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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.reduce3;
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.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.BaseReduceFloatOp;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.factory.Nd4j;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
/**
* Manhattan distance
*
* @author Adam Gibson
*/
public abstract class BaseReduce3Op extends BaseReduceFloatOp {
public BaseReduce3Op(SameDiff sameDiff, SDVariable i_v, int[] dimensions) {
super(sameDiff, i_v, dimensions);
}
public BaseReduce3Op(SameDiff sameDiff, SDVariable i_v, SDVariable i_v2, int... dimensions) {
super(sameDiff, i_v, i_v2, dimensions);
}
public BaseReduce3Op() {}
public BaseReduce3Op(INDArray x, INDArray y, int... dimensions) {
this(x, y, false, dimensions);
}
public BaseReduce3Op(INDArray x, INDArray y, boolean allDistances, int... dimensions) {
this(x, y, null, true, false, dimensions);
this.isComplex = allDistances;
}
public BaseReduce3Op(INDArray x, INDArray y, INDArray z) {
this(x, y, z, false, false, (int[])null);
}
public BaseReduce3Op(INDArray x, INDArray y, INDArray z, boolean keepDims, int... dimensions){
this(x,y,z,keepDims, false);
}
public BaseReduce3Op(INDArray x, INDArray y, INDArray z, boolean keepDims, boolean allDistances, int... dimensions){
super(x, y, z, keepDims, dimensions);
this.isComplex = allDistances;
extraArgs = new Object[]{0.0f, 0.0f};
}
public BaseReduce3Op(INDArray x, INDArray y, INDArray z, int... dimensions) {
super(x, y, z, false, dimensions);
}
@Override
public Type opType() {
return Type.REDUCE3;
}
@Override
public Type getOpType() {
return opType();
}
@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 DataType resultType() {
if(x.dataType().isFPType())
return x.dataType();
return Nd4j.defaultFloatingPointType();
}
@Override
public List calculateOutputDataTypes(List dataTypes){
//Second input is dynamic axis arg
Preconditions.checkState(dataTypes != null && (dataTypes.size() == 2 || dataTypes.size() == 3),
"Expected 2 or 3 input datatype for %s, got input %s", getClass(), dataTypes);
Preconditions.checkState(dataTypes.size() == 2 || dataTypes.get(2).isIntType(), "When executing distance reductions" +
"with 3 inputs, third input (axis) must be an integer datatype for %s, got %s", getClass(), dataTypes);
//Output data type: always float. TODO let's allow configuration...
if(dataTypes.get(0).isFPType()){
return Collections.singletonList(dataTypes.get(0));
}
return Collections.singletonList(Nd4j.defaultFloatingPointType());
}
}