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/*
* ******************************************************************************
* *
* *
* * 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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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.NonNull;
import lombok.val;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.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.*;
/**
* Split op
*/
public class Split extends DynamicCustomOp {
private int numSplit;
private int splitDim;
public Split() {
}
public Split(SameDiff sameDiff, SDVariable input, int numSplit, int splitDim) {
super(null,sameDiff,new SDVariable[]{input});
this.numSplit = numSplit;
this.splitDim = splitDim;
addIArgument(numSplit,splitDim);
}
public Split(@NonNull INDArray in, INDArray out) {
super(null, new INDArray[]{in}, wrapOrNull(out), null, (List)null);
}
public Split(INDArray input, int numSplit, int splitDim) {
super(null,input,null,Collections.emptyList(),new int[0]);
addIArgument(numSplit,splitDim);
}
@Override
public String opName() {
return "split";
}
@Override
public String tensorflowName() {
return "Split";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
val numSplits = (int) attributesForNode.get("num_split").getI();
this.numSplit = numSplits;
addIArgument(numSplits);
val splitDim = TFGraphMapper.getArrayFrom(TFGraphMapper.getNodeWithNameFromGraph(graph,nodeDef.getInput(0)),graph);
if(splitDim != null) {
this.splitDim = splitDim.getInt(0);
addIArgument(splitDim.getInt(0));
}
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
Map map = new HashMap<>();
val splitDim = PropertyMapping.builder()
.tfInputPosition(0)
.propertyNames(new String[]{"splitDim"})
.build();
val numSplit = PropertyMapping.builder()
.tfAttrName("num_split")
.propertyNames(new String[]{"numSplit"})
.build();
map.put("numSplit",numSplit);
map.put("splitDim",splitDim);
ret.put(tensorflowName(),map);
return ret;
}
@Override
public int getNumOutputs(){
return numSplit;
}
@Override
public List calculateOutputDataTypes(List dataTypes) {
Preconditions.checkState(dataTypes != null && !dataTypes.isEmpty(), "No datatypes were provided for %s: %s", getClass(), dataTypes);
DataType dt;
if(dataTypes.size() == 1) {
dt = dataTypes.get(0);
} else {
//Order seems to usually be axis first for TF import? libnd4j supports both...
if(dataTypes.get(0).isIntType()){
dt = dataTypes.get(1);
} else {
dt = dataTypes.get(0);
}
}
//Output types are same as first input type - just numSplits of them...
List out = new ArrayList<>(numSplit);
for( int i = 0; i < numSplit; i++) {
out.add(dt);
}
return out;
}
}