org.nd4j.linalg.api.ops.impl.controlflow.compat.BaseCompatOp Maven / Gradle / Ivy
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package org.nd4j.linalg.api.ops.impl.controlflow.compat;
import java.util.List;
import lombok.NonNull;
import lombok.val;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.NoOpNameFoundException;
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.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.nd4j.linalg.factory.Nd4j;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.HashMap;
import java.util.Map;
public abstract class BaseCompatOp extends DynamicCustomOp {
protected String frameName;
public BaseCompatOp(SameDiff sameDiff, SDVariable[] inputs){
super(null, sameDiff, inputs);
}
public BaseCompatOp(INDArray... inputs) {
addInputArgument(inputs);
}
public BaseCompatOp(){
}
public String getFrameName() {
if(numSArguments() > 0)
return getSArgument(0);
return frameName;
}
public void setFrameName(@NonNull String frameName) {
this.frameName = frameName;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode,nodeDef, graph);
}
@Override
public Map propertiesForFunction() {
Map ret = new HashMap<>();
if(frameName != null)
ret.put("frameName",frameName);
return ret;
}
@Override
public void configureFromArguments() {
super.configureFromArguments();
}
@Override
public void setPropertiesForFunction(Map properties) {
super.setPropertiesForFunction(properties);
if(properties.containsKey("frameName")) {
String frameName = getStringFromProperty("frameName",properties);
this.frameName = frameName;
}
}
@Override
public void configureWithSameDiff(SameDiff sameDiff) {
super.configureWithSameDiff(sameDiff);
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
Map map = new HashMap<>();
val frameNameMapping = PropertyMapping.builder()
.tfAttrName("frame_name")
.onnxAttrName("frame_name") // not sure if it exists in onnx
.propertyNames(new String[]{"frameName"})
.build();
map.put("frameName", frameNameMapping);
try {
ret.put(onnxName(), map);
}catch(NoOpNameFoundException e) {
//ignore, we dont care about onnx for this set of ops
}
try {
ret.put(tensorflowName(),map);
}catch(NoOpNameFoundException e) {
//ignore
}
return ret;
}
@Override
public void computeArrays() {
if(sameDiff.isEagerMode()) {
SDVariable[] args = args();
//special work around for non existing arrays like nextiteration that aren't computed till last
//note we do this in case shape related ops are impacted by the stub arrays during calculation
//usually in this situation shapes can be disregarded and won't impact normal compute
long[] shape = new long[6];
for(int i = 0; i < shape.length; i++)
shape[i] = 1;
INDArray arr = Nd4j.scalar(1.0f).reshape(shape);
outputVariables[0].setShape(arr.shape());
sameDiff.setEagerArrForVarName(outputVariables[0].name(),arr);
}
}
@Override
public void addSArgument(String... args) {
super.addSArgument(args);
if(args != null && args.length >= 1) {
setFrameName(args[0]);
}
}
@Override
public Map> attributeAdaptersForFunction() {
return super.attributeAdaptersForFunction();
}
@Override
public List calculateOutputShape() {
throw new UnsupportedOperationException("calculateOutputShape() is not supported for control flow ops.");
}
}