org.nd4j.linalg.api.ops.impl.layers.convolution.LocalResponseNormalization Maven / Gradle / Ivy
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package org.nd4j.linalg.api.ops.impl.layers.convolution;
import lombok.Builder;
import lombok.Getter;
import lombok.NoArgsConstructor;
import lombok.NonNull;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import onnx.Onnx;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.LocalResponseNormalizationConfig;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.*;
@Slf4j
@Getter
@NoArgsConstructor
public class LocalResponseNormalization extends DynamicCustomOp {
protected LocalResponseNormalizationConfig config;
@Builder(builderMethodName = "sameDiffBuilder")
public LocalResponseNormalization(SameDiff sameDiff, SDVariable[] inputFunctions, boolean inPlace,
LocalResponseNormalizationConfig config) {
super(null,sameDiff, inputFunctions, inPlace);
this.config = config;
addArgs();
}
public LocalResponseNormalization(SameDiff sameDiff, SDVariable input, LocalResponseNormalizationConfig config) {
this(sameDiff, new SDVariable[]{input}, false, config);
}
public LocalResponseNormalization(@NonNull INDArray input, INDArray output, @NonNull LocalResponseNormalizationConfig config){
super(new INDArray[]{input}, wrapOrNull(output));
this.config = config;
addArgs();
}
public LocalResponseNormalization(@NonNull INDArray input, @NonNull LocalResponseNormalizationConfig LocalResponseNormalizationConfig){
super(new INDArray[]{input}, null);
this.config = config;
addArgs();
}
@Override
public Map propertiesForFunction() {
if(config != null)
return config.toProperties();
return Collections.emptyMap();
}
private void addArgs() {
addTArgument(config.getBias());
addTArgument(config.getAlpha());
addTArgument(config.getBeta());
addIArgument(config.getDepth());
}
@Override
public boolean isConfigProperties() {
return true;
}
@Override
public String configFieldName(){
return "config";
}
@Override
public String opName() {
return "lrn";
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
val aAlpha = nodeDef.getAttrOrThrow("alpha");
val aBeta = nodeDef.getAttrOrThrow("beta");
val aBias = nodeDef.getAttrOrThrow("bias");
val aDepth = nodeDef.getAttrOrThrow("depth_radius");
double alpha = aAlpha.getF();
double beta = aBeta.getF();
double bias = aBias.getF();
int depth = (int)aDepth.getI();
LocalResponseNormalizationConfig localResponseNormalizationConfig = LocalResponseNormalizationConfig.builder()
.alpha(alpha)
.beta(beta)
.bias(bias)
.depth((int) depth)
.build();
this.config = localResponseNormalizationConfig;
addArgs();
}
@Override
public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) {
val aAlpha = attributesForNode.get("alpha");
val aBeta = attributesForNode.get("beta");
val aBias = attributesForNode.get("bias");
val aDepth = attributesForNode.get("size");
val alpha = aAlpha.getF();
val beta = aBeta.getF();
val bias = aBias.getF();
val depth = aDepth.getF();
LocalResponseNormalizationConfig localResponseNormalizationConfig = LocalResponseNormalizationConfig.builder()
.alpha(alpha)
.beta(beta)
.bias(bias)
.depth((int) depth)
.build();
this.config = localResponseNormalizationConfig;
addArgs();
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
val depthMapping = PropertyMapping.builder()
.tfAttrName("depth_radius")
.propertyNames(new String[]{"depth"})
.onnxAttrName("size")
.build();
val alphaMapping = PropertyMapping.builder()
.tfAttrName("alpha")
.onnxAttrName("alpha")
.propertyNames(new String[]{"alpha"})
.build();
val betaMapping = PropertyMapping.builder()
.tfAttrName("beta")
.onnxAttrName("beta")
.propertyNames(new String[]{"beta"})
.build();
val biasMapping = PropertyMapping.builder()
.tfAttrName("bias")
.onnxAttrName("bias")
.propertyNames(new String[]{"bias"})
.build();
Map map = new HashMap<>();
map.put("depth",depthMapping);
map.put("alpha",alphaMapping);
map.put("beta",betaMapping);
map.put("bias",biasMapping);
ret.put(tensorflowName(),map);
ret.put(onnxName(),map);
return ret;
}
@Override
public List doDiff(List f1) {
SDVariable[] gradFnInputs = new SDVariable[]{arg(), f1.get(0)};
LocalResponseNormalizationDerivative lrnGrad = LocalResponseNormalizationDerivative.derivativeBuilder()
.inPlace(inPlace)
.sameDiff(sameDiff)
.inputFunctions(gradFnInputs)
.config(config)
.build();
return Collections.singletonList(lrnGrad.outputVariable());
}
@Override
public String onnxName() {
return "LRN";
}
@Override
public String tensorflowName() {
return "LRN";
}
@Override
public List calculateOutputDataTypes(List inputDataTypes){
Preconditions.checkState(inputDataTypes.get(0).isFPType(), "Input 0 should be a floating point type for %s, got %s", getClass(), inputDataTypes.get(0));
return Collections.singletonList(inputDataTypes.get(0));
}
}