Add docstrings to YOLOv5 functions (#12760)

* Add docstrings to top level files * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Update activations.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Auto-format by https://ultralytics.com/actions --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
2024-02-25 14:04:01 +01:00
1 parent ca00c34c2a
commit 41603da162
@@ -71,7 +71,9 @@ from utils.torch_utils import de_parallel, select_device, smart_inference_mode


 def save_one_txt(predn, save_conf, shape, file):
-    # Save one txt result
+    """Saves detection results in txt format; includes class, xywh (normalized), optionally confidence if `save_conf` is
+    True.
+    """
    gn = torch.tensor(shape)[[1, 0, 1, 0]]  # normalization gain whwh
    for *xyxy, conf, cls in predn.tolist():
        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
@@ -81,7 +83,11 @@ def save_one_txt(predn, save_conf, shape, file):


 def save_one_json(predn, jdict, path, class_map, pred_masks):
-    # Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
+    """
+    Saves a JSON file with detection results including bounding boxes, category IDs, scores, and segmentation masks.
+
+    Example JSON result: {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}.
+    """
    from pycocotools.mask import encode

    def single_encode(x):
@@ -437,6 +443,9 @@ def run(


 def parse_opt():
+    """Parses command line arguments for configuring YOLOv5 options like dataset path, weights, batch size, and
+    inference settings.
+    """
    parser = argparse.ArgumentParser()
    parser.add_argument("--data", type=str, default=ROOT / "data/coco128-seg.yaml", help="dataset.yaml path")
    parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-seg.pt", help="model path(s)")
@@ -469,6 +478,7 @@ def parse_opt():


 def main(opt):
+    """Executes YOLOv5 tasks including training, validation, testing, speed, and study with configurable options."""
    check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))

    if opt.task in ("train", "val", "test"):  # run normally