Merge pull request #1768 from roboflow/fix/usage-collector-model-id

Usage collector model_id

Author: grzegorz-roboflow

inference/core/version.py

@@ -1,4 +1,4 @@
-__version__ = "0.62.2"
+__version__ = "0.62.3"
 
 
 if __name__ == "__main__":

inference/models/clip/clip_model.py

@@ -24,7 +24,6 @@
     CLIP_MODEL_ID,
     ONNXRUNTIME_EXECUTION_PROVIDERS,
     REQUIRED_ONNX_PROVIDERS,
-    TENSORRT_CACHE_PATH,
 )
 from inference.core.exceptions import OnnxProviderNotAvailable
 from inference.core.models.roboflow import OnnxRoboflowCoreModel

inference/models/sam2/segment_anything2.py

@@ -42,6 +42,7 @@
 from inference.core.utils.image_utils import load_image_rgb
 from inference.core.utils.postprocess import masks2multipoly
 from inference.core.utils.torchscript_guard import _temporarily_disable_torch_jit_script
+from inference.usage_tracking.collector import usage_collector
 
 if DEVICE is None:
     DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
@@ -176,6 +177,7 @@ def embed_image(
                     safe_remove_from_dict(values=self.image_size_cache, key=cache_key)
             return embedding_dict, img_in.shape[:2], image_id
 
+    @usage_collector("model")
     def infer_from_request(self, request: Sam2InferenceRequest):
         """Performs inference based on the request type.
 

inference/models/sam3/segment_anything3.py

@@ -65,6 +65,7 @@
 )
 from inference.core.utils.image_utils import load_image_rgb
 from inference.core.utils.postprocess import masks2multipoly
+from inference.usage_tracking.collector import usage_collector
 
 
 def _to_numpy_masks(masks_any) -> np.ndarray:
@@ -355,6 +356,7 @@ def preproc_image(self, image: InferenceRequestImage) -> np.ndarray:
         np_image = load_image_rgb(image)
         return np_image
 
+    @usage_collector("model")
     def infer_from_request(self, request: Sam3InferenceRequest):
         # with self.sam3_lock:
         t1 = perf_counter()

inference/models/sam3/visual_segmentation.py

@@ -51,6 +51,7 @@
 from inference.core.utils.image_utils import load_image_rgb
 from inference.core.utils.postprocess import masks2multipoly
 from inference.core.utils.torchscript_guard import _temporarily_disable_torch_jit_script
+from inference.usage_tracking.collector import usage_collector
 
 # from sam3.model.sam1_task_predictor import SAM3InteractiveImagePredictor
 # from sam3.sam3_video_model_builder import build_sam3_tracking_predictor
@@ -186,6 +187,7 @@ def embed_image(
                     safe_remove_from_dict(values=self.image_size_cache, key=cache_key)
             return embedding_dict, img_in.shape[:2], image_id
 
+    @usage_collector("model")
     def infer_from_request(self, request: Sam2InferenceRequest):
         """Performs inference based on the request type.
 

inference/usage_tracking/decorator_helpers.py

@@ -107,6 +107,12 @@ def get_request_resource_id_from_kwargs(func_kwargs: Dict[str, Any]) -> Optional
             if version_id:
                 return f"{dataset_id}/{version_id}"
             return str(dataset_id)
+        if hasattr(inference_request, "model_id"):
+            return str(inference_request.model_id)
+    if "request" in func_kwargs:
+        request = func_kwargs["request"]
+        if hasattr(request, "model_id"):
+            return str(request.model_id)
     if "dataset_id" in func_kwargs and "version_id" in func_kwargs:
         dataset_id = func_kwargs["dataset_id"]
         version_id = func_kwargs["version_id"]
@@ -119,6 +125,18 @@ def get_request_resource_id_from_kwargs(func_kwargs: Dict[str, Any]) -> Optional
         workflow_request = func_kwargs["workflow_request"]
         if hasattr(workflow_request, "workflow_id"):
             return str(workflow_request.workflow_id)
+    if "self" in func_kwargs:
+        _self = func_kwargs["self"]
+        if hasattr(_self, "dataset_id") and hasattr(_self, "version_id"):
+            dataset_id = _self.dataset_id
+            version_id = _self.version_id
+            if version_id:
+                return f"{dataset_id}/{version_id}"
+            return str(dataset_id)
+        if hasattr(_self, "model_id"):
+            return str(_self.model_id)
+        if hasattr(_self, "endpoint"):
+            return str(_self.endpoint)
     return None