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

Example 26 with TensorArray

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

the class SumInputsLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    Arrays.stream(inObj).forEach(x -> x.getData().addRef());
    return new Result(Arrays.stream(inObj).parallel().map(x -> {
        TensorList data = x.getData();
        data.addRef();
        return data;
    }).reduce((l, r) -> {
        assert l.length() == r.length() || 1 == l.length() || 1 == r.length();
        @Nonnull TensorArray sum = TensorArray.wrap(IntStream.range(0, l.length()).parallel().mapToObj(i -> {
            @Nullable final Tensor left = l.get(1 == l.length() ? 0 : i);
            @Nullable final Tensor right = r.get(1 == r.length() ? 0 : i);
            @Nullable Tensor tensor;
            if (right.length() == 1) {
                tensor = left.mapParallel(v -> v + right.get(0));
            } else {
                tensor = left.reduceParallel(right, (v1, v2) -> v1 + v2);
            }
            left.freeRef();
            right.freeRef();
            return tensor;
        }).toArray(i -> new Tensor[i]));
        l.freeRef();
        r.freeRef();
        return sum;
    }).get(), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        for (@Nonnull final Result input : inObj) {
            if (input.isAlive()) {
                @Nonnull TensorList projectedDelta = delta;
                if (1 < projectedDelta.length() && input.getData().length() == 1) {
                    projectedDelta = TensorArray.wrap(projectedDelta.stream().parallel().reduce((a, b) -> {
                        @Nullable Tensor c = a.addAndFree(b);
                        b.freeRef();
                        return c;
                    }).get());
                } else {
                    projectedDelta.addRef();
                }
                if (1 < Tensor.length(projectedDelta.getDimensions()) && Tensor.length(input.getData().getDimensions()) == 1) {
                    Tensor[] data = projectedDelta.stream().map(t -> new Tensor(new double[] { t.sum() })).toArray(i -> new Tensor[i]);
                    @Nonnull TensorArray data2 = TensorArray.wrap(data);
                    projectedDelta.freeRef();
                    projectedDelta = data2;
                }
                input.accumulate(buffer, projectedDelta);
            }
        }
    }) {

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

        @Override
        public boolean isAlive() {
            for (@Nonnull final Result element : inObj) if (element.isAlive()) {
                return true;
            }
            return false;
        }
    };
}

Example 27 with TensorArray

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

the class AvgPoolingLayer method eval.

@Nonnull
@SuppressWarnings("unchecked")
@Override
public Result eval(@Nonnull final Result... inObj) {
    final int kernelSize = Tensor.length(kernelDims);
    final TensorList data = inObj[0].getData();
    @Nonnull final int[] inputDims = data.getDimensions();
    final int[] newDims = IntStream.range(0, inputDims.length).map(i -> {
        assert 0 == inputDims[i] % kernelDims[i] : inputDims[i] + ":" + kernelDims[i];
        return inputDims[i] / kernelDims[i];
    }).toArray();
    final Map<Coordinate, List<int[]>> coordMap = AvgPoolingLayer.getCoordMap(kernelDims, newDims);
    final Tensor[] outputValues = IntStream.range(0, data.length()).mapToObj(dataIndex -> {
        @Nullable final Tensor input = data.get(dataIndex);
        @Nonnull final Tensor output = new Tensor(newDims);
        for (@Nonnull final Entry<Coordinate, List<int[]>> entry : coordMap.entrySet()) {
            double sum = entry.getValue().stream().mapToDouble(inputCoord -> input.get(inputCoord)).sum();
            if (Double.isFinite(sum)) {
                output.add(entry.getKey(), sum / kernelSize);
            }
        }
        input.freeRef();
        return output;
    }).toArray(i -> new Tensor[i]);
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    return new Result(TensorArray.wrap(outputValues), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        if (inObj[0].isAlive()) {
            final Tensor[] passback = IntStream.range(0, delta.length()).mapToObj(dataIndex -> {
                @Nullable Tensor tensor = delta.get(dataIndex);
                @Nonnull final Tensor backSignal = new Tensor(inputDims);
                for (@Nonnull final Entry<Coordinate, List<int[]>> outputMapping : coordMap.entrySet()) {
                    final double outputValue = tensor.get(outputMapping.getKey());
                    for (@Nonnull final int[] inputCoord : outputMapping.getValue()) {
                        backSignal.add(inputCoord, outputValue / kernelSize);
                    }
                }
                tensor.freeRef();
                return backSignal;
            }).toArray(i -> new Tensor[i]);
            @Nonnull TensorArray tensorArray = TensorArray.wrap(passback);
            inObj[0].accumulate(buffer, tensorArray);
        }
    }) {

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

        @Override
        public boolean isAlive() {
            return inObj[0].isAlive();
        }
    };
}

Example 28 with TensorArray

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

the class CudaTensorList method toHeap.

private TensorArray toHeap(final boolean avoidAllocations) {
    CudaTensor gpuCopy = this.gpuCopy;
    TimedResult<TensorArray> timedResult = TimedResult.time(() -> CudaDevice.run(gpu -> {
        assert CudaDevice.isThreadDeviceId(gpu.getDeviceId());
        if (null == gpuCopy) {
            if (null == heapCopy) {
                throw new IllegalStateException("No data");
            } else if (heapCopy.isFinalized()) {
                throw new IllegalStateException("Local data has been freed");
            }
        }
        gpuCopy.addRef();
        assert CudaDevice.isThreadDeviceId(gpu.getDeviceId());
        try {
            assert getPrecision() == gpuCopy.getPrecision();
            assert getPrecision() == gpuCopy.descriptor.dataType;
            assert CudaDevice.isThreadDeviceId(gpu.getDeviceId());
            final Tensor[] output = IntStream.range(0, getLength()).mapToObj(dataIndex -> new Tensor(getDimensions())).toArray(i -> new Tensor[i]);
            assert CudaDevice.isThreadDeviceId(gpu.getDeviceId());
            for (int i = 0; i < getLength(); i++) {
                assert CudaDevice.isThreadDeviceId(gpu.getDeviceId());
                gpuCopy.read(gpu, i, output[i], avoidAllocations);
                assert CudaDevice.isThreadDeviceId(gpu.getDeviceId());
            }
            return TensorArray.wrap(output);
        } finally {
            gpuCopy.freeRef();
        }
    }, this));
    CudaTensorList.logger.debug(String.format("Read %s bytes in %.4f from Tensor %s on GPU at %s, created by %s", gpuCopy.size(), timedResult.seconds(), Integer.toHexString(System.identityHashCode(timedResult.result)), TestUtil.toString(TestUtil.getStackTrace()).replaceAll("\n", "\n\t"), TestUtil.toString(createdBy).replaceAll("\n", "\n\t")));
    return timedResult.result;
}

Example 29 with TensorArray

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

the class CudaTensorList method heapCopy.

/**
 * Inner tensor list.
 *
 * @param avoidAllocations
 * @return the tensor list
 */
@Nullable
private TensorArray heapCopy(final boolean avoidAllocations) {
    TensorArray heapCopy;
    heapCopy = this.heapCopy;
    if (null == heapCopy || heapCopy.isFinalized()) {
        TensorArray copy = toHeap(avoidAllocations);
        final TensorArray prev;
        synchronized (this) {
            heapCopy = this.heapCopy;
            if (null == heapCopy || heapCopy.isFinalized()) {
                prev = this.heapCopy;
                this.heapCopy = copy;
                heapCopy = copy;
            } else {
                prev = null;
            }
        }
        if (null != prev)
            prev.freeRef();
    }
    return heapCopy;
}

Example 30 with TensorArray

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

the class GaussianNoiseLayer method eval.

@Nonnull
@Override
public Result eval(final Result... inObj) {
    final Result in0 = inObj[0];
    final TensorList inputData = in0.getData();
    final int itemCnt = inputData.length();
    in0.addRef();
    inputData.addRef();
    final Tensor[] outputA = IntStream.range(0, itemCnt).mapToObj(dataIndex -> {
        @Nonnull final Random random = new Random(seed);
        @Nullable final Tensor input = inputData.get(dataIndex);
        @Nullable final Tensor output = input.map(x -> {
            return x + random.nextGaussian() * getValue();
        });
        input.freeRef();
        return output;
    }).toArray(i -> new Tensor[i]);
    return new Result(TensorArray.wrap(outputA), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        if (in0.isAlive()) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, delta.length()).mapToObj(dataIndex -> {
                Tensor tensor = delta.get(dataIndex);
                @Nullable final double[] deltaData = tensor.getData();
                @Nonnull final Tensor passback = new Tensor(inputData.getDimensions());
                for (int i = 0; i < passback.length(); i++) {
                    passback.set(i, deltaData[i]);
                }
                tensor.freeRef();
                return passback;
            }).toArray(i -> new Tensor[i]));
            in0.accumulate(buffer, tensorArray);
        }
    }) {

        @Override
        protected void _free() {
            inputData.freeRef();
            in0.freeRef();
        }

        @Override
        public boolean isAlive() {
            return in0.isAlive() || !isFrozen();
        }
    };
}