org.deeplearning4j.nn.params.Deconvolution3DParamInitializer Maven / Gradle / Ivy

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package org.deeplearning4j.nn.params;


import lombok.val;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.Deconvolution3D;
import org.deeplearning4j.nn.conf.layers.Layer;
import org.deeplearning4j.nn.weights.WeightInitUtil;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.indexing.NDArrayIndex;

import java.util.Collections;
import java.util.LinkedHashMap;
import java.util.Map;

public class Deconvolution3DParamInitializer extends ConvolutionParamInitializer {

    private static final Deconvolution3DParamInitializer INSTANCE = new Deconvolution3DParamInitializer();

    public static Deconvolution3DParamInitializer getInstance() {
        return INSTANCE;
    }

    public final static String WEIGHT_KEY = DefaultParamInitializer.WEIGHT_KEY;
    public final static String BIAS_KEY = DefaultParamInitializer.BIAS_KEY;

    @Override
    public long numParams(NeuralNetConfiguration conf) {
        return numParams(conf.getLayer());
    }

    @Override
    public long numParams(Layer l) {
        Deconvolution3D layerConf = (Deconvolution3D) l;

        int[] kernel = layerConf.getKernelSize();
        val nIn = layerConf.getNIn();
        val nOut = layerConf.getNOut();
        return nIn * nOut * kernel[0] * kernel[1] * kernel[2] + (layerConf.hasBias() ? nOut : 0);
    }


    @Override
    public Map init(NeuralNetConfiguration conf, INDArray paramsView, boolean initializeParams) {
        Deconvolution3D layer = (Deconvolution3D) conf.getLayer();
        if (layer.getKernelSize().length != 3) throw new IllegalArgumentException("Filter size must be == 3");

        Map params = Collections.synchronizedMap(new LinkedHashMap());

        Deconvolution3D layerConf = (Deconvolution3D) conf.getLayer();
        val nOut = layerConf.getNOut();
        INDArray paramsViewReshape = paramsView.reshape(paramsView.length());
        if (layer.hasBias()) {
            INDArray biasView = paramsViewReshape.get(NDArrayIndex.interval(0, nOut));
            INDArray weightView = paramsViewReshape.get( NDArrayIndex.interval(nOut, numParams(conf)));
            params.put(BIAS_KEY, createBias(conf, biasView, initializeParams));
            params.put(WEIGHT_KEY, createWeightMatrix(conf, weightView, initializeParams));
            conf.addVariable(WEIGHT_KEY);
            conf.addVariable(BIAS_KEY);
        } else {
            INDArray weightView = paramsView;
            params.put(WEIGHT_KEY, createWeightMatrix(conf, weightView, initializeParams));
            conf.addVariable(WEIGHT_KEY);
        }

        return params;
    }

    @Override
    public Map getGradientsFromFlattened(NeuralNetConfiguration conf, INDArray gradientView) {

        Deconvolution3D layerConf = (Deconvolution3D) conf.getLayer();

        int[] kernel = layerConf.getKernelSize();
        val nIn = layerConf.getNIn();
        val nOut = layerConf.getNOut();

        Map out = new LinkedHashMap<>();
        INDArray gradientViewReshape = gradientView.reshape(gradientView.length());
        if (layerConf.hasBias()) {
            INDArray biasGradientView = gradientViewReshape.get(NDArrayIndex.interval(0, nOut));
            INDArray weightGradientView =
                    gradientViewReshape.get(NDArrayIndex.interval(nOut, numParams(conf)))
                            .reshape('c', kernel[0], kernel[1], kernel[2], nOut, nIn);
            out.put(BIAS_KEY, biasGradientView);
            out.put(WEIGHT_KEY, weightGradientView);
        } else {
            INDArray weightGradientView = gradientView.reshape('c', kernel[0], kernel[1], kernel[2], nOut, nIn);
            out.put(WEIGHT_KEY, weightGradientView);
        }
        return out;
    }


    protected INDArray createWeightMatrix(NeuralNetConfiguration conf, INDArray weightView, boolean initializeParams) {
        /*
         Create a 5d weight matrix of:
           (number of kernels, num input channels, kernel depth, kernel height, kernel width)
         Note c order is used specifically for the CNN weights, as opposed to f order elsewhere
         Inputs to the convolution layer are:
         (batch size, num input feature maps, image depth, image height, image width)
         */
        Deconvolution3D layerConf = (Deconvolution3D) conf.getLayer();

        if (initializeParams) {
            int[] kernel = layerConf.getKernelSize();
            int[] stride = layerConf.getStride();

            val inputDepth = layerConf.getNIn();
            val outputDepth = layerConf.getNOut();

            double fanIn = inputDepth * kernel[0] * kernel[1] * kernel[2];
            double fanOut = outputDepth * kernel[0] * kernel[1] * kernel[2] /
                    ((double) stride[0] * stride[1] * stride[2]);

            //libnd4j: [kD, kH, kW, oC, iC]
            val weightsShape = new long[]{kernel[0], kernel[1], kernel[2], outputDepth, inputDepth};

            return layerConf.getWeightInitFn().init(fanIn, fanOut, weightsShape, 'c', weightView);
        } else {
            int[] kernel = layerConf.getKernelSize();
            return WeightInitUtil.reshapeWeights(
                    new long[]{kernel[0], kernel[1], kernel[2], layerConf.getNOut(), layerConf.getNIn()}, weightView, 'c');
        }
    }
}