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org.nd4j.linalg.api.ops.impl.accum.CumProd 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.accum;
import lombok.val;
import onnx.OnnxProto3;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
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.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.*;
public class CumProd extends DynamicCustomOp {
protected boolean exclusive = false;
protected boolean reverse = false;
protected int[] axis = new int[0];
public CumProd() {
}
public CumProd(SameDiff sameDiff, SDVariable x, int... axis) {
this(sameDiff, x, false, false, axis);
}
public CumProd(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.axis = axis;
tArguments.clear();
iArguments.clear();
addArgs();
}
public CumProd(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.axis = axis;
tArguments.clear();
iArguments.clear();
addArgs();
}
@Override
public String opName() {
return "cumprod";
}
@Override
public String tensorflowName() {
return "Cumprod";
}
@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.getInstance().initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
addArgs();
}
protected void addArgs() {
addIArgument(exclusive ? 1 : 0, reverse ? 1 : 0);
if (axis != null)
for (val a: axis)
addIArgument(a);
}
@Override
public void initFromOnnx(OnnxProto3.NodeProto node, SameDiff initWith, Map attributesForNode, OnnxProto3.GraphProto graph) {
super.initFromOnnx(node, initWith, attributesForNode, graph);
}
@Override
public List doDiff(List grad) {
return Collections.singletonList(f().cumprodBp(arg(0), grad.get(0), exclusive, reverse, axis));
}
}