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package com.yahoo.searchlib.rankingexpression.integration.ml;
import com.yahoo.searchlib.rankingexpression.ExpressionFunction;
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;
import static org.junit.Assert.assertNotNull;
import static org.junit.Assert.assertTrue;
/**
* @author lesters
*/
public class OnnxMnistSoftmaxImportTestCase {
@Test
public void testMnistSoftmaxImport() {
ImportedModel model = new OnnxImporter().importModel("test", "src/test/files/integration/onnx/mnist_softmax/mnist_softmax.onnx");
// Check constants
assertEquals(2, model.largeConstants().size());
Tensor constant0 = model.largeConstants().get("test_Variable");
assertNotNull(constant0);
assertEquals(new TensorType.Builder().indexed("d2", 784).indexed("d1", 10).build(),
constant0.type());
assertEquals(7840, constant0.size());
Tensor constant1 = model.largeConstants().get("test_Variable_1");
assertNotNull(constant1);
assertEquals(new TensorType.Builder().indexed("d1", 10).build(), constant1.type());
assertEquals(10, constant1.size());
// Check inputs
assertEquals(1, model.inputs().size());
assertTrue(model.inputs().containsKey("Placeholder"));
assertEquals(TensorType.fromSpec("tensor(d0[],d1[784])"), model.inputs().get("Placeholder"));
// Check signature
ExpressionFunction output = model.defaultSignature().outputExpression("add");
assertNotNull(output);
assertEquals("add", output.getBody().getName());
assertEquals("join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(test_Variable), f(a,b)(a * b)), sum, d2), constant(test_Variable_1), f(a,b)(a + b))",
output.getBody().getRoot().toString());
assertEquals(TensorType.fromSpec("tensor(d0[],d1[784])"),
model.inputs().get(model.defaultSignature().inputs().get("Placeholder")));
assertEquals("{Placeholder=tensor(d0[],d1[784])}", output.argumentTypes().toString());
}
@Test
public void testComparisonBetweenOnnxAndTensorflow() {
String tfModelPath = "src/test/files/integration/tensorflow/mnist_softmax/saved";
String onnxModelPath = "src/test/files/integration/onnx/mnist_softmax/mnist_softmax.onnx";
Tensor argument = placeholderArgument();
Tensor tensorFlowResult = evaluateTensorFlowModel(tfModelPath, argument, "Placeholder", "add");
Tensor onnxResult = evaluateOnnxModel(onnxModelPath, argument, "Placeholder", "add");
assertEquals("Operation 'add' produces equal results", tensorFlowResult, onnxResult);
}
private Tensor evaluateTensorFlowModel(String path, Tensor argument, String input, String output) {
ImportedModel model = new TensorFlowImporter().importModel("test", path);
return evaluateExpression(model.expressions().get(output), contextFrom(model), argument, input);
}
private Tensor evaluateOnnxModel(String path, Tensor argument, String input, String output) {
ImportedModel model = new OnnxImporter().importModel("test", path);
return evaluateExpression(model.expressions().get(output), 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)));
result.smallConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(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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