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

Example 1 with Tensor

use of com.simiacryptus.mindseye.lang.Tensor 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 Tensor

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

the class BandAvgReducerLayer method evalAndFree.

@Nullable
@Override
public Result evalAndFree(final Result... inObj) {
    if (!CudaSystem.isEnabled())
        return getCompatibilityLayer().evalAndFree(inObj);
    final Result input = inObj[0];
    TensorList inputData = input.getData();
    @Nonnull final int[] inputSize = inputData.getDimensions();
    int length = inputData.length();
    final int bands = inputSize[2];
    CudaTensorList result = CudaSystem.run(gpu -> {
        CudaTensor inputTensor = gpu.getTensor(inputData, precision, MemoryType.Device, false);
        @Nonnull final CudaDevice.CudaTensorDescriptor outputDescriptor = gpu.newTensorDescriptor(precision, length, bands, 1, 1);
        long size = (long) precision.size * outputDescriptor.nStride * length;
        @Nonnull final CudaMemory outputPtr = gpu.allocate(size, MemoryType.Managed, true);
        CudaResource<cudnnReduceTensorDescriptor> reduceTensorDescriptor = gpu.cudnnCreateReduceTensorDescriptor(cudnnReduceTensorOp.CUDNN_REDUCE_TENSOR_AVG, precision.code, cudnnNanPropagation.CUDNN_NOT_PROPAGATE_NAN, cudnnReduceTensorIndices.CUDNN_REDUCE_TENSOR_NO_INDICES, cudnnIndicesType.CUDNN_32BIT_INDICES);
        CudaMemory inputMemory = inputTensor.getMemory(gpu);
        @Nonnull final CudaMemory workspacePtr = gpu.allocate(inputMemory.size, MemoryType.Device, true);
        @Nonnull final CudaMemory indexPtr = gpu.allocate(12 * length, MemoryType.Device, false);
        gpu.cudnnReduceTensor(reduceTensorDescriptor.getPtr(), indexPtr.getPtr(), indexPtr.size, workspacePtr.getPtr(), workspacePtr.size, precision.getPointer(alpha), inputTensor.descriptor.getPtr(), inputMemory.getPtr(), precision.getPointer(0.0), outputDescriptor.getPtr(), outputPtr.getPtr());
        outputPtr.dirty();
        inputMemory.dirty();
        Stream.of(inputMemory, inputTensor, reduceTensorDescriptor, workspacePtr, indexPtr, inputData).forEach(ReferenceCounting::freeRef);
        return CudaTensorList.wrap(CudaTensor.wrap(outputPtr, outputDescriptor, precision), length, new int[] { 1, 1, bands }, precision);
    });
    int pixels = inputSize[0] * inputSize[1];
    return new Result(result, (DeltaSet<Layer> ctx, TensorList delta) -> {
        TensorList passback;
        passback = TensorArray.wrap(delta.stream().map(x -> {
            Tensor tensor = new Tensor(inputSize[0], inputSize[1], inputSize[2]).setByCoord(c -> x.get(c.getCoords()[2]) * alpha / pixels);
            x.freeRef();
            return tensor;
        }).toArray(i -> new Tensor[i]));
        // passback = CudaSystem.run(gpu -> {
        // CudaTensor deltaTensor = gpu.getTensor(delta, precision, MemoryType.Device, true);
        // @Nonnull final CudaDevice.CudaTensorDescriptor outputDescriptor = gpu.newTensorDescriptor(precision,
        // length, inputSize[2], inputSize[1], inputSize[0]);
        // @Nonnull final CudaMemory outputPtr = gpu.allocate((long) precision.size * outputDescriptor.nStride * length, MemoryType.Device, true);
        // CudaMemory deltaMemory = deltaTensor.getMemory(gpu);
        // @Nonnull final CudaDevice.CudaTensorDescriptor inputDescriptor = gpu.newTensorDescriptor(precision,
        // 1, 1, inputSize[1], inputSize[0]);
        // for(int batch=0;batch<length;batch++){
        // Tensor tensor = delta.get(batch);
        // for(int band=0;band<bands;band++){
        // int i = batch * bands + band;
        // CudaMemory img = outputPtr.withByteOffset(precision.size * i * outputDescriptor.cStride);
        // CudaMemory val = deltaMemory.withByteOffset(precision.size * i);
        // gpu.cudnnSetTensor(inputDescriptor.getPtr(), img.getPtr(), precision.getPointer(tensor.get(band) / outputDescriptor.cStride));
        // img.freeRef();
        // val.freeRef();
        // outputPtr.dirty().synchronize();
        // }
        // }
        // Stream.of(deltaMemory, deltaTensor, inputDescriptor).forEach(ReferenceCounting::freeRef);
        // return CudaTensorList.wrap(CudaTensor.wrap(outputPtr, outputDescriptor, precision), length, inputSize, precision);
        // });
        input.accumulate(ctx, passback);
    }) {

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

Example 3 with Tensor

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

the class ExplodedConvolutionGrid method read.

/**
 * Read tensor.
 *
 * @param extractor the extractor
 * @return the tensor
 */
public Tensor read(@Nonnull Function<ExplodedConvolutionLeg, Tensor> extractor) {
    if (1 == subLayers.size()) {
        return extractor.apply(subLayers.get(0));
    } else {
        @Nonnull final Tensor filterDelta = new Tensor(convolutionParams.masterFilterDimensions);
        for (@Nonnull ExplodedConvolutionLeg leg : subLayers) {
            Tensor tensor = extractor.apply(leg);
            tensor.forEach((v, c) -> {
                int[] coords = c.getCoords();
                filterDelta.set(coords[0], coords[1], getFilterBand(leg, coords[2]), v);
            }, false);
            tensor.freeRef();
        }
        return filterDelta;
    }
}

Example 4 with Tensor

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

the class MaxDropoutNoiseLayer method getCellMap.

private List<List<Coordinate>> getCellMap(@Nonnull final IntArray dims) {
    Tensor tensor = new Tensor(dims.data);
    ArrayList<List<Coordinate>> lists = new ArrayList<>(tensor.coordStream(true).collect(Collectors.groupingBy((@Nonnull final Coordinate c) -> {
        int cellId = 0;
        int max = 0;
        for (int dim = 0; dim < dims.size(); dim++) {
            final int pos = c.getCoords()[dim] / kernelSize[dim];
            cellId = cellId * max + pos;
            max = dims.get(dim) / kernelSize[dim];
        }
        return cellId;
    })).values());
    tensor.freeRef();
    return lists;
}

Example 5 with Tensor

use of com.simiacryptus.mindseye.lang.Tensor 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();
        }
    };
}