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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.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.Ignore;
import org.junit.Test;
import static org.junit.Assert.assertEquals;
/**
* @author lesters
*/
public class Tf2OnnxImportTestCase {
@Ignore // Ignored because conversion requires python tf2onnx dependencies - tested in system test
@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();
}
}
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