Revert "Import Tensorflow models vis ONNX conversion"

This reverts commit 0a886d74d4c9ffde41eef1f7e3c186b60b9f3726.

1 files changed, 43 insertions, 0 deletions

diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxMnistSoftmaxImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxMnistSoftmaxImportTestCase.java
index 09455abc380..35c853bd746 100644
--- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxMnistSoftmaxImportTestCase.java
+++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/OnnxMnistSoftmaxImportTestCase.java
@@ -55,4 +55,47 @@ public class OnnxMnistSoftmaxImportTestCase {
         assertEquals("{Placeholder=tensor<float>(d0[],d1[784])}", output.argumentTypes().toString());
     }
 
+    @Test
+    public void testComparisonBetweenOnnxAndTensorflow() {
+        String tfModelPath = "src/test/models/tensorflow/mnist_softmax/saved";
+        String onnxModelPath = "src/test/models/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.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();
+    }
+
+
 }