org.nd4j.linalg.api.ops.impl.shape.ShapeN Maven / Gradle / Ivy
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package org.nd4j.linalg.api.ops.impl.shape;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.NoOpNameFoundException;
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.ArrayList;
import java.util.List;
import java.util.Map;
public class ShapeN extends DynamicCustomOp {
protected DataType dataType;
public ShapeN() {}
public ShapeN(SameDiff sameDiff, SDVariable[] inputs, boolean inPlace) {
super(null, sameDiff, inputs, inPlace);
}
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx name found for shape " + opName());
}
@Override
public String opName() {
return "shapes_of";
}
@Override
public String tensorflowName() {
return "ShapeN";
}
@Override
public List doDiff(List i_v) {
List out = new ArrayList<>();
for(SDVariable in : args()){
out.add(sameDiff.zerosLike(in));
}
return out;
}
@Override
public int getNumOutputs(){
return args().length;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
super.initFromTensorFlow(nodeDef, initWith, attributesForNode, graph);
dataType = TFGraphMapper.convertType(nodeDef.getAttrOrThrow("out_type").getType());
}
@Override
public List calculateOutputDataTypes(List dataTypes){
//Output type is always long (i.e., shape of array) - for each input
//TODO TF allows customizing int or long
int n = getNumOutputs();
List outputTypes = new ArrayList<>(n);
for(int i=0; i