ONNX: use node output as name if not explicitly set

3 files changed, 96 insertions, 0 deletions

diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/SimpleImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/SimpleImportTestCase.java
new file mode 100644
index 00000000000..d1dea730da5
--- /dev/null
+++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/SimpleImportTestCase.java
@@ -0,0 +1,41 @@
+// Copyright 2019 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+
+package ai.vespa.rankingexpression.importer.onnx;
+
+import ai.vespa.rankingexpression.importer.ImportedModel;
+import com.yahoo.searchlib.rankingexpression.evaluation.MapContext;
+import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue;
+import com.yahoo.tensor.Tensor;
+import com.yahoo.tensor.TensorType;
+import org.junit.Test;
+
+import static org.junit.Assert.assertEquals;
+
+/**
+ * @author lesters
+ */
+public class SimpleImportTestCase {
+
+    @Test
+    public void testSimpleOnnxModelImport() {
+        ImportedModel model = new OnnxImporter().importModel("test", "src/test/models/onnx/simple/simple.onnx");
+
+        MapContext context = new MapContext();
+        context.put("query_tensor", new TensorValue(Tensor.Builder.of(TensorType.fromSpec("tensor(d0[1],d1[4])")).
+                cell(0.1, 0, 0).
+                cell(0.2, 0, 1).
+                cell(0.3, 0, 2).
+                cell(0.4, 0, 3).build()));
+        context.put("attribute_tensor", new TensorValue(Tensor.Builder.of(TensorType.fromSpec("tensor(d0[4],d1[1])")).
+                cell(0.1, 0, 0).
+                cell(0.2, 1, 0).
+                cell(0.3, 2, 0).
+                cell(0.4, 3, 0).build()));
+        context.put("bias_tensor", new TensorValue(Tensor.Builder.of(TensorType.fromSpec("tensor(d0[1],d1[1])")).
+                cell(1.0, 0, 0).build()));
+
+        Tensor result = model.expressions().get("output").evaluate(context).asTensor();
+        assertEquals(result, Tensor.from("tensor(d0[1],d1[1]):{{d0:0,d1:0}:1.3}"));
+    }
+
+}
diff --git a/model-integration/src/test/models/onnx/simple/simple.onnx b/model-integration/src/test/models/onnx/simple/simple.onnx
new file mode 100644
index 00000000000..1c746c90efa
--- /dev/null
+++ b/model-integration/src/test/models/onnx/simple/simple.onnx
@@ -0,0 +1,23 @@
+	simple.py:ã

+0
+query_tensor
+attribute_tensormatmul"MatMul
+"
+matmul
+bias_tensoroutput"addsimple_scoringZ
+query_tensor
+

+

+Z"
+attribute_tensor
+

+
+
Z
+bias_tensor
+

+

+
b
+output
+

+

+
B
diff --git a/model-integration/src/test/models/onnx/simple/simple.py b/model-integration/src/test/models/onnx/simple/simple.py
new file mode 100755
index 00000000000..4471ed812b8
--- /dev/null
+++ b/model-integration/src/test/models/onnx/simple/simple.py
@@ -0,0 +1,32 @@
+import onnx
+from onnx import helper, TensorProto
+
+QUERY_TENSOR = helper.make_tensor_value_info('query_tensor', TensorProto.FLOAT, [1, 4])
+ATTRIBUTE_TENSOR = helper.make_tensor_value_info('attribute_tensor', TensorProto.FLOAT, [4, 1])
+BIAS_TENSOR = helper.make_tensor_value_info('bias_tensor', TensorProto.FLOAT, [1, 1])
+OUTPUT = helper.make_tensor_value_info('output', TensorProto.FLOAT, [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,
+    'simple_scoring',
+    [
+        QUERY_TENSOR,
+        ATTRIBUTE_TENSOR,
+        BIAS_TENSOR,
+    ],
+    [OUTPUT],
+)
+model_def = helper.make_model(graph_def, producer_name='simple.py')
+onnx.save(model_def, 'simple.onnx')