Finalize keypoint detection

docs/inference_sdk/http_client.md

@@ -31,7 +31,7 @@ print(predictions)
 ## Why client has two modes - `v0` and `v1`?
 We are constantly improving our `infrence` package - initial version (`v0`) is compatible with
 models deployed at Roboflow platform (task types: `classification`, `object-detection`, `instance-segmentation` and
-`keypoints-detection`)
+`keypoint-detection`)
 are supported. Version `v1` is available in locally hosted Docker images with HTTP API. 
 
 Locally hosted `inference` server exposes endpoints for model manipulations, but those endpoints are not available

inference/core/constants.py

@@ -1,4 +1,4 @@
 CLASSIFICATION_TASK = "classification"
 OBJECT_DETECTION_TASK = "object-detection"
 INSTANCE_SEGMENTATION_TASK = "instance-segmentation"
-KEYPOINTS_DETECTION_TASK = "keypoints-detection"
+KEYPOINTS_DETECTION_TASK = "keypoint-detection"

inference/core/interfaces/http/http_api.py

@@ -1024,7 +1024,7 @@ async def legacy_infer_from_request(
                     }
                 elif task_type == "classification":
                     inference_request_type = ClassificationInferenceRequest
-                elif task_type == "keypoints-detection":
+                elif task_type == "keypoint-detection":
                     inference_request_type = KeypointsDetectionInferenceRequest
                     args = {"keypoint_confidence": keypoint_confidence}
                 inference_request = inference_request_type(

inference/core/models/keypoints_detection_base.py

@@ -15,6 +15,7 @@
 from inference.core.models.types import PreprocessReturnMetadata
 from inference.core.nms import w_np_non_max_suppression
 from inference.core.utils.postprocess import post_process_bboxes, post_process_keypoints
+from inference.core.utils.validate import get_num_classes_from_model_prediction_shape
 
 DEFAULT_CONFIDENCE = 0.5
 DEFAULT_IOU_THRESH = 0.5
@@ -28,7 +29,7 @@ class KeypointsDetectionBaseOnnxRoboflowInferenceModel(
 ):
     """Roboflow ONNX Object detection model. This class implements an object detection specific infer method."""
 
-    task_type = "keypoints-detection"
+    task_type = "keypoint-detection"
 
     def __init__(self, model_id: str, *args, **kwargs):
         super().__init__(model_id, *args, **kwargs)
@@ -188,3 +189,27 @@ def _model_keypoints_to_response(
             )
             results.append(keypoint)
         return results
+
+    def superset_keypoints_count(self) -> int:
+        """Returns the number of keypoints in the superset."""
+        if self.keypoints_metadata is None:
+            raise ModelArtefactError("Keypoints metadata not available.")
+        max_keypoints = 0
+        for keypoints in self.keypoints_metadata.values():
+            if len(keypoints) > max_keypoints:
+                max_keypoints = len(keypoints)
+        return max_keypoints
+
+    def validate_model_classes(self) -> None:
+        num_keypoints = self.superset_keypoints_count()
+        keypoints_shape = num_keypoints * 3
+        output_shape = self.get_model_output_shape()
+        num_classes = get_num_classes_from_model_prediction_shape(
+            len_prediction=output_shape[2], keypoints_shape=keypoints_shape
+        )
+        try:
+            assert num_classes == self.num_classes
+        except AssertionError:
+            raise ValueError(
+                f"Number of classes in model ({num_classes}) does not match the number of classes in the environment ({self.num_classes})"
+            )

inference/core/models/stubs.py

@@ -117,7 +117,7 @@ def make_response(
 
 
 class KeypointsDetectionModelStub(ModelStub):
-    task_type = "keypoints-detection"
+    task_type = "keypoint-detection"
 
     def make_response(
         self, request: InferenceRequest, prediction: dict, **kwargs

inference/core/roboflow_api.py

@@ -35,6 +35,7 @@
     "object-detection": "yolov5v2s",
     "instance-segmentation": "yolact",
     "classification": "vit",
+    "keypoint-detection": "yolov8n",
 }
 PROJECT_TASK_TYPE_KEY = "project_task_type"
 MODEL_TYPE_KEY = "model_type"

inference/core/utils/validate.py

@@ -1,3 +1,5 @@
-def get_num_classes_from_model_prediction_shape(len_prediction, masks=0):
-    num_classes = len_prediction - 5 - masks
+def get_num_classes_from_model_prediction_shape(
+    len_prediction, masks=0, keypoints_shape=0
+):
+    num_classes = len_prediction - 5 - masks - keypoints_shape
     return num_classes

inference/models/utils.py

@@ -119,17 +119,17 @@
         "instance-segmentation",
         "yolov8-seg",
     ): YOLOv8InstanceSegmentation,
-    ("keypoints-detection", "stub"): KeypointsDetectionModelStub,
-    ("keypoints-detection", "yolov8n"): YOLOv8KeypointsDetection,
-    ("keypoints-detection", "yolov8s"): YOLOv8KeypointsDetection,
-    ("keypoints-detection", "yolov8m"): YOLOv8KeypointsDetection,
-    ("keypoints-detection", "yolov8l"): YOLOv8KeypointsDetection,
-    ("keypoints-detection", "yolov8x"): YOLOv8KeypointsDetection,
-    ("keypoints-detection", "yolov8n-pose"): YOLOv8KeypointsDetection,
-    ("keypoints-detection", "yolov8s-pose"): YOLOv8KeypointsDetection,
-    ("keypoints-detection", "yolov8m-pose"): YOLOv8KeypointsDetection,
-    ("keypoints-detection", "yolov8l-pose"): YOLOv8KeypointsDetection,
-    ("keypoints-detection", "yolov8x-pose"): YOLOv8KeypointsDetection,
+    ("keypoint-detection", "stub"): KeypointsDetectionModelStub,
+    ("keypoint-detection", "yolov8n"): YOLOv8KeypointsDetection,
+    ("keypoint-detection", "yolov8s"): YOLOv8KeypointsDetection,
+    ("keypoint-detection", "yolov8m"): YOLOv8KeypointsDetection,
+    ("keypoint-detection", "yolov8l"): YOLOv8KeypointsDetection,
+    ("keypoint-detection", "yolov8x"): YOLOv8KeypointsDetection,
+    ("keypoint-detection", "yolov8n-pose"): YOLOv8KeypointsDetection,
+    ("keypoint-detection", "yolov8s-pose"): YOLOv8KeypointsDetection,
+    ("keypoint-detection", "yolov8m-pose"): YOLOv8KeypointsDetection,
+    ("keypoint-detection", "yolov8l-pose"): YOLOv8KeypointsDetection,
+    ("keypoint-detection", "yolov8x-pose"): YOLOv8KeypointsDetection,
 }
 
 try:

inference_sdk/http/entities.py

@@ -17,7 +17,7 @@
 CLASSIFICATION_TASK = "classification"
 OBJECT_DETECTION_TASK = "object-detection"
 INSTANCE_SEGMENTATION_TASK = "instance-segmentation"
-KEYPOINTS_DETECTION_TASK = "keypoints-detection"
+KEYPOINTS_DETECTION_TASK = "keypoint-detection"
 DEFAULT_MAX_INPUT_SIZE = 1024
 
 

tests/inference/unit_tests/core/models/test_stubs.py

@@ -25,7 +25,7 @@
         (ClassificationModelStub, "classification"),
         (ObjectDetectionModelStub, "object-detection"),
         (InstanceSegmentationModelStub, "instance-segmentation"),
-        (KeypointsDetectionModelStub, "keypoints-detection"),
+        (KeypointsDetectionModelStub, "keypoint-detection"),
     ],
 )
 def test_model_stub(stub_class: Type[ModelStub], expected_task_type: str) -> None: