Include keypoints_metadata.json when uploading model

roboflow/__init__.py

@@ -21,7 +21,7 @@
     CLIPModel = None  # type: ignore[assignment,misc]
     GazeModel = None  # type: ignore[assignment,misc]
 
-__version__ = "1.3.9"
+__version__ = "1.3.10"
 
 
 def check_key(api_key, model, notebook, num_retries=0):

roboflow/util/model_processor.py

@@ -125,6 +125,28 @@ def _detect_yolo_task(model_instance) -> Optional[str]:
     }.get(type(model_instance).__name__)
 
 
+def _validate_pose_kpt_shape(model_type: str, model_instance, pt_path: str) -> None:
+    """Fail fast if a pose model lacks `kpt_shape` in its config.
+
+    Roboflow's converter reads `model_artifacts["yaml"]["kpt_shape"]` to build
+    keypoints_metadata.json. Without it the conversion crashes and the deployed package
+    loads as incomplete (CorruptedModelPackageError) — so reject the upload here with an
+    actionable message rather than shipping a model that can never serve.
+    """
+    if task_of_model_type(model_type) != TASK_POSE:
+        return
+    yaml_cfg = getattr(model_instance, "yaml", None)
+    kpt_shape = yaml_cfg.get("kpt_shape") if isinstance(yaml_cfg, dict) else None
+    if not kpt_shape:
+        raise ValueError(
+            f"model_type '{model_type}' is a keypoint/pose model but the checkpoint at "
+            f"'{pt_path}' has no 'kpt_shape' in its config, so the number of keypoints is "
+            "unknown and the deployed model would fail to load. Train/export the model with "
+            "Ultralytics on a pose dataset whose data.yaml sets "
+            "'kpt_shape: [num_keypoints, dims]' (e.g. [17, 3]), then redeploy that .pt."
+        )
+
+
 def _process_yolo(model_type: str, model_path: str, filename: str) -> tuple[str, str]:
     if "yolov8" in model_type:
         try:
@@ -218,6 +240,8 @@ def _process_yolo(model_type: str, model_path: str, filename: str) -> tuple[str,
                 f".pt file is a '{detected_task}' checkpoint. Use a matching model_type."
             )
 
+    _validate_pose_kpt_shape(model_type, model_instance, os.path.join(model_path, filename))
+
     if isinstance(model_instance.names, list):
         class_names = model_instance.names
     else:

tests/util/test_model_processor.py

@@ -5,6 +5,7 @@
 from roboflow.util.model_processor import (
     _detect_rfdetr_task,
     _detect_yolo_task,
+    _validate_pose_kpt_shape,
     task_of_model_type,
 )
 
@@ -84,5 +85,33 @@ def test_unrecognized_returns_none(self):
         self.assertIsNone(_detect_rfdetr_task({"args": SimpleNamespace(other=1)}))
 
 
+class ValidatePoseKptShapeTest(unittest.TestCase):
+    def test_non_pose_is_noop(self):
+        # Detection model with no yaml at all must not raise.
+        _validate_pose_kpt_shape("yolov11", SimpleNamespace(yaml=None), "/tmp/best.pt")
+        _validate_pose_kpt_shape("yolov11-seg", SimpleNamespace(), "/tmp/best.pt")
+
+    def test_pose_with_kpt_shape_ok(self):
+        inst = SimpleNamespace(yaml={"nc": 1, "kpt_shape": [17, 3]})
+        _validate_pose_kpt_shape("yolov11-pose", inst, "/tmp/best.pt")
+
+    def test_pose_missing_kpt_shape_raises(self):
+        inst = SimpleNamespace(yaml={"nc": 1})
+        with self.assertRaises(ValueError) as ctx:
+            _validate_pose_kpt_shape("yolov11-pose", inst, "/tmp/best.pt")
+        msg = str(ctx.exception)
+        self.assertIn("kpt_shape", msg)
+        self.assertIn("/tmp/best.pt", msg)
+
+    def test_pose_empty_kpt_shape_raises(self):
+        inst = SimpleNamespace(yaml={"kpt_shape": []})
+        with self.assertRaises(ValueError):
+            _validate_pose_kpt_shape("yolov11-pose", inst, "/tmp/best.pt")
+
+    def test_pose_no_yaml_raises(self):
+        with self.assertRaises(ValueError):
+            _validate_pose_kpt_shape("yolo26-pose", SimpleNamespace(yaml=None), "/tmp/best.pt")
+
+
 if __name__ == "__main__":
     unittest.main()