More verbose error when matching input and model dimensionality

model_server/extensions/ilastik/models.py

@@ -162,8 +162,15 @@ class IlastikObjectClassifierFromSegmentationModel(IlastikModel, InstanceSegment
         return ObjectClassificationWorkflowBinary
 
     def infer(self, input_img: GenericImageDataAccessor, segmentation_img: GenericImageDataAccessor) -> (np.ndarray, dict):
-        if self.model_chroma != input_img.chroma or self.model_3d != input_img.is_3d():
-            raise IlastikInputShapeError()
+        if self.model_chroma != input_img.chroma:
+            raise IlastikInputShapeError(
+                f'Model {self} expects {self.model_chroma} input channels but received only {input_img.chroma}'
+            )
+        if self.model_3d != input_img.is_3d():
+            if self.model_3d:
+                raise IlastikInputShapeError(f'Model is 3D but input image is 2D')
+            else:
+                raise IlastikInputShapeError(f'Model is 2D but input image is 3D')
 
         assert segmentation_img.is_mask()
         if isinstance(input_img, PatchStack):