org.nd4j.linalg.api.ops.impl.shape.bp.ConcatBp Maven / Gradle / Ivy
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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.bp;
import lombok.extern.slf4j.Slf4j;
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.NoOpNameFoundException;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.Op;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.*;
/**
* Backprop op for concat
*
* @author Alex Black
*/
@Slf4j
public class ConcatBp extends DynamicCustomOp {
private int concatDimension;
public ConcatBp(){
}
/**
*
* @param sameDiff
* @param concatDimension
* @param inputsAndGrad Original inputs, followed by output gradient
*/
public ConcatBp(SameDiff sameDiff, int concatDimension, SDVariable... inputsAndGrad){
super(null, sameDiff, inputsAndGrad);
addIArgument(concatDimension);
this.concatDimension = concatDimension;
}
@Override
public String opName() {
return "concat_bp";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
//No op
}
@Override
public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) {
//No op
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
throw new NoOpNameFoundException("No tensorflow op opName found for " + opName());
}
@Override
public Op.Type opType() {
return Op.Type.CUSTOM;
}
@Override
public int getNumOutputs(){
return args().length - 1;
}
@Override
public List calculateOutputDataTypes(List dataTypes){
SDVariable[] args = args();
Preconditions.checkState(dataTypes.size() == args.length, "Expected list with exactly %s datatypes (original inputs + gradient), got %s", args.length, dataTypes);
//Output type is same as (original) input types
int n = getNumOutputs();
List out = new ArrayList<>(n);
for( int i=0; i