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org.nd4j.linalg.api.ops.impl.transforms.custom.CumSum Maven / Gradle / Ivy
/*******************************************************************************
* 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.transforms.custom;
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.descriptors.properties.AttributeAdapter;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
import org.nd4j.imports.descriptors.properties.adapters.BooleanAdapter;
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.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.*;
/**
* Cumulative sum operation, optionally along dimension.
*
* @author Alex Black
*/
public class CumSum extends DynamicCustomOp {
protected boolean exclusive = false;
protected boolean reverse = false;
protected int[] jaxis = new int[0];
public CumSum() {
}
public CumSum(SameDiff sameDiff, SDVariable x, int... axis) {
this(sameDiff, x, false, false, axis);
}
public CumSum(SameDiff sameDiff, SDVariable x, boolean exclusive, boolean reverse, int... axis) {
super(null, sameDiff, new SDVariable[]{x});
this.sameDiff = sameDiff;
this.exclusive = exclusive;
this.reverse = reverse;
this.jaxis = axis;
addArgs();
}
public CumSum(INDArray in, INDArray result, boolean exclusive, boolean reverse, int... axis) {
super(null, new INDArray[]{in}, new INDArray[]{result}, null, (List)null);
this.exclusive = exclusive;
this.reverse = reverse;
this.jaxis = axis;
addArgs();
}
@Override
public String opName() {
return "cumsum";
}
@Override
public String tensorflowName() {
return "Cumsum";
}
@Override
public Map> attributeAdaptersForFunction() {
Map> ret = new HashMap<>();
Map tfMappings = new LinkedHashMap<>();
tfMappings.put("exclusive", new BooleanAdapter());
tfMappings.put("reverse", new BooleanAdapter());
ret.put(tensorflowName(), tfMappings);
return ret;
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
Map map = new HashMap<>();
val exclusiveMapper = PropertyMapping.builder()
.tfAttrName("exclusive")
.propertyNames(new String[]{"exclusive"})
.build();
val reverseMapper = PropertyMapping.builder()
.tfAttrName("reverse")
.propertyNames(new String[]{"reverse"})
.build();
map.put("exclusive", exclusiveMapper);
map.put("reverse", reverseMapper);
ret.put(tensorflowName(), map);
return ret;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
addArgs();
}
protected void addArgs() {
addIArgument(exclusive ? 1 : 0, reverse ? 1 : 0);
for (val a: jaxis)
addIArgument(jaxis);
}
@Override
public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) {
super.initFromOnnx(node, initWith, attributesForNode, graph);
}
@Override
public List doDiff(List grad) {
return Collections.singletonList(f().cumsumBp(arg(0), grad.get(0), exclusive, reverse, jaxis));
}
@Override
public List calculateOutputDataTypes(List dataTypes){
Preconditions.checkState(dataTypes != null && (dataTypes.size() == 1 || dataTypes.size() == 2),
"Expected 1 or 2 input datatype for %s, got %s", getClass(), dataTypes); //2nd optional input - axis
return Collections.singletonList(dataTypes.get(0));
}
}