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* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
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* * information regarding copyright ownership.
* * Unless required by applicable law or agreed to in writing, software
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* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* * SPDX-License-Identifier: Apache-2.0
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package org.nd4j.linalg.api.ops.impl.shape;
import lombok.NonNull;
import lombok.val;
import onnx.Onnx;
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.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.*;
public class Unstack extends DynamicCustomOp {
// TODO: libnd4j currently doesn't support "num", number of outputs is inferred.
private int num = -1;
private int jaxis;
public Unstack() {
}
public Unstack(SameDiff sameDiff, SDVariable value, int axis) {
super(null, sameDiff, new SDVariable[]{value}, false);
this.jaxis = axis;
if (value.getShape() != null){
if (value.getShape()[axis] != -1){
num = (int)value.getShape()[axis];
}
}
if (num <= 0){
throw new ND4JIllegalStateException("Unstack: Unable to infer number of outputs from input. Provide number of outputs explicitly.");
}
addArgs();
}
public Unstack(SameDiff sameDiff, SDVariable value, int axis, int num) {
super(null, sameDiff, new SDVariable[]{value}, false);
this.jaxis = axis;
this.num = num;
addArgs();
}
public Unstack(@NonNull INDArray value, int axis, int num){
super(new INDArray[]{value}, null);
this.jaxis = axis;
this.num = num;
addArgs();
}
public Unstack(INDArray in, INDArray[] out, int axis){
super(null, new INDArray[]{in}, out, null, (int[])null);
this.jaxis = axis;
addArgs();
}
public void addArgs() {
addIArgument(jaxis);
}
@Override
public String[] tensorflowNames() {
return new String[]{"Unstack", "Unpack"};
}
@Override
public String tensorflowName() {
return "Unstack";
}
@Override
public String opName() {
return "unstack";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
val attrAxis = nodeDef.getAttrOrThrow("axis");
int axis = (int) attrAxis.getI();
this.jaxis = axis;
val attrNum = nodeDef.getAttrOrDefault("num", null);
if(attrNum != null){
this.num = (int) attrNum.getI();
}
addArgs();
}
@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);
return ret;
}
@Override
public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) {
throw new UnsupportedOperationException("No analog found for onnx for " + opName());
}
@Override
public int getNumOutputs(){
return num;
}
@Override
public List doDiff(List f1) {
return Collections.singletonList(sameDiff.stack(jaxis, f1.toArray(new SDVariable[0])));
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes.size() == 1, "Expected list with exactly 1 datatype for %s, got %s", getClass(), dataTypes);
//Output types are same as input type - i.e., just unpack rank R array into N rank R-1 arrays
List out = new ArrayList<>();
for( int i=0; i