Examples with TensorList com.simiacryptus.mindseye.lang.TensorList used on opensource projects

Example 41 with TensorList

use of com.simiacryptus.mindseye.lang.TensorList in project MindsEye by SimiaCryptus.

the class BandReducerLayer method evalAndFree.

@Nullable
@Override
public Result evalAndFree(final Result... inObj) {
    if (!CudaSystem.isEnabled())
        return getCompatibilityLayer().evalAndFree(inObj);
    final Result input = inObj[0];
    final TensorList batch = input.getData();
    @Nonnull final int[] inputSize = batch.getDimensions();
    @Nonnull PoolingLayer impl = new PoolingLayer().setMode(mode).setPrecision(precision).setWindowX(inputSize[1]).setWindowY(inputSize[0]).setAlpha(alpha);
    @Nullable Result result = impl.evalAndFree(inObj);
    impl.freeRef();
    return result;
}

Example 42 with TensorList

use of com.simiacryptus.mindseye.lang.TensorList in project MindsEye by SimiaCryptus.

the class ConvolutionLayer method evalAndFree.

@Nullable
@Override
public Result evalAndFree(@Nonnull final Result... inObj) {
    final Tensor kernel = getKernel();
    kernel.addRef();
    assert kernel.isValid();
    assert 1 == inObj.length;
    assert 3 == inObj[0].getData().getDimensions().length;
    assert inputBands == inObj[0].getData().getDimensions()[2] : Arrays.toString(inObj[0].getData().getDimensions()) + "[2] != " + inputBands;
    if (!CudaSystem.isEnabled())
        return getCompatibilityLayer().evalAndFree(inObj);
    @Nonnull ExplodedConvolutionGrid grid = getExplodedNetwork();
    @Nonnull PipelineNetwork network = grid.getNetwork();
    if (isFrozen()) {
        network.freeze();
    }
    final Result result = network.evalAndFree(inObj);
    network.freeRef();
    final TensorList resultData = result.getData();
    assert inObj[0].getData().length() == resultData.length();
    assert 3 == resultData.getDimensions().length;
    assert outputBands == resultData.getDimensions()[2];
    ConvolutionLayer.this.addRef();
    return new Result(resultData, (@Nonnull final DeltaSet<Layer> deltaSet, @Nonnull final TensorList delta) -> {
        result.accumulate(deltaSet, delta);
        if (!isFrozen()) {
            Tensor read = grid.read(deltaSet, true);
            deltaSet.get(ConvolutionLayer.this, kernel.getData()).addInPlace(read.getData()).freeRef();
            read.freeRef();
        }
    }) {

        @Override
        public void accumulate(final DeltaSet<Layer> buffer, final TensorList delta) {
            getAccumulator().accept(buffer, delta);
        }

        @Override
        protected void _free() {
            grid.freeRef();
            result.freeRef();
            kernel.freeRef();
            ConvolutionLayer.this.freeRef();
        }

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

Example 43 with TensorList

use of com.simiacryptus.mindseye.lang.TensorList in project MindsEye by SimiaCryptus.

the class BasicTrainable method eval.

/**
 * Eval point sample.
 *
 * @param list    the list
 * @param monitor the monitor
 * @return the point sample
 */
@Nonnull
protected PointSample eval(@Nonnull final List<Tensor[]> list, @Nullable final TrainingMonitor monitor) {
    @Nonnull final TimedResult<PointSample> timedResult = TimedResult.time(() -> {
        final Result[] nnContext = BasicTrainable.getNNContext(list, mask);
        final Result result = network.eval(nnContext);
        for (@Nonnull Result nnResult : nnContext) {
            nnResult.getData().freeRef();
            nnResult.freeRef();
        }
        final TensorList resultData = result.getData();
        @Nonnull final DeltaSet<Layer> deltaSet = new DeltaSet<Layer>();
        @Nonnull StateSet<Layer> stateSet = null;
        try {
            final DoubleSummaryStatistics statistics = resultData.stream().flatMapToDouble(x -> {
                double[] array = Arrays.stream(x.getData()).toArray();
                x.freeRef();
                return Arrays.stream(array);
            }).summaryStatistics();
            final double sum = statistics.getSum();
            result.accumulate(deltaSet, 1.0);
            stateSet = new StateSet<>(deltaSet);
            // log.info(String.format("Evaluated to %s delta buffers, %s mag", DeltaSet<LayerBase>.getMap().size(), DeltaSet<LayerBase>.getMagnitude()));
            return new PointSample(deltaSet, stateSet, sum, 0.0, list.size());
        } finally {
            if (null != stateSet)
                stateSet.freeRef();
            resultData.freeRefAsync();
            result.freeRefAsync();
            deltaSet.freeRefAsync();
        }
    });
    if (null != monitor && verbosity() > 0) {
        monitor.log(String.format("Device completed %s items in %.3f sec", list.size(), timedResult.timeNanos / 1e9));
    }
    @Nonnull PointSample normalize = timedResult.result.normalize();
    timedResult.result.freeRef();
    return normalize;
}

Example 44 with TensorList

use of com.simiacryptus.mindseye.lang.TensorList in project MindsEye by SimiaCryptus.

the class TensorListTrainable method eval.

/**
 * Eval point sample.
 *
 * @param list    the list
 * @param monitor the monitor
 * @return the point sample
 */
@Nonnull
protected PointSample eval(@Nonnull final TensorList[] list, @Nullable final TrainingMonitor monitor) {
    int inputs = data.length;
    assert 0 < inputs;
    int items = data[0].length();
    assert 0 < items;
    @Nonnull final TimedResult<PointSample> timedResult = TimedResult.time(() -> {
        final Result[] nnContext = TensorListTrainable.getNNContext(list, mask);
        final Result result = network.eval(nnContext);
        for (@Nonnull Result nnResult : nnContext) {
            nnResult.getData().freeRef();
            nnResult.freeRef();
        }
        final TensorList resultData = result.getData();
        final DoubleSummaryStatistics statistics = resultData.stream().flatMapToDouble(x -> {
            double[] array = Arrays.stream(x.getData()).toArray();
            x.freeRef();
            return Arrays.stream(array);
        }).summaryStatistics();
        final double sum = statistics.getSum();
        @Nonnull final DeltaSet<Layer> deltaSet = new DeltaSet<Layer>();
        @Nonnull PointSample pointSample;
        try {
            result.accumulate(deltaSet, 1.0);
            // log.info(String.format("Evaluated to %s delta buffers, %s mag", DeltaSet<LayerBase>.getMap().size(), DeltaSet<LayerBase>.getMagnitude()));
            @Nonnull StateSet<Layer> stateSet = new StateSet<>(deltaSet);
            pointSample = new PointSample(deltaSet, stateSet, sum, 0.0, items);
            stateSet.freeRef();
        } finally {
            resultData.freeRef();
            result.freeRef();
            deltaSet.freeRef();
        }
        return pointSample;
    });
    if (null != monitor && verbosity() > 0) {
        monitor.log(String.format("Device completed %s items in %.3f sec", items, timedResult.timeNanos / 1e9));
    }
    @Nonnull PointSample normalize = timedResult.result.normalize();
    timedResult.result.freeRef();
    return normalize;
}

Example 45 with TensorList

use of com.simiacryptus.mindseye.lang.TensorList in project MindsEye by SimiaCryptus.

the class CudaLayerTester method testNonstandardBoundsBackprop.

/**
 * Test nonstandard bounds backprop tolerance statistics.
 *
 * @param log            the log
 * @param layer          the layer
 * @param inputPrototype the input prototype
 * @return the tolerance statistics
 */
@Nonnull
public ToleranceStatistics testNonstandardBoundsBackprop(final NotebookOutput log, @Nullable final Layer layer, @Nonnull final Tensor[] inputPrototype) {
    log.h2("Irregular Backprop");
    log.p("This layer should accept non-dense tensors as delta input.");
    return log.code(() -> {
        Tensor[] randomized = Arrays.stream(inputPrototype).map(x -> x.map(v -> getRandom())).toArray(i -> new Tensor[i]);
        logger.info("Input: " + Arrays.stream(randomized).map(Tensor::prettyPrint).collect(Collectors.toList()));
        Precision precision = Precision.Double;
        TensorList[] controlInput = Arrays.stream(randomized).map(original -> {
            return TensorArray.wrap(original);
        }).toArray(i -> new TensorList[i]);
        @Nonnull final SimpleResult testResult = CudaSystem.run(gpu -> {
            TensorList[] copy = copy(controlInput);
            SimpleResult result = new SimpleGpuEval(layer, gpu, copy) {

                @Nonnull
                @Override
                public TensorList getFeedback(@Nonnull final TensorList original) {
                    Tensor originalTensor = original.get(0).mapAndFree(x -> 1);
                    CudaTensorList cudaTensorList = buildIrregularCudaTensor(gpu, precision, originalTensor);
                    originalTensor.freeRef();
                    return cudaTensorList;
                }
            }.call();
            Arrays.stream(copy).forEach(ReferenceCounting::freeRef);
            return result;
        });
        @Nonnull final SimpleResult controlResult = CudaSystem.run(gpu -> {
            TensorList[] copy = copy(controlInput);
            SimpleResult result = SimpleGpuEval.run(layer, gpu, copy);
            Arrays.stream(copy).forEach(ReferenceCounting::freeRef);
            return result;
        }, 1);
        try {
            ToleranceStatistics compareOutput = compareOutput(controlResult, testResult);
            ToleranceStatistics compareDerivatives = compareDerivatives(controlResult, testResult);
            return compareDerivatives.combine(compareOutput);
        } finally {
            Arrays.stream(controlInput).forEach(ReferenceCounting::freeRef);
            controlResult.freeRef();
            testResult.freeRef();
        }
    });
}