org.nd4j.linalg.api.ops.impl.transforms.dtype.Cast Maven / Gradle / Ivy
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package org.nd4j.linalg.api.ops.impl.transforms.dtype;
import lombok.NonNull;
import lombok.val;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.autodiff.samediff.serde.FlatBuffersMapper;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.converters.DifferentialFunctionClassHolder;
import org.nd4j.imports.descriptors.properties.AttributeAdapter;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
import org.nd4j.imports.descriptors.properties.adapters.DataTypeAdapter;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.BaseDynamicTransformOp;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.lang.reflect.Field;
import java.util.*;
public class Cast extends BaseDynamicTransformOp {
private DataType typeDst;
public Cast() {
//
}
public Cast(SameDiff sameDiff, SDVariable arg, @NonNull DataType dst) {
super(sameDiff, new SDVariable[] {arg}, false);
this.typeDst = dst;
addArgs();
}
public Cast(@NonNull INDArray arg, @NonNull DataType dataType) {
super(new INDArray[]{arg}, null);
this.typeDst = dataType;
addArgs();
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
addArgs();
}
protected void addArgs() {
addIArgument(FlatBuffersMapper.getDataTypeAsByte(typeDst));
}
@Override
public Map> attributeAdaptersForFunction() {
Map> ret = new LinkedHashMap<>();
Map tfAdapters = new LinkedHashMap<>();
val fields = DifferentialFunctionClassHolder.getInstance().getFieldsForFunction(this);
tfAdapters.put("typeDst", new DataTypeAdapter());
ret.put(tensorflowName(),tfAdapters);
return ret;
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
Map map = new HashMap<>();
val dstMapping = PropertyMapping.builder()
.tfAttrName("DstT")
.propertyNames(new String[]{"typeDst"})
.build();
for(val propertyMapping : new PropertyMapping[] {dstMapping}) {
for (val keys : propertyMapping.getPropertyNames())
map.put(keys, propertyMapping);
}
ret.put(tensorflowName(),map);
return ret;
}
@Override
public void setValueFor(Field target, Object value) {
//This is a hack around a property mapping issue - TF datatype DT_DOUBLE return attribute.getType() of DT_DOUBLE which doesn't make sense
if(value == null || value instanceof String || value instanceof DataType) {
super.setValueFor(target, value);
}
}
@Override
public String opName() {
return "cast";
}
@Override
public String tensorflowName() {
return "Cast";
}
@Override
public List doDiff(List i_v) {
//If input is numerical: reverse cast. Otherwise 0
if(arg().dataType().isFPType()){
return Collections.singletonList(i_v.get(0).castTo(arg().dataType()));
} else {
return Collections.singletonList(sameDiff.zerosLike(arg()));
}
}
@Override
public List calculateOutputDataTypes(List dataTypes) {
//All scalar ops: output type is same as input type
Preconditions.checkState(dataTypes != null && dataTypes.size() == 1, "Expected exactly 1 input datatype for %s, got input %s", getClass(), dataTypes);
return Collections.singletonList(typeDst);
}
}