Examples with Tuple2 com.simiacryptus.util.lang.Tuple2 used on opensource projects

Example 1 with Tuple2

use of com.simiacryptus.util.lang.Tuple2 in project MindsEye by SimiaCryptus.

the class MaxPoolingLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    final Result in = inObj[0];
    in.getData().length();
    @Nonnull final int[] inputDims = in.getData().getDimensions();
    final List<Tuple2<Integer, int[]>> regions = MaxPoolingLayer.calcRegionsCache.apply(new MaxPoolingLayer.CalcRegionsParameter(inputDims, kernelDims));
    final Tensor[] outputA = IntStream.range(0, in.getData().length()).mapToObj(dataIndex -> {
        final int[] newDims = IntStream.range(0, inputDims.length).map(i -> {
            return (int) Math.ceil(inputDims[i] * 1.0 / kernelDims[i]);
        }).toArray();
        @Nonnull final Tensor output = new Tensor(newDims);
        return output;
    }).toArray(i -> new Tensor[i]);
    Arrays.stream(outputA).mapToInt(x -> x.length()).sum();
    @Nonnull final int[][] gradientMapA = new int[in.getData().length()][];
    IntStream.range(0, in.getData().length()).forEach(dataIndex -> {
        @Nullable final Tensor input = in.getData().get(dataIndex);
        final Tensor output = outputA[dataIndex];
        @Nonnull final IntToDoubleFunction keyExtractor = inputCoords -> input.get(inputCoords);
        @Nonnull final int[] gradientMap = new int[input.length()];
        regions.parallelStream().forEach(tuple -> {
            final Integer from = tuple.getFirst();
            final int[] toList = tuple.getSecond();
            int toMax = -1;
            double bestValue = Double.NEGATIVE_INFINITY;
            for (final int c : toList) {
                final double value = keyExtractor.applyAsDouble(c);
                if (-1 == toMax || bestValue < value) {
                    bestValue = value;
                    toMax = c;
                }
            }
            gradientMap[from] = toMax;
            output.set(from, input.get(toMax));
        });
        input.freeRef();
        gradientMapA[dataIndex] = gradientMap;
    });
    return new Result(TensorArray.wrap(outputA), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        if (in.isAlive()) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, in.getData().length()).parallel().mapToObj(dataIndex -> {
                @Nonnull final Tensor backSignal = new Tensor(inputDims);
                final int[] ints = gradientMapA[dataIndex];
                @Nullable final Tensor datum = data.get(dataIndex);
                for (int i = 0; i < datum.length(); i++) {
                    backSignal.add(ints[i], datum.get(i));
                }
                datum.freeRef();
                return backSignal;
            }).toArray(i -> new Tensor[i]));
            in.accumulate(buffer, tensorArray);
        }
    }) {

        @Override
        protected void _free() {
            Arrays.stream(inObj).forEach(nnResult -> nnResult.freeRef());
        }

        @Override
        public boolean isAlive() {
            return in.isAlive();
        }
    };
}

Example 2 with Tuple2

use of com.simiacryptus.util.lang.Tuple2 in project MindsEye by SimiaCryptus.

the class DeepDream method getContentComponents.

/**
 * Gets content components.
 *
 * @param setup   the setup
 * @param nodeMap the node map
 * @return the content components
 */
@Nonnull
public ArrayList<Tuple2<Double, DAGNode>> getContentComponents(NeuralSetup<T> setup, final Map<T, DAGNode> nodeMap) {
    ArrayList<Tuple2<Double, DAGNode>> contentComponents = new ArrayList<>();
    for (final T layerType : getLayerTypes()) {
        final DAGNode node = nodeMap.get(layerType);
        if (setup.style.coefficients.containsKey(layerType)) {
            final double coeff_content = setup.style.coefficients.get(layerType).rms;
            DAGNetwork network = node.getNetwork();
            contentComponents.add(new Tuple2<>(coeff_content, network.wrap(new MeanSqLossLayer(), node, network.wrap(new ValueLayer(setup.contentTarget.content.get(layerType))))));
            final double coeff_gain = setup.style.coefficients.get(layerType).gain;
            contentComponents.add(new Tuple2<>(-coeff_gain, network.wrap(new AvgReducerLayer(), network.wrap(new SquareActivationLayer(), node))));
        }
    }
    return contentComponents;
}

Example 3 with Tuple2

use of com.simiacryptus.util.lang.Tuple2 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;
}

Example 4 with Tuple2

use of com.simiacryptus.util.lang.Tuple2 in project MindsEye by SimiaCryptus.

the class PerformanceTester method testPerformance.

/**
 * Test learning performance double statistics.
 *
 * @param component      the component
 * @param inputPrototype the input prototype
 * @return the double statistics
 */
@Nonnull
protected Tuple2<Double, Double> testPerformance(@Nonnull final Layer component, final Tensor... inputPrototype) {
    final Tensor[][] data = IntStream.range(0, batches).mapToObj(x -> x).flatMap(x -> Stream.<Tensor[]>of(inputPrototype)).toArray(i -> new Tensor[i][]);
    @Nonnull TimedResult<Result> timedEval = TimedResult.time(() -> {
        Result[] input = ConstantResult.batchResultArray(data);
        @Nullable Result result;
        try {
            result = component.eval(input);
        } finally {
            for (@Nonnull Result nnResult : input) {
                nnResult.freeRef();
                nnResult.getData().freeRef();
            }
        }
        return result;
    });
    final Result result = timedEval.result;
    @Nonnull final DeltaSet<Layer> buffer = new DeltaSet<Layer>();
    try {
        long timedBackprop = TimedResult.time(() -> {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(result.getData().stream().map(x -> {
                return x.mapAndFree(v -> 1.0);
            }).toArray(i -> new Tensor[i]));
            result.accumulate(buffer, tensorArray);
            assert tensorArray.currentRefCount() == 0;
            return buffer;
        }).timeNanos;
        return new Tuple2<>(timedEval.timeNanos / 1e9, timedBackprop / 1e9);
    } finally {
        buffer.freeRef();
        result.freeRef();
        result.getData().freeRef();
    }
}

Example 5 with Tuple2

use of com.simiacryptus.util.lang.Tuple2 in project MindsEye by SimiaCryptus.

the class StyleTransfer method getContentComponents.

/**
 * Gets content components.
 *
 * @param setup   the setup
 * @param nodeMap the node map
 * @return the content components
 */
@Nonnull
public ArrayList<Tuple2<Double, DAGNode>> getContentComponents(NeuralSetup<T> setup, final Map<T, DAGNode> nodeMap) {
    ArrayList<Tuple2<Double, DAGNode>> contentComponents = new ArrayList<>();
    for (final T layerType : getLayerTypes()) {
        final DAGNode node = nodeMap.get(layerType);
        final double coeff_content = !setup.style.content.params.containsKey(layerType) ? 0 : setup.style.content.params.get(layerType);
        final PipelineNetwork network1 = (PipelineNetwork) node.getNetwork();
        if (coeff_content != 0) {
            Tensor content = setup.contentTarget.content.get(layerType);
            contentComponents.add(new Tuple2<>(coeff_content, network1.wrap(new MeanSqLossLayer().setAlpha(1.0 / content.rms()), node, network1.wrap(new ValueLayer(content), new DAGNode[] {}))));
        }
    }
    return contentComponents;
}