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

Example 1 with TensorArray

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

the class ConvolutionLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    final Result input = inObj[0];
    final TensorList batch = input.getData();
    batch.addRef();
    @Nonnull final int[] inputDims = batch.get(0).getDimensions();
    @Nonnull final int[] kernelDims = kernel.getDimensions();
    @Nullable final double[] kernelData = ConvolutionLayer.this.kernel.getData();
    @Nonnull final ConvolutionController convolutionController = new ConvolutionController(inputDims, kernelDims, paddingX, paddingY);
    final Tensor[] output = IntStream.range(0, batch.length()).mapToObj(dataIndex -> new Tensor(convolutionController.getOutputDims())).toArray(i -> new Tensor[i]);
    try {
        final double[][] inputBuffers = batch.stream().map(x -> {
            @Nullable double[] data = x.getData();
            x.detach();
            return data;
        }).toArray(i -> new double[i][]);
        final double[][] outputBuffers = Arrays.stream(output).map(x -> x.getData()).toArray(i -> new double[i][]);
        convolutionController.convolve(inputBuffers, kernelData, outputBuffers);
    } catch (@Nonnull final Throwable e) {
        throw new RuntimeException("Error mapCoords image res " + Arrays.toString(inputDims), e);
    }
    int outputLength = output.length;
    return new Result(TensorArray.wrap(output), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList error) -> {
        if (!isFrozen()) {
            final double[][] inputBuffers = batch.stream().map(x -> {
                @Nullable double[] data = x.getData();
                x.freeRef();
                return data;
            }).toArray(i -> new double[i][]);
            final double[][] outputBuffers = error.stream().map(x -> {
                @Nullable double[] data = x.getData();
                x.freeRef();
                return data;
            }).toArray(i -> new double[i][]);
            @Nonnull final Tensor weightGradient = new Tensor(kernelDims);
            convolutionController.gradient(inputBuffers, weightGradient.getData(), outputBuffers);
            buffer.get(ConvolutionLayer.this, kernelData).addInPlace(weightGradient.getData()).freeRef();
            weightGradient.freeRef();
        }
        if (input.isAlive()) {
            final Tensor[] inputBufferTensors = IntStream.range(0, outputLength).mapToObj(dataIndex -> new Tensor(inputDims)).toArray(i -> new Tensor[i]);
            final double[][] inputBuffers = Arrays.stream(inputBufferTensors).map(x -> {
                @Nullable double[] data = x.getData();
                return data;
            }).toArray(i -> new double[i][]);
            final double[][] outputBuffers = error.stream().map(x -> {
                @Nullable double[] data = x.getData();
                x.freeRef();
                return data;
            }).toArray(i -> new double[i][]);
            convolutionController.backprop(inputBuffers, kernelData, outputBuffers);
            @Nonnull TensorArray tensorArray = TensorArray.wrap(inputBufferTensors);
            input.accumulate(buffer, tensorArray);
        }
    }) {

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

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

Example 2 with TensorArray

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

the class MaxImageBandLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
    assert 1 == inObj.length;
    final TensorList inputData = inObj[0].getData();
    inputData.addRef();
    inputData.length();
    @Nonnull final int[] inputDims = inputData.getDimensions();
    assert 3 == inputDims.length;
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    final Coordinate[][] maxCoords = inputData.stream().map(data -> {
        Coordinate[] coordinates = IntStream.range(0, inputDims[2]).mapToObj(band -> {
            return data.coordStream(true).filter(e -> e.getCoords()[2] == band).max(Comparator.comparing(c -> data.get(c))).get();
        }).toArray(i -> new Coordinate[i]);
        data.freeRef();
        return coordinates;
    }).toArray(i -> new Coordinate[i][]);
    return new Result(TensorArray.wrap(IntStream.range(0, inputData.length()).mapToObj(dataIndex -> {
        Tensor tensor = inputData.get(dataIndex);
        final DoubleStream doubleStream = IntStream.range(0, inputDims[2]).mapToDouble(band -> {
            final int[] maxCoord = maxCoords[dataIndex][band].getCoords();
            double v = tensor.get(maxCoord[0], maxCoord[1], band);
            return v;
        });
        Tensor tensor1 = new Tensor(1, 1, inputDims[2]).set(Tensor.getDoubles(doubleStream, inputDims[2]));
        tensor.freeRef();
        return tensor1;
    }).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        if (inObj[0].isAlive()) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, delta.length()).parallel().mapToObj(dataIndex -> {
                Tensor deltaTensor = delta.get(dataIndex);
                @Nonnull final Tensor passback = new Tensor(inputData.getDimensions());
                IntStream.range(0, inputDims[2]).forEach(b -> {
                    final int[] maxCoord = maxCoords[dataIndex][b].getCoords();
                    passback.set(new int[] { maxCoord[0], maxCoord[1], b }, deltaTensor.get(0, 0, b));
                });
                deltaTensor.freeRef();
                return passback;
            }).toArray(i -> new Tensor[i]));
            inObj[0].accumulate(buffer, tensorArray);
        }
    }) {

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

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

Example 3 with TensorArray

use of com.simiacryptus.mindseye.lang.TensorArray 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 4 with TensorArray

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

the class MaxMetaLayer method eval.

@Nonnull
@Override
public Result eval(final Result... inObj) {
    final Result input = inObj[0];
    input.addRef();
    final int itemCnt = input.getData().length();
    final Tensor input0Tensor = input.getData().get(0);
    final int vectorSize = input0Tensor.length();
    @Nonnull final int[] indicies = new int[vectorSize];
    for (int i = 0; i < vectorSize; i++) {
        final int itemNumber = i;
        indicies[i] = IntStream.range(0, itemCnt).mapToObj(x -> x).max(Comparator.comparing(dataIndex -> {
            Tensor tensor = input.getData().get(dataIndex);
            double v = tensor.getData()[itemNumber];
            tensor.freeRef();
            return v;
        })).get();
    }
    return new Result(TensorArray.wrap(input0Tensor.mapIndex((v, c) -> {
        Tensor tensor = input.getData().get(indicies[c]);
        double v1 = tensor.getData()[c];
        tensor.freeRef();
        return v1;
    })), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        if (input.isAlive()) {
            @Nullable final Tensor delta = data.get(0);
            @Nonnull final Tensor[] feedback = new Tensor[itemCnt];
            Arrays.parallelSetAll(feedback, i -> new Tensor(delta.getDimensions()));
            input0Tensor.coordStream(true).forEach((inputCoord) -> {
                feedback[indicies[inputCoord.getIndex()]].add(inputCoord, delta.get(inputCoord));
            });
            @Nonnull TensorArray tensorArray = TensorArray.wrap(feedback);
            input.accumulate(buffer, tensorArray);
            delta.freeRef();
        }
    }) {

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

        @Override
        protected void _free() {
            input.freeRef();
            input0Tensor.freeRef();
        }
    };
}

Example 5 with TensorArray

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

the class FullyConnectedLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
    final TensorList indata = inObj[0].getData();
    indata.addRef();
    for (@Nonnull Result result : inObj) {
        result.addRef();
    }
    FullyConnectedLayer.this.addRef();
    assert Tensor.length(indata.getDimensions()) == Tensor.length(this.inputDims) : Arrays.toString(indata.getDimensions()) + " == " + Arrays.toString(this.inputDims);
    @Nonnull DoubleMatrix doubleMatrix = new DoubleMatrix(Tensor.length(indata.getDimensions()), Tensor.length(outputDims), this.weights.getData());
    @Nonnull final DoubleMatrix matrixObj = FullyConnectedLayer.transpose(doubleMatrix);
    @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, indata.length()).parallel().mapToObj(dataIndex -> {
        @Nullable final Tensor input = indata.get(dataIndex);
        @Nullable final Tensor output = new Tensor(outputDims);
        matrixObj.mmuli(new DoubleMatrix(input.length(), 1, input.getData()), new DoubleMatrix(output.length(), 1, output.getData()));
        input.freeRef();
        return output;
    }).toArray(i -> new Tensor[i]));
    RecycleBin.DOUBLES.recycle(matrixObj.data, matrixObj.data.length);
    this.weights.addRef();
    return new Result(tensorArray, (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        if (!isFrozen()) {
            final Delta<Layer> deltaBuffer = buffer.get(FullyConnectedLayer.this, this.weights.getData());
            final int threads = 4;
            IntStream.range(0, threads).parallel().mapToObj(x -> x).flatMap(thread -> {
                @Nullable Stream<Tensor> stream = IntStream.range(0, indata.length()).filter(i -> thread == i % threads).mapToObj(dataIndex -> {
                    @Nonnull final Tensor weightDelta = new Tensor(Tensor.length(inputDims), Tensor.length(outputDims));
                    Tensor deltaTensor = delta.get(dataIndex);
                    Tensor inputTensor = indata.get(dataIndex);
                    FullyConnectedLayer.crossMultiplyT(deltaTensor.getData(), inputTensor.getData(), weightDelta.getData());
                    inputTensor.freeRef();
                    deltaTensor.freeRef();
                    return weightDelta;
                });
                return stream;
            }).reduce((a, b) -> {
                @Nullable Tensor c = a.addAndFree(b);
                b.freeRef();
                return c;
            }).map(data -> {
                @Nonnull Delta<Layer> layerDelta = deltaBuffer.addInPlace(data.getData());
                data.freeRef();
                return layerDelta;
            });
            deltaBuffer.freeRef();
        }
        if (inObj[0].isAlive()) {
            @Nonnull final TensorList tensorList = TensorArray.wrap(IntStream.range(0, indata.length()).parallel().mapToObj(dataIndex -> {
                Tensor deltaTensor = delta.get(dataIndex);
                @Nonnull final Tensor passback = new Tensor(indata.getDimensions());
                FullyConnectedLayer.multiply(this.weights.getData(), deltaTensor.getData(), passback.getData());
                deltaTensor.freeRef();
                return passback;
            }).toArray(i -> new Tensor[i]));
            inObj[0].accumulate(buffer, tensorList);
        }
    }) {

        @Override
        protected void _free() {
            indata.freeRef();
            FullyConnectedLayer.this.freeRef();
            for (@Nonnull Result result : inObj) {
                result.freeRef();
            }
            FullyConnectedLayer.this.weights.freeRef();
        }

        @Override
        public boolean isAlive() {
            return !isFrozen() || Arrays.stream(inObj).anyMatch(x -> x.isAlive());
        }
    };
}