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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.linalg.api.ops.impl.shape;
import lombok.NoArgsConstructor;
import lombok.val;
import onnx.Onnx;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.*;
/**
* Repeat function
*
* @author Adam Gibson
*/
@NoArgsConstructor
public class Repeat extends DynamicCustomOp {
private int jaxis;
public Repeat(int axis) {
this.jaxis = axis;
}
public Repeat(SameDiff sameDiff, SDVariable[] args, int axis) {
super(null, sameDiff, args);
this.jaxis = axis;
}
public Repeat(INDArray[] inputs, INDArray[] outputs, List tArguments, List iArguments, int axis) {
super(null, inputs, outputs, tArguments, iArguments);
this.jaxis = axis;
}
public Repeat(INDArray[] inputs, INDArray[] outputs, int axis) {
super(null, inputs, outputs);
this.jaxis = axis;
}
public Repeat(SameDiff sameDiff, SDVariable[] args, boolean inPlace, int axis) {
super(null, sameDiff, args, inPlace);
this.jaxis = axis;
}
@Override
public Map propertiesForFunction() {
return Collections.singletonMap("axis", axis);
}
@Override
public String opName() {
return "repeat";
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
Map map = new HashMap<>();
val axisMapping = PropertyMapping.builder()
.onnxAttrName("axis")
.tfInputPosition(-1)
.propertyNames(new String[]{"axis"})
.build();
map.put("axis", axisMapping);
ret.put(tensorflowName(), map);
ret.put(onnxName(), map);
return ret;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
TFGraphMapper.getInstance().initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
addIArgument(jaxis);
}
@Override
public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) {
super.initFromOnnx(node, initWith, attributesForNode, graph);
}
@Override
public String onnxName() {
return "Repeat";
}
@Override
public String tensorflowName() {
return "Repeat";
}
@Override
public List doDiff(List i_v) {
SDVariable ret = outputVariables()[0];
return Collections.singletonList(ret);
}
@Override
public List calculateOutputDataTypes(List dataTypes){
//Output type is always same as input type
return Collections.singletonList(dataTypes.get(0));
}
}