adapt and convert between vespa and onnx types

1 files changed, 33 insertions, 0 deletions

diff --git a/eval/src/tests/tensor/onnx_wrapper/int_types.py b/eval/src/tests/tensor/onnx_wrapper/int_types.py
new file mode 100755
index 00000000000..cd82bfd44b5
--- /dev/null
+++ b/eval/src/tests/tensor/onnx_wrapper/int_types.py
@@ -0,0 +1,33 @@
+# Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+import onnx
+from onnx import helper, TensorProto
+
+QUERY_TENSOR = helper.make_tensor_value_info('query_tensor', TensorProto.INT32, [1, 4])
+ATTRIBUTE_TENSOR = helper.make_tensor_value_info('attribute_tensor', TensorProto.INT32, [4, 1])
+BIAS_TENSOR = helper.make_tensor_value_info('bias_tensor', TensorProto.INT32, [1, 1])
+OUTPUT = helper.make_tensor_value_info('output', TensorProto.INT32, [1, 1])
+
+nodes = [
+    helper.make_node(
+        'MatMul',
+        ['query_tensor', 'attribute_tensor'],
+        ['matmul'],
+    ),
+    helper.make_node(
+        'Add',
+        ['matmul', 'bias_tensor'],
+        ['output'],
+    ),
+]
+graph_def = helper.make_graph(
+    nodes,
+    'int_types_scoring',
+    [
+        QUERY_TENSOR,
+        ATTRIBUTE_TENSOR,
+        BIAS_TENSOR,
+    ],
+    [OUTPUT],
+)
+model_def = helper.make_model(graph_def, producer_name='int_types.py')
+onnx.save(model_def, 'int_types.onnx')