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

Example 41 with TensorArray

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

the class SimpleGpuEval method getDelta.

/**
 * Gets delta.
 *
 * @param output the output
 * @return the delta
 */
@Nonnull
public TensorArray getDelta(final TensorList output) {
    return TensorArray.wrap(output.stream().map(t -> {
        @Nullable Tensor map = t.map(v -> 1.0);
        t.freeRef();
        return map;
    }).toArray(i -> new Tensor[i]));
}

Example 42 with TensorArray

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

the class ImgPixelSoftmaxLayer method eval.

/**
 * Eval nn result.
 *
 * @param input the input
 * @return the nn result
 */
@Nonnull
public Result eval(@Nonnull final Result input) {
    final TensorList inputData = input.getData();
    inputData.addRef();
    input.addRef();
    int[] inputDims = inputData.getDimensions();
    assert 3 == inputDims.length;
    final int inputBands = inputDims[2];
    final int width = inputDims[0];
    final int height = inputDims[1];
    TensorArray maxima = TensorArray.wrap(inputData.stream().map(inputTensor -> {
        try {
            return new Tensor(width, height, 1).setByCoord(c -> {
                return IntStream.range(0, inputBands).mapToDouble(band -> {
                    int[] coords = c.getCoords();
                    return inputTensor.get(coords[0], coords[1], band);
                }).max().getAsDouble();
            });
        } finally {
            inputTensor.freeRef();
        }
    }).toArray(i -> new Tensor[i]));
    TensorArray exps = TensorArray.wrap(IntStream.range(0, inputData.length()).mapToObj(index -> {
        final Tensor inputTensor = inputData.get(index);
        Tensor maxTensor = maxima.get(index);
        try {
            return new Tensor(inputDims).setByCoord(c -> {
                int[] coords = c.getCoords();
                return Math.exp(inputTensor.get(c) - maxTensor.get(coords[0], coords[1], 0));
            });
        } finally {
            inputTensor.freeRef();
            maxTensor.freeRef();
        }
    }).toArray(i -> new Tensor[i]));
    maxima.freeRef();
    TensorArray sums = TensorArray.wrap(exps.stream().map(expTensor -> {
        try {
            return new Tensor(width, height, 1).setByCoord(c -> {
                return IntStream.range(0, inputBands).mapToDouble(band -> {
                    int[] coords = c.getCoords();
                    return expTensor.get(coords[0], coords[1], band);
                }).sum();
            });
        } finally {
            expTensor.freeRef();
        }
    }).toArray(i -> new Tensor[i]));
    TensorArray output = TensorArray.wrap(IntStream.range(0, inputData.length()).mapToObj(index -> {
        Tensor sumTensor = sums.get(index);
        Tensor expTensor = exps.get(index);
        try {
            return new Tensor(inputDims).setByCoord(c -> {
                int[] coords = c.getCoords();
                return (expTensor.get(c) / sumTensor.get(coords[0], coords[1], 0));
            });
        } finally {
            sumTensor.freeRef();
            expTensor.freeRef();
        }
    }).toArray(i -> new Tensor[i]));
    return new Result(output, (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        if (input.isAlive()) {
            TensorArray dots = TensorArray.wrap(IntStream.range(0, inputData.length()).mapToObj(index -> {
                final Tensor deltaTensor = delta.get(index);
                Tensor expTensor = exps.get(index);
                try {
                    return new Tensor(width, height, 1).setByCoord(c -> {
                        return IntStream.range(0, inputBands).mapToDouble(band -> {
                            int[] coords = c.getCoords();
                            return expTensor.get(coords[0], coords[1], band) * deltaTensor.get(coords[0], coords[1], band);
                        }).sum();
                    });
                } finally {
                    expTensor.freeRef();
                    deltaTensor.freeRef();
                }
            }).toArray(i -> new Tensor[i]));
            TensorArray passback = TensorArray.wrap(IntStream.range(0, inputData.length()).mapToObj(index -> {
                final Tensor deltaTensor = delta.get(index);
                final Tensor expTensor = exps.get(index);
                Tensor sumTensor = sums.get(index);
                Tensor dotTensor = dots.get(index);
                try {
                    return new Tensor(inputDims).setByCoord(c -> {
                        int[] coords = c.getCoords();
                        double sum = sumTensor.get(coords[0], coords[1], 0);
                        double dot = dotTensor.get(coords[0], coords[1], 0);
                        double deltaValue = deltaTensor.get(c);
                        double expValue = expTensor.get(c);
                        return (sum * deltaValue - dot) * expValue / (sum * sum);
                    });
                } finally {
                    deltaTensor.freeRef();
                    expTensor.freeRef();
                    sumTensor.freeRef();
                    dotTensor.freeRef();
                }
            }).toArray(i -> new Tensor[i]));
            input.accumulate(buffer, passback);
            dots.freeRef();
        }
    }) {

        @Override
        protected void _free() {
            inputData.freeRef();
            input.freeRef();
            sums.freeRef();
            exps.freeRef();
        }

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

Example 43 with TensorArray

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

the class ImgPixelSumLayer method evalAndFree.

/**
 * Eval nn result.
 *
 * @param input the input
 * @return the nn result
 */
@Nonnull
public Result evalAndFree(@Nonnull final Result input) {
    final TensorList inputData = input.getData();
    int[] inputDims = inputData.getDimensions();
    assert 3 == inputDims.length;
    return new Result(TensorArray.wrap(inputData.stream().map(tensor -> {
        Tensor result = new Tensor(inputDims[0], inputDims[1], 1).setByCoord(c -> {
            return IntStream.range(0, inputDims[2]).mapToDouble(i -> {
                int[] coords = c.getCoords();
                return tensor.get(coords[0], coords[1], i);
            }).sum();
        });
        tensor.freeRef();
        return result;
    }).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        if (input.isAlive()) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(delta.stream().map(deltaTensor -> {
                int[] deltaDims = deltaTensor.getDimensions();
                Tensor result = new Tensor(deltaDims[0], deltaDims[1], inputDims[2]).setByCoord(c -> {
                    int[] coords = c.getCoords();
                    return deltaTensor.get(coords[0], coords[1], 0);
                });
                deltaTensor.freeRef();
                return result;
            }).toArray(i -> new Tensor[i]));
            input.accumulate(buffer, tensorArray);
        }
    }) {

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

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

Example 44 with TensorArray

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

the class ImgReshapeLayer method eval.

@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
    // assert Arrays.stream(inObj).flatMapToDouble(input-> input.getData().stream().flatMapToDouble(x-> Arrays.stream(x.getData()))).allMatch(v->Double.isFinite(v));
    Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
    final Result input = inObj[0];
    final TensorList batch = input.getData();
    @Nonnull final int[] inputDims = batch.getDimensions();
    assert 3 == inputDims.length;
    assert expand || 0 == inputDims[0] % kernelSizeX : (inputDims[0] + " % " + kernelSizeX);
    assert expand || 0 == inputDims[1] % kernelSizeY : (inputDims[1] + " % " + kernelSizeY);
    assert !expand || 0 == inputDims[2] % (kernelSizeX * kernelSizeY);
    // assert input.getData().stream().flatMapToDouble(x-> Arrays.stream(x.getData())).allMatch(v->Double.isFinite(v));
    Tensor outputDims;
    if (expand) {
        outputDims = new Tensor(inputDims[0] * kernelSizeX, inputDims[1] * kernelSizeY, inputDims[2] / (kernelSizeX * kernelSizeY));
    } else {
        outputDims = new Tensor(inputDims[0] / kernelSizeX, inputDims[1] / kernelSizeY, inputDims[2] * kernelSizeX * kernelSizeY);
    }
    TensorArray data = TensorArray.wrap(IntStream.range(0, batch.length()).parallel().mapToObj(dataIndex -> {
        Tensor inputData = batch.get(dataIndex);
        Tensor tensor = expand ? ImgReshapeLayer.copyExpand(inputData, outputDims.copy()) : ImgReshapeLayer.copyCondense(inputData, outputDims.copy());
        inputData.freeRef();
        return tensor;
    }).toArray(i -> new Tensor[i]));
    outputDims.freeRef();
    return new Result(data, (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList error) -> {
        // assert error.stream().flatMapToDouble(x-> Arrays.stream(x.getData())).allMatch(v->Double.isFinite(v));
        if (input.isAlive()) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, error.length()).parallel().mapToObj(dataIndex -> {
                @Nonnull final Tensor passback = new Tensor(inputDims);
                @Nullable final Tensor err = error.get(dataIndex);
                Tensor tensor = expand ? ImgReshapeLayer.copyCondense(err, passback) : ImgReshapeLayer.copyExpand(err, passback);
                err.freeRef();
                return tensor;
            }).toArray(i -> new Tensor[i]));
            input.accumulate(buffer, tensorArray);
        }
    }) {

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

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

Example 45 with TensorArray

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

the class ImgTileSelectLayer 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();
    @Nonnull final int[] inputDims = batch.getDimensions();
    assert 3 == inputDims.length;
    @Nonnull final int[] dimOut = getViewDimensions(inputDims, new int[] { sizeY, sizeX, inputDims[2] }, new int[] { positionX, positionY, 0 });
    return new Result(TensorArray.wrap(IntStream.range(0, batch.length()).parallel().mapToObj(dataIndex -> {
        @Nonnull final Tensor outputData = new Tensor(dimOut);
        Tensor inputData = batch.get(dataIndex);
        copy(inputData, outputData, positionX, positionY);
        inputData.freeRef();
        return outputData;
    }).toArray(i -> new Tensor[i])), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList error) -> {
        if (input.isAlive()) {
            @Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, error.length()).parallel().mapToObj(dataIndex -> {
                @Nullable final Tensor err = error.get(dataIndex);
                @Nonnull final Tensor passback = new Tensor(inputDims);
                copy(err, passback, -positionX, -positionY);
                err.freeRef();
                return passback;
            }).toArray(i -> new Tensor[i]));
            input.accumulate(buffer, tensorArray);
        }
    }) {

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

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