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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.reduce;
import lombok.NoArgsConstructor;
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.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.nd4j.linalg.factory.Nd4j;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Map;
@NoArgsConstructor
public class Moments extends DynamicCustomOp {
private int[] axes;
public Moments(SameDiff sameDiff, SDVariable input) {
this(sameDiff, input, null);
}
public Moments(SameDiff sameDiff, SDVariable input, int[] axes) {
super(null, sameDiff, new SDVariable[] {input}, false);
this.axes = axes;
addArgs();
}
public Moments(INDArray in, INDArray outMean, INDArray outStd, int... axes){
super(null, new INDArray[]{in}, new INDArray[]{outMean, outStd}, null, axes);
}
private void addArgs() {
if(axes != null) {
for (int axis : axes) {
addIArgument(axis);
}
}
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
TFGraphMapper.getInstance().initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
addArgs();
}
@Override
public String opName() {
return "moments";
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
return "moments";
}
@Override
public List doDiff(List grad){
SDVariable dLdMean = grad.get(0);
SDVariable dLdVar = grad.get(1); //Note: non-bias-corrected variance
SDVariable meanBp = f().meanBp(arg(), dLdMean, false, axes);
SDVariable varBp = f().varianceBp(arg(), dLdVar, false, false, axes);
return Collections.singletonList(meanBp.add(varBp));
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes != null && dataTypes.size() == 1, "Expected 1 datatype for %s, got %s", getClass(), dataTypes);
if(dataTypes.get(0).isFPType())
return Arrays.asList(dataTypes.get(0), dataTypes.get(0));
return Arrays.asList(Nd4j.defaultFloatingPointType(), Nd4j.defaultFloatingPointType());
}
}