Examples with GramianLayer com.simiacryptus.mindseye.layers.cudnn.GramianLayer used on opensource projects

Example 1 with GramianLayer

use of com.simiacryptus.mindseye.layers.cudnn.GramianLayer in project MindsEye by SimiaCryptus.

the class ArtistryUtil method gram.

/**
 * Gram pipeline network.
 *
 * @param network      the network
 * @param mean         the mean
 * @param pcaTransform the pca transform
 * @return the pipeline network
 */
@Nonnull
public static PipelineNetwork gram(final PipelineNetwork network, Tensor mean, Tensor pcaTransform) {
    int[] dimensions = pcaTransform.getDimensions();
    int inputBands = mean.getDimensions()[2];
    int pcaBands = dimensions[2];
    int outputBands = pcaBands / inputBands;
    int width = dimensions[0];
    int height = dimensions[1];
    network.wrap(new ImgBandBiasLayer(mean.scale(-1)));
    network.wrap(new ConvolutionLayer(width, height, inputBands, outputBands).set(pcaTransform));
    network.wrap(new GramianLayer());
    return network;
}

Example 2 with GramianLayer

use of com.simiacryptus.mindseye.layers.cudnn.GramianLayer in project MindsEye by SimiaCryptus.

the class StyleTransfer method getStyleComponents.

/**
 * Gets style components.
 *
 * @param node          the node
 * @param network       the network
 * @param styleParams   the style params
 * @param mean          the mean
 * @param covariance    the covariance
 * @param centeringMode the centering mode
 * @return the style components
 */
@Nonnull
public ArrayList<Tuple2<Double, DAGNode>> getStyleComponents(final DAGNode node, final PipelineNetwork network, final LayerStyleParams styleParams, final Tensor mean, final Tensor covariance, final CenteringMode centeringMode) {
    ArrayList<Tuple2<Double, DAGNode>> styleComponents = new ArrayList<>();
    if (null != styleParams && (styleParams.cov != 0 || styleParams.mean != 0)) {
        double meanRms = mean.rms();
        double meanScale = 0 == meanRms ? 1 : (1.0 / meanRms);
        InnerNode negTarget = network.wrap(new ValueLayer(mean.scale(-1)), new DAGNode[] {});
        InnerNode negAvg = network.wrap(new BandAvgReducerLayer().setAlpha(-1), node);
        if (styleParams.cov != 0) {
            DAGNode recentered;
            switch(centeringMode) {
                case Origin:
                    recentered = node;
                    break;
                case Dynamic:
                    recentered = network.wrap(new GateBiasLayer(), node, negAvg);
                    break;
                case Static:
                    recentered = network.wrap(new GateBiasLayer(), node, negTarget);
                    break;
                default:
                    throw new RuntimeException();
            }
            int[] covDim = covariance.getDimensions();
            assert 0 < covDim[2] : Arrays.toString(covDim);
            int inputBands = mean.getDimensions()[2];
            assert 0 < inputBands : Arrays.toString(mean.getDimensions());
            int outputBands = covDim[2] / inputBands;
            assert 0 < outputBands : Arrays.toString(covDim) + " / " + inputBands;
            double covRms = covariance.rms();
            double covScale = 0 == covRms ? 1 : (1.0 / covRms);
            styleComponents.add(new Tuple2<>(styleParams.cov, network.wrap(new MeanSqLossLayer().setAlpha(covScale), network.wrap(new ValueLayer(covariance), new DAGNode[] {}), network.wrap(ArtistryUtil.wrapTilesAvg(new GramianLayer()), recentered))));
        }
        if (styleParams.mean != 0) {
            styleComponents.add(new Tuple2<>(styleParams.mean, network.wrap(new MeanSqLossLayer().setAlpha(meanScale), negAvg, negTarget)));
        }
    }
    return styleComponents;
}