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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.tensorops;
import lombok.Getter;
import lombok.val;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ops.Op;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.util.ArrayUtil;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
public class TensorArray extends BaseTensorOp {
@Getter
protected DataType tensorArrayDataType;
@Override
public String tensorflowName() {
return "TensorArrayV3";
}
public TensorArray(String name, SameDiff sameDiff, DataType dataType){
super(name, sameDiff, new SDVariable[]{});
this.tensorArrayDataType = dataType;
}
public TensorArray(SameDiff sameDiff, DataType dataType){
super(sameDiff, new SDVariable[]{});
this.tensorArrayDataType = dataType;
}
public TensorArray(TensorArray ta){
super(ta.sameDiff, new SDVariable[]{});
this.tensorArrayDataType = ta.tensorArrayDataType;
}
public TensorArray(TensorArray ta, SDVariable[] inputs){
super(ta.sameDiff, inputs);
this.tensorArrayDataType = ta.tensorArrayDataType;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
val idd = nodeDef.getInput(nodeDef.getInputCount() - 1);
NodeDef iddNode = null;
for(int i = 0; i < graph.getNodeCount(); i++) {
if(graph.getNode(i).getName().equals(idd)) {
iddNode = graph.getNode(i);
}
}
val arr = TFGraphMapper.getInstance().getNDArrayFromTensor("value",iddNode,graph);
if (arr != null) {
int idx = arr.getInt(0);
addIArgument(idx);
}
this.tensorArrayDataType = TFGraphMapper.convertType(attributesForNode.get("dtype").getType());
}
public TensorArray(){
this(DataType.FLOAT);
}
public TensorArray(DataType dataType){
this.tensorArrayDataType = dataType;
}
@Override
public String toString() {
return opName();
}
@Override
public String opName() {
return "tensorarrayv3";
}
@Override
public Op.Type opType() {
return Op.Type.CUSTOM;
}
private SDVariable getVar(){
return outputVariable();
}
@Override
public SameDiff getSameDiff(){
val sd = this.sameDiff;
if (sd.getChild() != null){
return sd.getChild();
}
return sd;
}
private SDVariable intToVar(int... index){
return this.sameDiff.constant(Nd4j.createFromArray(index));
}
//----------- read ops-----------------\\
public SDVariable read(int index){
return new TensorArrayRead(getSameDiff(), new SDVariable[]{getVar(), intToVar(index)}).outputVariable();
}
public SDVariable read(SDVariable index){
return new TensorArrayRead(getSameDiff(), new SDVariable[]{getVar(), index}).outputVariable();
}
public SDVariable gather(SDVariable flow, int... indices){
return new TensorArrayGather(getSameDiff(), new SDVariable[]{getVar(), sameDiff.constant(Nd4j.createFromArray(indices)), flow}).outputVariable();
}
public SDVariable gather(SDVariable flow, SDVariable indices){
return new TensorArrayGather(getSameDiff(), new SDVariable[]{getVar(), indices, flow}).outputVariable();
}
public SDVariable stack(SDVariable flow){
return new TensorArrayGather(getSameDiff(), new SDVariable[]{getVar(), intToVar(-1), flow}).outputVariable();
}
public SDVariable concat(SDVariable flow){
return new TensorArrayConcat(getSameDiff(), new SDVariable[]{getVar()}).outputVariable();
}
//----------- write ops-----------------\\
public SDVariable write(SDVariable flow, int index, SDVariable value){
return write(flow, intToVar(index), value);
}
public SDVariable write(SDVariable flow, SDVariable index, SDVariable value){
return new TensorArrayWrite(getSameDiff(),
new SDVariable[]{getVar(),
index, value, flow}).outputVariable();
}
public SDVariable scatter(SDVariable flow, SDVariable value, int... indices){
return new TensorArrayScatter(getSameDiff(),
new SDVariable[]{getVar(),
intToVar(indices),
value, flow}).outputVariable();
}
public SDVariable scatter(SDVariable flow, SDVariable value, SDVariable indices){
return new TensorArrayScatter(getSameDiff(),
new SDVariable[]{getVar(),
indices,
value, flow}).outputVariable();
}
public SDVariable unstack(SDVariable flow, SDVariable value){
return new TensorArrayScatter(getSameDiff(),
new SDVariable[]{getVar(),
intToVar(-1),
value, flow}).outputVariable();
}
@Override
public List calculateOutputDataTypes(List inputDataType){
//The SDVariable that is the output of this "function" is just a dummy variable anyway...
//Usually 2 outputs... seems like first one is dummy, second one is a float??
//TODO work out exactly what this second output is for (it's used in TensorArrayWrite for example...
return Arrays.asList(DataType.BOOL, DataType.FLOAT);
}
@Override
public int getNumOutputs(){
return 2;
}
}
