ultralytics 8.0.158 add benchmarks to coverage (#4432)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>

ultralytics/models/rtdetr/train.py

@@ -6,12 +6,28 @@ import torch
 
 from ultralytics.models.yolo.detect import DetectionTrainer
 from ultralytics.nn.tasks import RTDETRDetectionModel
-from ultralytics.utils import DEFAULT_CFG, RANK, colorstr
+from ultralytics.utils import RANK, colorstr
 
 from .val import RTDETRDataset, RTDETRValidator
 
 
 class RTDETRTrainer(DetectionTrainer):
+    """
+    A class extending the DetectionTrainer class for training based on an RT-DETR detection model.
+
+    Notes:
+        - F.grid_sample used in rt-detr does not support the `deterministic=True` argument.
+        - AMP training can lead to NaN outputs and may produce errors during bipartite graph matching.
+
+    Example:
+        ```python
+        from ultralytics.models.rtdetr.train import RTDETRTrainer
+
+        args = dict(model='rtdetr-l.yaml', data='coco8.yaml', imgsz=640, epochs=3)
+        trainer = RTDETRTrainer(overrides=args)
+        trainer.train()
+        ```
+    """
 
     def get_model(self, cfg=None, weights=None, verbose=True):
         """Return a YOLO detection model."""
@@ -54,27 +70,3 @@ class RTDETRTrainer(DetectionTrainer):
             gt_bbox.append(batch['bboxes'][batch_idx == i].to(batch_idx.device))
             gt_class.append(batch['cls'][batch_idx == i].to(device=batch_idx.device, dtype=torch.long))
         return batch
-
-
-def train(cfg=DEFAULT_CFG, use_python=False):
-    """Train and optimize RTDETR model given training data and device."""
-    model = 'rtdetr-l.yaml'
-    data = cfg.data or 'coco8.yaml'  # or yolo.ClassificationDataset("mnist")
-    device = cfg.device if cfg.device is not None else ''
-
-    # NOTE: F.grid_sample which is in rt-detr does not support deterministic=True
-    # NOTE: amp training causes nan outputs and end with error while doing bipartite graph matching
-    args = dict(model=model,
-                data=data,
-                device=device,
-                imgsz=640,
-                exist_ok=True,
-                batch=4,
-                deterministic=False,
-                amp=False)
-    trainer = RTDETRTrainer(overrides=args)
-    trainer.train()
-
-
-if __name__ == '__main__':
-    train()