org.nd4j.linalg.api.ops.impl.layers.convolution.LocalResponseNormalization Maven / Gradle / Ivy

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/*
 *  ******************************************************************************
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 *  * This program and the accompanying materials are made available under the
 *  * terms of the Apache License, Version 2.0 which is available at
 *  * https://www.apache.org/licenses/LICENSE-2.0.
 *  *
 *  *  See the NOTICE file distributed with this work for additional
 *  *  information regarding copyright ownership.
 *  * Unless required by applicable law or agreed to in writing, software
 *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
 *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
 *  * License for the specific language governing permissions and limitations
 *  * under the License.
 *  *
 *  * SPDX-License-Identifier: Apache-2.0
 *  *****************************************************************************
 */

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));
    }
}