporting to other formats. Default is False.
-        --hard-fail (str | bool): If set, raise an exception on benchmark failure. Can also be a string representing
-            the minimum metric floor for success. Default is False.
+        --hard-fail (str | bool): If set, raise an exception on benchmark failure. Can also be a string representing the
+            minimum metric floor for success. Default is False.
 
     Returns:
         argparse.Namespace: The parsed arguments as a namespace object.

diff --git a/detect.py b/detect.py
@@ -96,7 +96,7 @@ def run(
     vid_stride=1,  # video frame-rate stride
 ):
     """
-    Performs YOLOv3 detection on various input sources including images, videos, streams, and YouTube URLs.
+    Run YOLOv3 detection inference on various input sources such as images, videos, streams, and YouTube URLs.
 
     Args:
         weights (str | Path): Path to the model weights file or a Triton URL (default: 'yolov5s.pt').
@@ -287,39 +287,45 @@ def run(
 
 def parse_opt():
     """
-    Parses and returns command-line options for model inference configurations.
+    Parses and returns command-line options for running YOLOv3 model detection.
 
     Args:
-        --weights (list[str]): Model path or triton URL. Default: `ROOT / "yolov3-tiny.pt"`.
-        --source (str): File/directory/URL/glob/screen/0(webcam) for input data. Default: `ROOT / "data/images"`.
-        --data (str): (Optional) Path to dataset.yaml. Default: `ROOT / "data/coco128.yaml"`.
-        --imgsz (list[int]): Inference size as height, width. Accepts multiple values. Default: `[640]`.
-        --conf-thres (float): Confidence threshold for predictions. Default: `0.25`.
-        --iou-thres (float): IoU threshold for Non-Maximum Suppression (NMS). Default: `0.45`.
-        --max-det (int): Maximum number of detections per image. Default: `1000`.
-        --device (str): CUDA device to run the model on, e.g., `0` or `0,1,2,3` or `cpu`. Default: `""`.
-        --view-img (bool): Display results on the screen. Default: `False`.
-        --save-txt (bool): Save results to text files. Default: `False`.
-        --save-conf (bool): Save confidence scores in text labels. Default: `False`.
-        --save-crop (bool): Save cropped prediction boxes. Default: `False`.
-        --nosave (bool): Do not save images/videos. Default: `False`.
-        --classes (list[int]): Filter results by class, e.g., `--classes 0` or `--classes 0 2 3`. Default: `None`.
-        --agnostic-nms (bool): Perform class-agnostic NMS. Default: `False`.
-        --augment (bool): Perform augmented inference. Default: `False`.
-        --visualize (bool): Visualize features. Default: `False`.
-        --update (bool): Update all models. Default: `False`.
-        --project (str): Directory to save results, saved as "project/name". Default: `ROOT / "runs/detect"`.
-        --name (str): Directory name for saving results, e.g., "exp" in "project/name". Default: `"exp"`.
-        --exist-ok (bool): Allow results to be saved in an existing directory without incrementing. Default: `False`.
-        --line-thickness (int): Thickness of bounding box lines (in pixels). Default: `3`.
-        --hide-labels (bool): Hide labels on detected objects. Default: `False`.
-        --hide-conf (bool): Hide confidence scores on labels. Default: `False`.
-        --half (bool): Use FP16 half-precision inference. Default: `False`.
-        --dnn (bool): Use OpenCV DNN backend for ONNX inference. Default: `False`.
-        --vid-stride (int): Frame-rate stride for video input. Default: `1`.
+        --weights (list[str]): Model path or Triton URL. Default: ROOT / "yolov3-tiny.pt".
+        --source (str): Input data source like file/dir/URL/glob/screen/0(webcam). Default: ROOT / "data/images".
+        --data (str): Optional path to dataset.yaml. Default: ROOT / "data/coco128.yaml".
+        --imgsz (list[int]): Inference size as height, width. Accepts multiple values. Default: [640].
+        --conf-thres (float): Confidence threshold for predictions. Default: 0.25.
+        --iou-thres (float): IoU threshold for Non-Maximum Suppression (NMS). Default: 0.45.
+        --max-det (int): Maximum number of detections per image. Default: 1000.
+        --device (str): CUDA device identifier, e.g. "0" or "0,1,2,3" or "cpu". Default: "" (auto-select).
+        --view-img (bool): Display results. Default: False.
+        --save-txt (bool): Save results to *.txt files. Default: False.
+        --save-conf (bool): Save confidence scores in text labels. Default: False.
+        --save-crop (bool): Save cropped prediction boxes. Default: False.
+        --nosave (bool): Do not save images/videos. Default: False.
+        --classes (list[int] | None): Filter results by class, e.g. [0, 2, 3]. Default: None.
+        --agnostic-nms (bool): Perform class-agnostic NMS. Default: False.
+        --augment (bool): Apply augmented inference. Default: False.
+        --visualize (bool): Visualize feature maps. Default: False.
+        --update (bool): Update all models. Default: False.
+        --project (str): Directory to save results; results saved to "project/name". Default: ROOT / "runs/detect".
+        --name (str): Name of the specific run; results saved to "project/name". Default: "exp".
+        --exist-ok (bool): Allow results to be saved in an existing directory without incrementing. Default: False.
+        --line-thickness (int): Bounding box line thickness in pixels. Default: 3.
+        --hide-labels (bool): Hide labels on detections. Default: False.
+        --hide-conf (bool): Hide confidence scores on labels. Default: False.
+        --half (bool): Use FP16 half-precision inference. Default: False.
+        --dnn (bool): Use OpenCV DNN backend for ONNX inference. Default: False.
+        --vid-stride (int): Frame-rate stride for video input. Default: 1.
 
     Returns:
-        argparse.Namespace: Parsed command-line arguments for inference configurations.
+        argparse.Namespace: Parsed command-line arguments for YOLOv3 inference configurations.
+
+    Example:
+        ```python
+        options = parse_opt()
+        run(**vars(options))
+        ```
     """
     parser = argparse.ArgumentParser()
     parser.add_argument(
@@ -359,7 +365,7 @@ def parse_opt():
 
 def main(opt):
     """
-    Entry point for running the model; checks requirements and calls `run` with parsed options.
+    Entry point for running the YOLO model; checks requirements and calls `run` with parsed options.
 
     Args:
         opt (argparse.Namespace): Parsed command-line options, which include:
@@ -400,9 +406,10 @@ def main(opt):
             opt = parse_opt()
             main(opt)
         ```
+
     Notes:
-        Run this function as the entry point for using YOLOv3 for object detection on a variety of input sources such
-        as images, videos, directories, webcams, streams, etc. This function ensures all requirements are checked and
+        Run this function as the entry point for using YOLO for object detection on a variety of input sources such as
+        images, videos, directories, webcams, streams, etc. This function ensures all requirements are checked and
         subsequently initiates the detection process by calling the `run` function with appropriate options.
     """
     check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))