Preload OD models before import to check quantization

build.gradle

@@ -40,7 +40,7 @@ ext {
     javalinVersion = "5.6.2"
     libcameraDriverVersion = "v2025.0.3"
     rknnVersion = "dev-v2025.0.0-1-g33b6263"
-    rubikVersion = "v2025.1.0"
+    rubikVersion = "dev-v2025.1.0-8-g067a316"
     frcYear = "2025"
     mrcalVersion = "v2025.0.0";
 

photon-core/src/main/java/org/photonvision/vision/objects/RknnObjectDetector.java

@@ -74,6 +74,8 @@ public RknnObjectDetector(RknnModel model, Size inputSize) {
         if (objPointer <= 0) {
             throw new RuntimeException(
                     "Failed to create detector from path " + model.modelFile.getPath());
+        } else if (!RknnJNI.isQuantized(objPointer)) {
+            throw new UnsupportedOperationException("Model must be quantized.");
         }
 
         logger.debug("Created detector for model " + model.modelFile.getName());

photon-core/src/main/java/org/photonvision/vision/objects/RubikObjectDetector.java

@@ -80,6 +80,8 @@ public RubikObjectDetector(RubikModel model, Size inputSize) {
                             + ". Please ensure the model is valid and compatible with the Rubik backend.");
             throw new RuntimeException(
                     "Failed to create detector from path " + model.modelFile.getPath());
+        } else if (!RubikJNI.isQuantized(ptr)) {
+            throw new UnsupportedOperationException("Model must be quantized.");
         }
 
         logger.debug("Created detector for model " + model.modelFile.getName());

photon-server/src/main/java/org/photonvision/server/RequestHandler.java

@@ -58,6 +58,9 @@
 import org.photonvision.vision.calibration.CameraCalibrationCoefficients;
 import org.photonvision.vision.camera.CameraQuirk;
 import org.photonvision.vision.camera.PVCameraInfo;
+import org.photonvision.vision.objects.ObjectDetector;
+import org.photonvision.vision.objects.RknnModel;
+import org.photonvision.vision.objects.RubikModel;
 import org.photonvision.vision.processes.VisionSourceManager;
 import org.zeroturnaround.zip.ZipUtil;
 
@@ -659,18 +662,38 @@ public static void onImportObjectDetectionModelRequest(Context ctx) {
                 modelFile.content().transferTo(out);
             }
 
+            ModelProperties modelProperties =
+                    new ModelProperties(
+                            modelPath,
+                            modelFile.filename().replaceAll("." + modelFileExtension, ""),
+                            labels,
+                            width,
+                            height,
+                            family,
+                            version);
+
+            ObjectDetector objDetector = null;
+
+            try {
+                objDetector =
+                        switch (family) {
+                            case RUBIK -> new RubikModel(modelProperties).load();
+                            case RKNN -> new RknnModel(modelProperties).load();
+                        };
+            } catch (RuntimeException e) {
+                ctx.status(400);
+                ctx.result("Failed to load object detection model: " + e.getMessage());
+                logger.error("Failed to load object detection model", e);
+            } finally {
+                if (objDetector != null) {
+                    objDetector.release();
+                }
+            }
+
             ConfigManager.getInstance()
                     .getConfig()
                     .neuralNetworkPropertyManager()
-                    .addModelProperties(
-                            new ModelProperties(
-                                    modelPath,
-                                    modelFile.filename().replaceAll("." + modelFileExtension, ""),
-                                    labels,
-                                    width,
-                                    height,
-                                    family,
-                                    version));
+                    .addModelProperties(modelProperties);
 
             logger.debug(
                     ConfigManager.getInstance().getConfig().neuralNetworkPropertyManager().toString());