org.deeplearning4j.nn.layers.convolution.Cropping1DLayer Maven / Gradle / Ivy

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 *  *  information regarding copyright ownership.
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package org.deeplearning4j.nn.layers.convolution;

import lombok.val;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.convolutional.Cropping1D;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.AbstractLayer;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.common.primitives.Pair;

import static org.nd4j.linalg.indexing.NDArrayIndex.all;
import static org.nd4j.linalg.indexing.NDArrayIndex.interval;

public class Cropping1DLayer extends AbstractLayer {

    private int[] cropping; //[padTop, padBottom]

    public Cropping1DLayer(NeuralNetConfiguration conf, DataType dataType) {
        super(conf, dataType);
        this.cropping = ((Cropping1D) conf.getLayer()).getCropping();
    }

    @Override
    public boolean isPretrainLayer() {
        return false;
    }

    @Override
    public void clearNoiseWeightParams() {
        //No op
    }

    @Override
    public Type type() {
        return Type.CONVOLUTIONAL;
    }

    @Override
    public Pair backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {
        val inShape = input.shape();
        INDArray epsNext = workspaceMgr.create(ArrayType.ACTIVATION_GRAD, dataType, inShape, 'c');
        INDArray epsNextSubset = epsNext.get(all(), all(), interval(cropping[0], epsNext.size(2)-cropping[1]));
        epsNextSubset.assign(epsilon);
        return new Pair<>((Gradient) new DefaultGradient(), epsNext);
    }


    @Override
    public INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr) {
        assertInputSet(false);
        return inputSubset(input, ArrayType.ACTIVATIONS, workspaceMgr);
    }

    @Override
    public Layer clone() {
        return new Cropping2DLayer(conf.clone(), dataType);
    }

    @Override
    public double calcRegularizationScore(boolean backpropParamsOnly){
        return 0.0;
    }

    private INDArray inputSubset(INDArray from, ArrayType arrayType, LayerWorkspaceMgr workspaceMgr){
        try(MemoryWorkspace ws = workspaceMgr.notifyScopeBorrowed(arrayType)){
            if(from.dataType() == dataType){
                return from.get(all(), all(), interval(cropping[0], from.size(2)-cropping[1])).dup(from.ordering());
            } else {
                return from.get(all(), all(), interval(cropping[0], from.size(2)-cropping[1])).castTo(dataType);
            }
        }
    }
}