update onnx.proto

* use latest version from https://github.com/onnx/onnx/blob/main/onnx/onnx.proto
* track API changes (enum -> int32)

2 files changed, 7 insertions, 5 deletions

diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/TensorConverter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/TensorConverter.java
index f12f60dcc8e..f690b8e8c8a 100644
--- a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/TensorConverter.java
+++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/TensorConverter.java
@@ -32,8 +32,9 @@ class TensorConverter {
     }
 
     private static Values readValuesOf(Onnx.TensorProto tensorProto) {
+        var elemType = Onnx.TensorProto.DataType.forNumber(tensorProto.getDataType());
         if (tensorProto.hasRawData()) {
-            switch (tensorProto.getDataType()) {
+            switch (elemType) {
                 case BOOL: return new RawBoolValues(tensorProto);
                 case FLOAT: return new RawFloatValues(tensorProto);
                 case DOUBLE: return new RawDoubleValues(tensorProto);
@@ -41,7 +42,7 @@ class TensorConverter {
                 case INT64: return new RawLongValues(tensorProto);
             }
         } else {
-            switch (tensorProto.getDataType()) {
+            switch (elemType) {
                 case FLOAT: return new FloatValues(tensorProto);
                 case DOUBLE: return new DoubleValues(tensorProto);
                 case INT32: return new IntValues(tensorProto);
diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/TypeConverter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/TypeConverter.java
index 35ec1d8c54a..deac950d324 100644
--- a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/TypeConverter.java
+++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/onnx/TypeConverter.java
@@ -37,7 +37,8 @@ class TypeConverter {
     static OrderedTensorType typeFrom(Onnx.TypeProto type) {
         String dimensionPrefix = "d"; // standard naming convention: d0, d1, ...
         Onnx.TensorShapeProto shape = type.getTensorType().getShape();
-        OrderedTensorType.Builder builder = new OrderedTensorType.Builder(toValueType(type.getTensorType().getElemType()));
+        var elemType = Onnx.TensorProto.DataType.forNumber(type.getTensorType().getElemType());
+        OrderedTensorType.Builder builder = new OrderedTensorType.Builder(toValueType(elemType));
         for (int i = 0; i < shape.getDimCount(); ++ i) {
             String dimensionName = dimensionPrefix + i;
             Onnx.TensorShapeProto.Dimension onnxDimension = shape.getDim(i);
@@ -52,8 +53,8 @@ class TypeConverter {
     }
 
     static OrderedTensorType typeFrom(Onnx.TensorProto tensor) {
-        return OrderedTensorType.fromDimensionList(toValueType(tensor.getDataType()),
-                                                   tensor.getDimsList());
+        var elemType = Onnx.TensorProto.DataType.forNumber(tensor.getDataType());
+        return OrderedTensorType.fromDimensionList(toValueType(elemType), tensor.getDimsList());
     }
 
     private static TensorType.Value toValueType(Onnx.TensorProto.DataType dataType) {