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

Example 6 with TensorArray

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

the class HyperbolicActivationLayer method eval.

@Nonnull
@Override
public Result eval(final Result... inObj) {
    final TensorList indata = inObj[0].getData();
    indata.addRef();
    inObj[0].addRef();
    weights.addRef();
    HyperbolicActivationLayer.this.addRef();
    final int itemCnt = indata.length();
    return new Result(TensorArray.wrap(IntStream.range(0, itemCnt).mapToObj(dataIndex -> {
        @Nullable final Tensor input = indata.get(dataIndex);
        @Nullable Tensor map = input.map(v -> {
            final int sign = v < 0 ? negativeMode : 1;
            final double a = Math.max(0, weights.get(v < 0 ? 1 : 0));
            return sign * (Math.sqrt(Math.pow(a * v, 2) + 1) - a) / a;
        });
        input.freeRef();
        return map;
    }).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        if (!isFrozen()) {
            IntStream.range(0, delta.length()).forEach(dataIndex -> {
                @Nullable Tensor deltaI = delta.get(dataIndex);
                @Nullable Tensor inputI = indata.get(dataIndex);
                @Nullable final double[] deltaData = deltaI.getData();
                @Nullable final double[] inputData = inputI.getData();
                @Nonnull final Tensor weightDelta = new Tensor(weights.getDimensions());
                for (int i = 0; i < deltaData.length; i++) {
                    final double d = deltaData[i];
                    final double x = inputData[i];
                    final int sign = x < 0 ? negativeMode : 1;
                    final double a = Math.max(0, weights.getData()[x < 0 ? 1 : 0]);
                    weightDelta.add(x < 0 ? 1 : 0, -sign * d / (a * a * Math.sqrt(1 + Math.pow(a * x, 2))));
                }
                deltaI.freeRef();
                inputI.freeRef();
                buffer.get(HyperbolicActivationLayer.this, weights.getData()).addInPlace(weightDelta.getData()).freeRef();
                weightDelta.freeRef();
            });
        }
        if (inObj[0].isAlive()) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, delta.length()).mapToObj(dataIndex -> {
                @Nullable Tensor inputTensor = indata.get(dataIndex);
                Tensor deltaTensor = delta.get(dataIndex);
                @Nullable final double[] deltaData = deltaTensor.getData();
                @Nonnull final int[] dims = indata.getDimensions();
                @Nonnull final Tensor passback = new Tensor(dims);
                for (int i = 0; i < passback.length(); i++) {
                    final double x = inputTensor.getData()[i];
                    final double d = deltaData[i];
                    final int sign = x < 0 ? negativeMode : 1;
                    final double a = Math.max(0, weights.getData()[x < 0 ? 1 : 0]);
                    passback.set(i, sign * d * a * x / Math.sqrt(1 + a * x * a * x));
                }
                deltaTensor.freeRef();
                inputTensor.freeRef();
                return passback;
            }).toArray(i -> new Tensor[i]));
            inObj[0].accumulate(buffer, tensorArray);
        }
    }) {

        @Override
        protected void _free() {
            indata.freeRef();
            inObj[0].freeRef();
            weights.freeRef();
            HyperbolicActivationLayer.this.freeRef();
        }

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

Example 7 with TensorArray

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

the class ImgBandScaleLayer method eval.

/**
 * Eval nn result.
 *
 * @param input the input
 * @return the nn result
 */
@Nonnull
public Result eval(@Nonnull final Result input) {
    @Nullable final double[] weights = getWeights();
    final TensorList inData = input.getData();
    inData.addRef();
    input.addRef();
    @Nullable Function<Tensor, Tensor> tensorTensorFunction = tensor -> {
        if (tensor.getDimensions().length != 3) {
            throw new IllegalArgumentException(Arrays.toString(tensor.getDimensions()));
        }
        if (tensor.getDimensions()[2] != weights.length) {
            throw new IllegalArgumentException(String.format("%s: %s does not have %s bands", getName(), Arrays.toString(tensor.getDimensions()), weights.length));
        }
        @Nullable Tensor tensor1 = tensor.mapCoords(c -> tensor.get(c) * weights[c.getCoords()[2]]);
        tensor.freeRef();
        return tensor1;
    };
    Tensor[] data = inData.stream().parallel().map(tensorTensorFunction).toArray(i -> new Tensor[i]);
    return new Result(TensorArray.wrap(data), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        if (!isFrozen()) {
            final Delta<Layer> deltaBuffer = buffer.get(ImgBandScaleLayer.this, weights);
            IntStream.range(0, delta.length()).forEach(index -> {
                @Nonnull int[] dimensions = delta.getDimensions();
                int z = dimensions[2];
                int y = dimensions[1];
                int x = dimensions[0];
                final double[] array = RecycleBin.DOUBLES.obtain(z);
                Tensor deltaTensor = delta.get(index);
                @Nullable final double[] deltaArray = deltaTensor.getData();
                Tensor inputTensor = inData.get(index);
                @Nullable final double[] inputData = inputTensor.getData();
                for (int i = 0; i < z; i++) {
                    for (int j = 0; j < y * x; j++) {
                        // array[i] += deltaArray[i + z * j];
                        array[i] += deltaArray[i * x * y + j] * inputData[i * x * y + j];
                    }
                }
                inputTensor.freeRef();
                deltaTensor.freeRef();
                assert Arrays.stream(array).allMatch(v -> Double.isFinite(v));
                deltaBuffer.addInPlace(array);
                RecycleBin.DOUBLES.recycle(array, array.length);
            });
            deltaBuffer.freeRef();
        }
        if (input.isAlive()) {
            Tensor[] tensors = delta.stream().map(t -> {
                @Nullable Tensor tensor = t.mapCoords((c) -> t.get(c) * weights[c.getCoords()[2]]);
                t.freeRef();
                return tensor;
            }).toArray(i -> new Tensor[i]);
            @Nonnull TensorArray tensorArray = TensorArray.wrap(tensors);
            input.accumulate(buffer, tensorArray);
        }
    }) {

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

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

Example 8 with TensorArray

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

the class ImgConcatLayer method eval.

@Nullable
@Override
public Result eval(@Nonnull final Result... inObj) {
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    assert Arrays.stream(inObj).allMatch(x -> x.getData().getDimensions().length == 3) : "This component is for use mapCoords 3d image tensors only";
    final int numBatches = inObj[0].getData().length();
    assert Arrays.stream(inObj).allMatch(x -> x.getData().length() == numBatches) : "All inputs must use same batch size";
    @Nonnull final int[] outputDims = Arrays.copyOf(inObj[0].getData().getDimensions(), 3);
    outputDims[2] = Arrays.stream(inObj).mapToInt(x -> x.getData().getDimensions()[2]).sum();
    if (maxBands > 0)
        outputDims[2] = Math.min(maxBands, outputDims[2]);
    assert Arrays.stream(inObj).allMatch(x -> x.getData().getDimensions()[0] == outputDims[0]) : "Inputs must be same size";
    assert Arrays.stream(inObj).allMatch(x -> x.getData().getDimensions()[1] == outputDims[1]) : "Inputs must be same size";
    @Nonnull final List<Tensor> outputTensors = new ArrayList<>();
    for (int b = 0; b < numBatches; b++) {
        @Nonnull final Tensor outputTensor = new Tensor(outputDims);
        int pos = 0;
        @Nullable final double[] outputTensorData = outputTensor.getData();
        for (int i = 0; i < inObj.length; i++) {
            @Nullable Tensor tensor = inObj[i].getData().get(b);
            @Nullable final double[] data = tensor.getData();
            System.arraycopy(data, 0, outputTensorData, pos, Math.min(data.length, outputTensorData.length - pos));
            pos += data.length;
            tensor.freeRef();
        }
        outputTensors.add(outputTensor);
    }
    return new Result(TensorArray.wrap(outputTensors.toArray(new Tensor[] {})), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        assert numBatches == data.length();
        @Nonnull final List<Tensor[]> splitBatches = new ArrayList<>();
        for (int b = 0; b < numBatches; b++) {
            @Nullable final Tensor tensor = data.get(b);
            @Nonnull final Tensor[] outputTensors2 = new Tensor[inObj.length];
            int pos = 0;
            for (int i = 0; i < inObj.length; i++) {
                @Nonnull final Tensor dest = new Tensor(inObj[i].getData().getDimensions());
                @Nullable double[] tensorData = tensor.getData();
                System.arraycopy(tensorData, pos, dest.getData(), 0, Math.min(dest.length(), tensorData.length - pos));
                pos += dest.length();
                outputTensors2[i] = dest;
            }
            tensor.freeRef();
            splitBatches.add(outputTensors2);
        }
        @Nonnull final Tensor[][] splitData = new Tensor[inObj.length][];
        for (int i = 0; i < splitData.length; i++) {
            splitData[i] = new Tensor[numBatches];
        }
        for (int i = 0; i < inObj.length; i++) {
            for (int b = 0; b < numBatches; b++) {
                splitData[i][b] = splitBatches.get(b)[i];
            }
        }
        for (int i = 0; i < inObj.length; i++) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(splitData[i]);
            inObj[i].accumulate(buffer, tensorArray);
        }
    }) {

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

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

Example 9 with TensorArray

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

the class AvgMetaLayer method eval.

@Nonnull
@Override
public Result eval(final Result... inObj) {
    final Result input = inObj[0];
    input.addRef();
    TensorList inputData = input.getData();
    final int itemCnt = inputData.length();
    @Nullable Tensor thisResult;
    boolean passback;
    if (null == lastResult || inputData.length() > minBatchCount) {
        @Nonnull final ToDoubleFunction<Coordinate> f = (c) -> IntStream.range(0, itemCnt).mapToDouble(dataIndex -> {
            Tensor tensor = inputData.get(dataIndex);
            double v = tensor.get(c);
            tensor.freeRef();
            return v;
        }).sum() / itemCnt;
        Tensor tensor = inputData.get(0);
        thisResult = tensor.mapCoords(f);
        tensor.freeRef();
        passback = true;
        if (null != lastResult)
            lastResult.freeRef();
        lastResult = thisResult;
        lastResult.addRef();
    } else {
        passback = false;
        thisResult = lastResult;
        thisResult.freeRef();
    }
    return new Result(TensorArray.create(thisResult), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList data) -> {
        if (passback && input.isAlive()) {
            @Nullable final Tensor delta = data.get(0);
            @Nonnull final Tensor[] feedback = new Tensor[itemCnt];
            Arrays.parallelSetAll(feedback, i -> new Tensor(delta.getDimensions()));
            thisResult.coordStream(true).forEach((inputCoord) -> {
                for (int inputItem = 0; inputItem < itemCnt; inputItem++) {
                    feedback[inputItem].add(inputCoord, delta.get(inputCoord) / itemCnt);
                }
            });
            delta.freeRef();
            @Nonnull TensorArray tensorArray = TensorArray.wrap(feedback);
            input.accumulate(buffer, tensorArray);
        }
    }) {

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

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

Example 10 with TensorArray

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

the class CudaTensorList method getHeapCopy.

/**
 * Gets heap copy.
 *
 * @return the heap copy
 */
@Nullable
public TensorArray getHeapCopy() {
    @Nullable TensorArray tensorList = heapCopy();
    tensorList.addRef();
    return tensorList;
}