Support yololite with fused NMS

inference_models/inference_models/models/auto_loaders/models_registry.py

@@ -255,9 +255,12 @@ class RegistryEntry:
         module_name="inference_models.models.yolo26.yolo26_instance_segmentation_trt",
         class_name="YOLO26ForInstanceSegmentationTRT",
     ),
-    ("yololite", OBJECT_DETECTION_TASK, BackendType.ONNX): LazyClass(
-        module_name="inference_models.models.yololite.yololite_object_detection_onnx",
-        class_name="YOLOLiteForObjectDetectionOnnx",
+    ("yololite", OBJECT_DETECTION_TASK, BackendType.ONNX): RegistryEntry(
+        model_class=LazyClass(
+            module_name="inference_models.models.yololite.yololite_object_detection_onnx",
+            class_name="YOLOLiteForObjectDetectionOnnx",
+        ),
+        supported_model_features={"nms_fused"},
     ),
     ("paligemma-2", VLM_TASK, BackendType.HF): LazyClass(
         module_name="inference_models.models.paligemma.paligemma_hf",

inference_models/inference_models/models/yololite/yololite_object_detection_onnx.py

@@ -30,6 +30,7 @@
     parse_inference_config,
 )
 from inference_models.models.common.roboflow.post_processing import (
+    post_process_nms_fused_model_output,
     rescale_detections,
     run_nms_for_object_detection,
 )
@@ -198,38 +199,13 @@ def post_process(
         class_agnostic_nms: bool = INFERENCE_MODELS_YOLOLITE_DEFAULT_CLASS_AGNOSTIC_NMS,
         **kwargs,
     ) -> List[Detections]:
-        # YOLOLite decoded export outputs 3 tensors:
-        #   boxes_xyxy:  [B, N, 4]  - decoded bounding boxes in xyxy pixel coords
-        #   obj_logits:  [B, N, 1]  - objectness logits (pre-sigmoid)
-        #   cls_logits:  [B, N, C]  - class logits (pre-sigmoid)
-        boxes_xyxy, obj_logits, cls_logits = (
-            model_results[0],
-            model_results[1],
-            model_results[2],
-        )
-
-        # Apply sigmoid to convert logits to probabilities
-        obj_conf = torch.sigmoid(obj_logits)  # [B, N, 1]
-        cls_conf = torch.sigmoid(cls_logits)  # [B, N, C]
-
-        # Combined score: objectness * class confidence
-        combined_scores = obj_conf * cls_conf  # [B, N, C]
-
-        # Reshape to [B, 4+C, N] format expected by run_nms_for_object_detection:
-        #   channels 0-3: box coords (xyxy)
-        #   channels 4+:  class scores
-        boxes_t = boxes_xyxy.permute(0, 2, 1)  # [B, 4, N]
-        scores_t = combined_scores.permute(0, 2, 1)  # [B, C, N]
-        nms_input = torch.cat([boxes_t, scores_t], dim=1)  # [B, 4+C, N]
-
-        nms_results = run_nms_for_object_detection(
-            output=nms_input,
-            conf_thresh=confidence,
-            iou_thresh=iou_threshold,
-            max_detections=max_detections,
-            class_agnostic=class_agnostic_nms,
-            box_format="xyxy",
-        )
+        # Backward compatibility: earlier model packages have no post_processing config — always unfused 3-tensor output
+        if self._inference_config.post_processing and self._inference_config.post_processing.fused:
+            nms_results = self._post_process_fused(model_results, confidence)
+        else:
+            nms_results = self._post_process_unfused(
+                model_results, confidence, iou_threshold, max_detections, class_agnostic_nms,
+            )
         rescaled_results = rescale_detections(
             detections=nms_results,
             images_metadata=pre_processing_meta,
@@ -244,3 +220,41 @@ def post_process(
                 )
             )
         return results
+
+    def _post_process_fused(
+        self,
+        model_results: Tuple[torch.Tensor, ...],
+        confidence: float,
+    ) -> List[torch.Tensor]:
+        # Single output tensor [B, max_det, 6]: x1, y1, x2, y2, conf, class_id
+        output = model_results[0]
+        return post_process_nms_fused_model_output(output=output, conf_thresh=confidence)
+
+    def _post_process_unfused(
+        self,
+        model_results: Tuple[torch.Tensor, ...],
+        confidence: float,
+        iou_threshold: float,
+        max_detections: int,
+        class_agnostic_nms: bool,
+    ) -> List[torch.Tensor]:
+        # Decoded outputs without fused NMS: boxes_xyxy [B,N,4], obj_logits [B,N,1], cls_logits [B,N,C]
+        boxes_xyxy, obj_logits, cls_logits = (
+            model_results[0], model_results[1], model_results[2],
+        )
+        obj_conf = torch.sigmoid(obj_logits)
+        cls_conf = torch.sigmoid(cls_logits)
+        combined_scores = obj_conf * cls_conf
+
+        boxes_t = boxes_xyxy.permute(0, 2, 1)
+        scores_t = combined_scores.permute(0, 2, 1)
+        nms_input = torch.cat([boxes_t, scores_t], dim=1)
+
+        return run_nms_for_object_detection(
+            output=nms_input,
+            conf_thresh=confidence,
+            iou_thresh=iou_threshold,
+            max_detections=max_detections,
+            class_agnostic=class_agnostic_nms,
+            box_format="xyxy",
+        )

inference_models/tests/integration_tests/models/conftest.py

@@ -73,7 +73,9 @@
 COIN_COUNTING_YOLACT_ONNX_STATIC_BS_STATIC_CROP_STRETCH_URL = "https://storage.googleapis.com/roboflow-tests-assets/rf-platform-models/yolact-static-bs-static-crop-stretch-onnx.zip"
 COIN_COUNTING_YOLACT_ONNX_STATIC_BS_STRETCH_URL = "https://storage.googleapis.com/roboflow-tests-assets/rf-platform-models/yolact-static-bs-stretch-onnx.zip"
 
-COIN_COUNTING_YOLOLITE_N_ONNX_DYNAMIC_BS_LETTERBOX_URL = "https://storage.googleapis.com/roboflow-tests-assets/rf-platform-models/coin-counting-yololite-n-onnx-dynamic-bs-letterbox.zip"
+COIN_COUNTING_YOLOLITE_EDGE_N_ONNX_STATIC_BS_STRETCH_URL = "https://storage.googleapis.com/roboflow-tests-assets/rf-platform-models/coin-counting-yololite-edge-n-onnx-static-bs-stretch.zip"
+COIN_COUNTING_YOLOLITE_EDGE_N_ONNX_DYNAMIC_BS_STRETCH_URL = "https://storage.googleapis.com/roboflow-tests-assets/rf-platform-models/coin-counting-yololite-edge-n-onnx-dynamic-bs-stretch.zip"
+COIN_COUNTING_YOLOLITE_EDGE_N_ONNX_DYNAMIC_BS_STRETCH_FUSED_NMS_URL = "https://storage.googleapis.com/roboflow-tests-assets/rf-platform-models/coin-counting-yololite-edge-n-onnx-dynamic-bs-stretch-fused-nms.zip"
 
 ASL_YOLOV8N_SEG_ONNX_DYNAMIC_BS_STRETCH_URL = "https://storage.googleapis.com/roboflow-tests-assets/rf-platform-models/yolov8n-seg-onnx-dynamic-bs-stretch.zip"
 ASL_YOLOV8N_SEG_ONNX_DYNAMIC_BS_STRETCH_FUSED_NMS_URL = "https://storage.googleapis.com/roboflow-tests-assets/rf-platform-models/yolov8n-seg-onnx-dynamic-bs-stretch-fused-nms.zip"
@@ -700,10 +702,26 @@ def coin_counting_yolo_nas_onnx_static_bs_center_crop_package() -> str:
 
 
 @pytest.fixture(scope="module")
-def coin_counting_yololite_n_onnx_dynamic_bs_letterbox_package() -> str:
+def coin_counting_yololite_edge_n_onnx_static_bs_stretch_package() -> str:
     return download_model_package(
-        model_package_zip_url=COIN_COUNTING_YOLOLITE_N_ONNX_DYNAMIC_BS_LETTERBOX_URL,
-        package_name="coin-counting-yololite-n-onnx-dynamic-bs-letterbox",
+        model_package_zip_url=COIN_COUNTING_YOLOLITE_EDGE_N_ONNX_STATIC_BS_STRETCH_URL,
+        package_name="coin-counting-yololite-edge-n-onnx-static-bs-stretch",
+    )
+
+
+@pytest.fixture(scope="module")
+def coin_counting_yololite_edge_n_onnx_dynamic_bs_stretch_package() -> str:
+    return download_model_package(
+        model_package_zip_url=COIN_COUNTING_YOLOLITE_EDGE_N_ONNX_DYNAMIC_BS_STRETCH_URL,
+        package_name="coin-counting-yololite-edge-n-onnx-dynamic-bs-stretch",
+    )
+
+
+@pytest.fixture(scope="module")
+def coin_counting_yololite_edge_n_onnx_dynamic_bs_stretch_fused_nms_package() -> str:
+    return download_model_package(
+        model_package_zip_url=COIN_COUNTING_YOLOLITE_EDGE_N_ONNX_DYNAMIC_BS_STRETCH_FUSED_NMS_URL,
+        package_name="coin-counting-yololite-edge-n-onnx-dynamic-bs-stretch-fused-nms",
     )