org.nd4j.linalg.api.ops.impl.shape.OneHot Maven / Gradle / Ivy

Show more of this group Show more artifacts with this name
Show all versions of nd4j-api Show documentation
There is a newer version: 1.0.0-M2.1
/*
 *  ******************************************************************************
 *  *
 *  *
 *  * 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.shape;

import lombok.val;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
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.DynamicCustomOp;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;

import java.util.*;

public class OneHot extends DynamicCustomOp {
    public static final DataType DEFAULT_DTYPE = DataType.FLOAT;

    private int depth;
    private int jaxis = -1;
    private double on;
    private double off;
    private DataType outputType;

    public  OneHot() {

    }

    public OneHot(SameDiff sameDiff, SDVariable indices, int depth) {
        this(sameDiff, indices, depth, -1, 1, 0, DEFAULT_DTYPE);
    }

    public OneHot(SameDiff sameDiff, SDVariable indices, int depth, int axis, double on, double off, DataType dataType) {
        super(null, sameDiff,  new SDVariable[] {indices}, false);
        this.depth = depth;
        this.jaxis = axis;
        this.on = on;
        this.off = off;
        addArgs();
        this.outputType = dataType;
    }

    public OneHot(INDArray indices, INDArray output, int depth) {
        this(indices, output, depth, -1, 1, 0);
    }

    public OneHot(INDArray indices, int depth) {
        this(indices, null, depth, 0, 1.0, 0.0);
    }

    public OneHot(INDArray indices, INDArray output, int depth, int axis, double on, double off) {
        super(null, indices, output, null, null);
        this.depth = depth;
        this.jaxis = axis;
        this.on = on;
        this.off = off;
        addArgs();
    }

    public OneHot(INDArray indices, int depth, int axis, double on, double off) {
        this(indices, null, depth, axis, on, off);
    }

    public OneHot(INDArray indices, int depth, int axis, double on, double off, DataType dataType) {
        this(indices, null, depth, axis, on, off);
        this.outputType = dataType;
        if (outputType != null)
            addDArgument(outputType);
    }

    protected void addArgs() {
        addIArgument(jaxis);
        addIArgument(depth);
        addTArgument(on);
        addTArgument(off);

        if (outputType != null)
            addDArgument(outputType);
    }

    @Override
    public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
        TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
        addArgs();
        if(attributesForNode.containsKey("T")) {
            outputType = TFGraphMapper.convertType(attributesForNode.get("T").getType());
        }
    }


    @Override
    public Map> mappingsForFunction() {
        Map> ret = new HashMap<>();
        Map attrs = new LinkedHashMap<>();

        val depth = PropertyMapping.builder()
                .propertyNames(new String[]{"depth"})
                .tfInputPosition(1)
                .build();
        attrs.put("depth", depth);

        val on = PropertyMapping.builder()
                .propertyNames(new String[]{"on"})
                .tfInputPosition(2)
                .build();
        attrs.put("on", on);

        val off = PropertyMapping.builder()
                .propertyNames(new String[]{"off"})
                .tfInputPosition(3)
                .build();
        attrs.put("off", off);


        val axis = PropertyMapping.builder()
                .propertyNames(new String[] {"jaxis"})
                .tfAttrName("axis")
                .build();
        attrs.put("jaxis",axis);

        ret.put(tensorflowName(),attrs);
        return ret;
    }

    @Override
    public String tensorflowName() {
        return "OneHot";
    }

    @Override
    public String onnxName() {
        throw new NoOpNameFoundException("No onnx name found for " + opName());
    }

    @Override
    public String opName() {
        return "onehot";
    }

    @Override
    public List doDiff(List i_v) {
        return Collections.singletonList(sameDiff.zerosLike(arg()));
    }

    @Override
    public List calculateOutputDataTypes(List dataTypes){
        Preconditions.checkState(dataTypes.size() >= 1 && dataTypes.size() <= 4, "Expected list with 1 to 4 datatypes for %s, got %s", getClass(), dataTypes);
        if(outputType != null){
            return Collections.singletonList(outputType);
        } else {
            return Collections.singletonList(DEFAULT_DTYPE);
        }
    }
}