1 files changed, 0 insertions, 128 deletions
diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TypeConverter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TypeConverter.java
deleted file mode 100644
index 3102d5431d4..00000000000
--- a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TypeConverter.java
+++ /dev/null
@@ -1,128 +0,0 @@
-// Copyright 2018 Yahoo Holdings. 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.OrderedTensorType;
-import com.yahoo.tensor.TensorType;
-import org.tensorflow.framework.AttrValue;
-import org.tensorflow.framework.DataType;
-import org.tensorflow.framework.NodeDef;
-import org.tensorflow.framework.TensorShapeProto;
-
-/**
- * Converts and verifies TensorFlow tensor types into Vespa tensor types.
- *
- * @author lesters
- */
-class TypeConverter {
-
-    static void verifyType(NodeDef node, OrderedTensorType type) {
-        TensorShapeProto shape = tensorFlowShape(node);
-        if (shape != null) {
-            if (shape.getDimCount() != type.rank()) {
-                throw new IllegalArgumentException("TensorFlow shape of '" + node.getName() + "' " +
-                                                   "does not match Vespa shape");
-            }
-            for (int tensorFlowIndex = 0; tensorFlowIndex < type.dimensions().size(); ++tensorFlowIndex) {
-                int vespaIndex = type.dimensionMap(tensorFlowIndex);
-                TensorShapeProto.Dim tensorFlowDimension = shape.getDim(tensorFlowIndex);
-                TensorType.Dimension vespaDimension = type.type().dimensions().get(vespaIndex);
-                if (tensorFlowDimension.getSize() != vespaDimension.size().orElse(-1L)) {
-                    throw new IllegalArgumentException("TensorFlow dimensions of '" + node.getName() + "' " +
-                                                       "does not match Vespa dimensions");
-                }
-            }
-        }
-    }
-
-    static OrderedTensorType typeFrom(NodeDef node) {
-        String dimensionPrefix = "d"; // standard naming convention: d0, d1, ...
-        TensorShapeProto shape = tensorFlowShape(node);
-        OrderedTensorType.Builder builder = new OrderedTensorType.Builder(toValueType(tensorFlowValueType(node)));
-        for (int i = 0; i < shape.getDimCount(); ++ i) {
-            String dimensionName = dimensionPrefix + i;
-            TensorShapeProto.Dim tensorFlowDimension = shape.getDim(i);
-            if (tensorFlowDimension.getSize() >= 0) {
-                builder.add(TensorType.Dimension.indexed(dimensionName, tensorFlowDimension.getSize()));
-            } else {
-                builder.add(TensorType.Dimension.indexed(dimensionName));
-            }
-        }
-        return builder.build();
-    }
-
-    static TensorType typeFrom(org.tensorflow.Tensor<?> tfTensor, String dimensionPrefix) {
-        TensorType.Builder b = new TensorType.Builder(toValueType(tfTensor.dataType()));
-        int dimensionIndex = 0;
-        for (long dimensionSize : tfTensor.shape()) {
-            if (dimensionSize == 0) dimensionSize = 1; // TensorFlow ...
-            b.indexed(dimensionPrefix + (dimensionIndex++), dimensionSize);
-        }
-        return b.build();
-    }
-
-    private static TensorShapeProto tensorFlowShape(NodeDef node) {
-        // Use specific shape if available...
-        AttrValue attrShape = node.getAttrMap().get("shape");
-        if (attrShape != null && attrShape.getValueCase() == AttrValue.ValueCase.SHAPE) {
-            return attrShape.getShape();
-        }
-
-        // ... else use inferred shape
-        AttrValue attrOutputShapes = node.getAttrMap().get("_output_shapes");
-        if (attrOutputShapes == null)
-            throw new IllegalArgumentException("_output_shapes attribute of '" + node.getName() + "' " +
-                                               "does not exist");
-        if (attrOutputShapes.getValueCase() != AttrValue.ValueCase.LIST)
-            throw new IllegalArgumentException("_output_shapes attribute of '" + node.getName() + "' " +
-                                               "is not of expected type");
-
-        return attrOutputShapes.getList().getShape(0); // support multiple outputs?
-    }
-
-    private static DataType tensorFlowValueType(NodeDef node) {
-        AttrValue attrValueList = node.getAttrMap().get("dtypes");
-        if (attrValueList == null)
-            return DataType.DT_DOUBLE; // default. This will usually (always?) be used. TODO: How can we do better?
-        if (attrValueList.getValueCase() != AttrValue.ValueCase.LIST)
-            return DataType.DT_DOUBLE; // default
-
-        return attrValueList.getList().getType(0); // support multiple outputs?
-    }
-
-    private static TensorType.Value toValueType(DataType dataType) {
-        switch (dataType) {
-            case DT_FLOAT: return TensorType.Value.FLOAT;
-            case DT_DOUBLE: return TensorType.Value.DOUBLE;
-            // Imperfect conversion, for now:
-            case DT_BOOL: return TensorType.Value.FLOAT;
-            case DT_BFLOAT16: return TensorType.Value.FLOAT;
-            case DT_HALF: return TensorType.Value.FLOAT;
-            case DT_INT8: return TensorType.Value.FLOAT;
-            case DT_INT16: return TensorType.Value.DOUBLE;
-            case DT_INT32: return TensorType.Value.DOUBLE;
-            case DT_INT64: return TensorType.Value.DOUBLE;
-            case DT_UINT8: return TensorType.Value.FLOAT;
-            case DT_UINT16: return TensorType.Value.DOUBLE;
-            case DT_UINT32: return TensorType.Value.DOUBLE;
-            case DT_UINT64: return TensorType.Value.DOUBLE;
-            default: throw new IllegalArgumentException("A TensorFlow tensor with data type " + dataType +
-                                                        " cannot be converted to a Vespa tensor type");
-        }
-    }
-
-    private static TensorType.Value toValueType(org.tensorflow.DataType dataType) {
-        switch (dataType) {
-            case FLOAT: return TensorType.Value.FLOAT;
-            case DOUBLE: return TensorType.Value.DOUBLE;
-            // Imperfect conversion, for now:
-            case BOOL: return TensorType.Value.FLOAT;
-            case INT32: return TensorType.Value.DOUBLE;
-            case UINT8: return TensorType.Value.FLOAT;
-            case INT64: return TensorType.Value.DOUBLE;
-            default: throw new IllegalArgumentException("A TensorFlow tensor with data type " + dataType +
-                                                        " cannot be converted to a Vespa tensor type");
-        }
-    }
-
-}