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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.transforms.custom;
import lombok.val;
import org.apache.commons.lang3.ArrayUtils;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
import org.nd4j.linalg.api.buffer.DataType;
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.*;
/**
* Transforms a given input tensor into numPartitions partitions, as indicated by the indices in "partitions".
* Output tensor has one more dimension than input tensor, the first dimension indicates the partition.
*
* Example:
*
* input: [4, 3, 5, 7, 8, 0]
* input shape: [1, 6]
* partitions: [1, 0, 1, 0, 0, 1]
* numPartitions: 2
* outputs[0]: [3, 7, 8]
* outputs[1]: [4, 5, 0]
*
* @author Max Pumperla
*/
public class DynamicPartition extends DynamicCustomOp {
private int numPartitions;
private SDVariable partitions;
public DynamicPartition() {
}
public DynamicPartition(SameDiff sameDiff, SDVariable input, SDVariable partitions, int numPartitions) {
super(null, sameDiff, new SDVariable[] {input, partitions}, false);
this.partitions = partitions;
this.numPartitions = numPartitions;
addArgs();
}
@Override
public List doDiff(List i_v) {
return Arrays.asList(f().dynamicPartitionBp(arg(0), arg(1), i_v.toArray(new SDVariable[i_v.size()]), numPartitions));
}
protected void addArgs() {
addIArgument(numPartitions);
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
TFGraphMapper.getInstance().initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
addArgs();
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
Map attrs = new LinkedHashMap<>();
val numPartitions = PropertyMapping.builder()
.tfAttrName("num_partitions")
.propertyNames(new String[]{"numPartitions"})
.build();
attrs.put("numPartitions", numPartitions);
ret.put(tensorflowName(),attrs);
return ret;
}
@Override
public String opName() {
return "dynamic_partition";
}
@Override
public String tensorflowName() {
return "DynamicPartition";
}
@Override
public String onnxName() {
return "Dynamic partitioning currently not supported by ONNX";
}
@Override
public int getNumOutputs(){
return numPartitions;
}
@Override
public List calculateOutputDataTypes(List dataTypes){
//Output type: same as (data) input type
List out = new ArrayList<>(numPartitions);
for( int i=0; i