org.nd4j.linalg.api.ops.impl.transforms.custom.ParallelConcat Maven / Gradle / Ivy
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* * terms of the Apache License, Version 2.0 which is available at
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package org.nd4j.linalg.api.ops.impl.transforms.custom;
import lombok.extern.slf4j.Slf4j;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.descriptors.properties.AttributeAdapter;
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.*;
@Slf4j
public class ParallelConcat extends DynamicCustomOp {
public ParallelConcat() {
// we know that axis is always 0 for PC
iArguments.add(0L);
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
// We might want to import everything here? i.e. shape in advance?
}
@Override
public String opName() {
// :)
return "ParallelConcat";
}
@Override
public int getNumOutputs() {
return 1;
}
@Override
public String tensorflowName() {
return "ParallelConcat";
}
@Override
public Map> attributeAdaptersForFunction() {
Map> ret = new HashMap<>();
Map tfMappings = new LinkedHashMap<>();
return ret;
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
Map map = new HashMap<>();
return ret;
}
@Override
public List calculateOutputDataTypes(List dataTypes){
DataType first = dataTypes.get(0);
for(int i = 1; i < dataTypes.size(); i++) {
DataType dt = dataTypes.get(i);
Preconditions.checkState(first == dt, "Data types must all be equal: got %s", dataTypes);
}
return Collections.singletonList(first);
}
}