Initial conversion of TF to ONNX for testing

1 files changed, 59 insertions, 0 deletions

diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/Tf2OnnxImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/Tf2OnnxImportTestCase.java
new file mode 100644
index 00000000000..dc53678483d
--- /dev/null
+++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/Tf2OnnxImportTestCase.java
@@ -0,0 +1,59 @@
+// 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.tensorflow;
+
+import ai.vespa.rankingexpression.importer.ImportedModel;
+import com.yahoo.searchlib.rankingexpression.RankingExpression;
+import com.yahoo.searchlib.rankingexpression.evaluation.Context;
+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 Tf2OnnxImportTestCase {
+
+    @Test
+    public void testConversionFromTensorFlowToOnnx() {
+        String modelPath = "src/test/models/tensorflow/mnist_softmax/saved";
+        String modelPathToConvert = "src/test/models/tensorflow/mnist_softmax/tf_2_onnx";
+
+        Tensor argument = placeholderArgument();
+        Tensor tensorFlowResult = evaluateTensorFlowModel(modelPath, argument, "Placeholder", "add");
+        Tensor tf2OnnxResult = evaluateTensorFlowModel(modelPathToConvert, argument, "Placeholder", "add");
+
+        assertEquals("Operation 'add' produces equal results", tensorFlowResult, tf2OnnxResult);
+    }
+
+    private Tensor evaluateTensorFlowModel(String path, Tensor argument, String input, String output) {
+        ImportedModel model = new TensorFlowImporter().importModel("test", path);
+        String outputExpr = model.signatures().values().iterator().next().outputs().values().iterator().next();
+        return evaluateExpression(model.expressions().get(outputExpr), contextFrom(model), argument, input);
+    }
+
+    private Tensor evaluateExpression(RankingExpression expression, Context context, Tensor argument, String input) {
+        context.put(input, new TensorValue(argument));
+        return expression.evaluate(context).asTensor();
+    }
+
+    private Context contextFrom(ImportedModel result) {
+        MapContext context = new MapContext();
+        result.largeConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(Tensor.from(tensor))));
+        result.smallConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(Tensor.from(tensor))));
+        return context;
+    }
+
+    private Tensor placeholderArgument() {
+        Tensor.Builder b = Tensor.Builder.of(new TensorType.Builder().indexed("d0", 1).indexed("d1", 784).build());
+        for (int d0 = 0; d0 < 1; d0++)
+            for (int d1 = 0; d1 < 784; d1++)
+                b.cell(d1 * 1.0 / 784, d0, d1);
+        return b.build();
+    }
+
+
+}