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// 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.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.searchlib.rankingexpression.rule.CompositeNode;
import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode;
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.from("tensor(d0[1],d1[4]):[0.1, 0.2, 0.3, 0.4]")));
context.put("attribute_tensor", new TensorValue(Tensor.from("tensor(d0[4],d1[1]):[0.1, 0.2, 0.3, 0.4]")));
context.put("bias_tensor", new TensorValue(Tensor.from("tensor(d0[1],d1[1]):[1.0]")));
Tensor result = model.expressions().get("output").evaluate(context).asTensor();
assertEquals(result, Tensor.from("tensor(d0[1],d1[1]):{{d0:0,d1:0}:1.3}"));
}
@Test
public void testGather() {
ImportedModel model = new OnnxImporter().importModel("test", "src/test/models/onnx/simple/gather.onnx");
MapContext context = new MapContext();
context.put("data", new TensorValue(Tensor.from("tensor(d0[3],d1[2]):[1, 2, 3, 4, 5, 6]")));
context.put("indices", new TensorValue(Tensor.from("tensor(d0[2],d1[2]):[0, 1, 1, 2]")));
model.functions().forEach((k, v) -> evaluateFunction(context, model, k));
Tensor result = model.expressions().get("y").evaluate(context).asTensor();
assertEquals(result, Tensor.from("tensor(d0[2],d1[2],d2[2]):[1, 2, 3, 4, 3, 4, 5, 6]"));
}
private void evaluateFunction(Context context, ImportedModel model, String functionName) {
if (!context.names().contains(functionName)) {
RankingExpression e = RankingExpression.from(model.functions().get(functionName));
evaluateFunctionDependencies(context, model, e.getRoot());
context.put(functionName, new TensorValue(e.evaluate(context).asTensor()));
}
}
private void evaluateFunctionDependencies(Context context, ImportedModel model, ExpressionNode node) {
if (node instanceof ReferenceNode) {
String name = node.toString();
if (model.functions().containsKey(name)) {
evaluateFunction(context, model, name);
}
}
else if (node instanceof CompositeNode) {
for (ExpressionNode child : ((CompositeNode)node).children()) {
evaluateFunctionDependencies(context, model, child);
}
}
}
}
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