Examples with Precision com.simiacryptus.mindseye.lang.cudnn.Precision used on opensource projects

Example 21 with Precision

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

the class SumInputsLayer method evalAndFree.

@Nullable
@Override
public Result evalAndFree(@Nonnull final Result... inObj) {
    @Nonnull final int[] dimensions = inObj[0].getData().getDimensions();
    if (3 != dimensions.length) {
        throw new IllegalArgumentException("dimensions=" + Arrays.toString(dimensions));
    }
    for (int i = 1; i < inObj.length; i++) {
        if (Tensor.length(dimensions) != Tensor.length(inObj[i].getData().getDimensions())) {
            throw new IllegalArgumentException(Arrays.toString(dimensions) + " != " + Arrays.toString(inObj[i].getData().getDimensions()));
        }
    }
    if (!CudaSystem.isEnabled())
        return getCompatibilityLayer().evalAndFree(inObj);
    Stream<TensorList> tensorListStream = Arrays.stream(inObj).map(x -> x.getData());
    if (!CoreSettings.INSTANCE.isSingleThreaded() && parallel)
        tensorListStream = tensorListStream.parallel();
    return new Result(tensorListStream.reduce((leftData, rightData) -> CudaSystem.run(gpu -> {
        return gpu.addAndFree(precision, leftData, rightData);
    }, leftData, rightData)).get(), (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
        @Nonnull Stream<Result> deltaStream = Arrays.stream(inObj);
        if (!CoreSettings.INSTANCE.isSingleThreaded() && parallel)
            deltaStream = deltaStream.parallel();
        deltaStream.filter(Result::isAlive).forEach(obj -> {
            delta.addRef();
            obj.accumulate(buffer, delta);
        });
    }) {

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

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

Example 22 with Precision

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

the class SumReducerLayer method evalAndFree.

@Nullable
@Override
public Result evalAndFree(final Result... inObj) {
    if (!CudaSystem.isEnabled())
        return getCompatibilityLayer().evalAndFree(inObj);
    final Result input = inObj[0];
    final TensorList inputData = input.getData();
    @Nonnull final int[] inputSize = inputData.getDimensions();
    int length = inputData.length();
    CudaTensorList result = CudaSystem.run(gpu -> {
        CudaTensor inputTensor = gpu.getTensor(inputData, precision, MemoryType.Device, false);
        inputData.freeRef();
        CudaMemory inputMemory = inputTensor.getMemory(gpu);
        @Nonnull final CudaDevice.CudaTensorDescriptor outputDescriptor = gpu.newTensorDescriptor(precision, length, 1, 1, 1);
        long size = (long) precision.size * outputDescriptor.nStride * length;
        @Nonnull final CudaMemory outputMemory = gpu.allocate(size, MemoryType.Managed, true);
        CudaResource<cudnnReduceTensorDescriptor> reduceTensorDescriptor = gpu.cudnnCreateReduceTensorDescriptor(cudnnReduceTensorOp.CUDNN_REDUCE_TENSOR_ADD, precision.code, cudnnNanPropagation.CUDNN_NOT_PROPAGATE_NAN, cudnnReduceTensorIndices.CUDNN_REDUCE_TENSOR_NO_INDICES, cudnnIndicesType.CUDNN_32BIT_INDICES);
        @Nonnull final CudaMemory workspacePtr = gpu.allocate(inputMemory.size, MemoryType.Device, true);
        @Nonnull final CudaMemory indexPtr = gpu.allocate(12 * length, MemoryType.Device, false);
        // outputPtr.synchronize();
        gpu.cudnnReduceTensor(reduceTensorDescriptor.getPtr(), indexPtr.getPtr(), indexPtr.size, workspacePtr.getPtr(), workspacePtr.size, precision.getPointer(1.0), inputTensor.descriptor.getPtr(), inputMemory.getPtr(), precision.getPointer(0.0), outputDescriptor.getPtr(), outputMemory.getPtr());
        inputMemory.dirty();
        outputMemory.dirty();
        workspacePtr.dirty();
        Stream.of(inputTensor, inputMemory, reduceTensorDescriptor, workspacePtr, indexPtr).forEach(ReferenceCounting::freeRef);
        return CudaTensorList.wrap(CudaTensor.wrap(outputMemory, outputDescriptor, precision), length, new int[] { 1, 1, 1 }, precision);
    });
    return new Result(result, (DeltaSet<Layer> ctx, TensorList delta) -> {
        // Not supported by CuDNN?
        // CudaTensorList passback = CudaSystem.run(gpu -> {
        // CudaTensor deltaTensor = gpu.getTensor(delta, precision, MemoryType.Device, false);
        // CudaMemory deltaMemory = deltaTensor.getMemory(gpu);
        // 
        // @Nonnull final CudaDevice.CudaTensorDescriptor passbackDescriptor1 = gpu.newTensorDescriptor(
        // precision, length, inputSize[2], inputSize[1], inputSize[0]
        // );
        // @Nonnull final CudaMemory passbackPtr1 = gpu.allocate((long) precision.size * passbackDescriptor1.nStride * length, MemoryType.Device, false);
        // gpu.cudnnAddTensor(precision.getPointer(1.0), deltaTensor.descriptor.getPtr(), deltaMemory.getPtr(),
        // precision.getPointer(1.0), passbackDescriptor1.getPtr(), passbackPtr1.getPtr());
        // passbackPtr1.dirty();
        // 
        // Stream.of(deltaTensor, deltaMemory, passbackDescriptor1, passbackPtr1).forEach(ReferenceCounting::freeRef);
        // return CudaTensorList.wrap(CudaTensor.wrap(passbackPtr1, passbackDescriptor1, precision), length, inputSize, precision);
        // });
        TensorList passback = TensorArray.wrap(IntStream.range(0, length).mapToObj(i -> {
            Tensor tensor = delta.get(i);
            Tensor tensor1 = new Tensor(inputSize).setAll(tensor.get(0));
            tensor.freeRef();
            return tensor1;
        }).toArray(i -> new Tensor[i]));
        input.accumulate(ctx, passback);
    }) {

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

Example 23 with Precision

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

the class DeepDream_VGG19 method run.

/**
 * Test.
 *
 * @param log the log
 */
public void run(@Nonnull NotebookOutput log) {
    DeepDream<MultiLayerVGG19.LayerType, MultiLayerVGG19> dreamBase = new DeepDream.VGG19();
    init(log);
    Precision precision = Precision.Float;
    int imageSize = 800;
    String lakeAndForest = "H:\\SimiaCryptus\\Artistry\\Owned\\IMG_20170624_153541213-EFFECTS.jpg";
    CharSequence vanGogh = "H:\\SimiaCryptus\\Artistry\\portraits\\picasso\\800px-Pablo_Picasso,_1921,_Nous_autres_musiciens_(Three_Musicians),_oil_on_canvas,_204.5_x_188.3_cm,_Philadelphia_Museum_of_Art.jpg";
    CharSequence threeMusicians = "H:\\SimiaCryptus\\Artistry\\portraits\\picasso\\800px-Pablo_Picasso,_1921,_Nous_autres_musiciens_(Three_Musicians),_oil_on_canvas,_204.5_x_188.3_cm,_Philadelphia_Museum_of_Art.jpg";
    Map<MultiLayerVGG19.LayerType, DeepDream.ContentCoefficients> contentCoefficients = new HashMap<>();
    contentCoefficients.put(MultiLayerVGG19.LayerType.Layer_1d, new DeepDream.ContentCoefficients(0, 1e-1));
    // contentCoefficients.put(MultiLayerVGG19.LayerType.Layer_1e, new ContentCoefficients(0, 1e0));
    contentCoefficients.put(MultiLayerVGG19.LayerType.Layer_2b, new DeepDream.ContentCoefficients(0, 1e0));
    contentCoefficients.put(MultiLayerVGG19.LayerType.Layer_3a, new DeepDream.ContentCoefficients(0, 1e1));
    int trainingMinutes = 180;
    log.h1("Phase 0");
    BufferedImage canvasImage = ArtistryUtil.load(lakeAndForest, imageSize);
    // canvasImage = randomize(canvasImage);
    canvasImage = TestUtil.resize(canvasImage, imageSize, true);
    BufferedImage contentImage = ArtistryUtil.load(lakeAndForest, canvasImage.getWidth(), canvasImage.getHeight());
    dreamBase.deepDream(server, log, canvasImage, new DeepDream.StyleSetup(precision, contentImage, contentCoefficients), trainingMinutes);
    log.setFrontMatterProperty("status", "OK");
}

Example 24 with Precision

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

the class CudaLayerTester method testNonstandardBounds.

/**
 * Test nonstandard bounds tolerance statistics.
 *
 * @param log            the log
 * @param reference      the reference
 * @param inputPrototype the input prototype
 * @return the tolerance statistics
 */
@Nonnull
public ToleranceStatistics testNonstandardBounds(final NotebookOutput log, @Nullable final Layer reference, @Nonnull final Tensor[] inputPrototype) {
    log.h2("Irregular Input");
    log.p("This layer should be able to accept non-dense inputs.");
    return log.code(() -> {
        Tensor[] randomized = Arrays.stream(inputPrototype).map(x -> x.map(v -> getRandom())).toArray(i -> new Tensor[i]);
        logger.info("Input: " + Arrays.stream(randomized).map(Tensor::prettyPrint).collect(Collectors.toList()));
        Precision precision = Precision.Double;
        TensorList[] controlInput = CudaSystem.run(gpu -> {
            return Arrays.stream(randomized).map(original -> {
                TensorArray data = TensorArray.create(original);
                CudaTensorList wrap = CudaTensorList.wrap(gpu.getTensor(data, precision, MemoryType.Managed, false), 1, original.getDimensions(), precision);
                data.freeRef();
                return wrap;
            }).toArray(i -> new TensorList[i]);
        }, 0);
        @Nonnull final SimpleResult controlResult = CudaSystem.run(gpu -> {
            return SimpleGpuEval.run(reference, gpu, controlInput);
        }, 1);
        final TensorList[] irregularInput = CudaSystem.run(gpu -> {
            return Arrays.stream(randomized).map(original -> {
                return buildIrregularCudaTensor(gpu, precision, original);
            }).toArray(i -> new TensorList[i]);
        }, 0);
        @Nonnull final SimpleResult testResult = CudaSystem.run(gpu -> {
            return SimpleGpuEval.run(reference, gpu, irregularInput);
        }, 1);
        try {
            ToleranceStatistics compareOutput = compareOutput(controlResult, testResult);
            ToleranceStatistics compareDerivatives = compareDerivatives(controlResult, testResult);
            return compareDerivatives.combine(compareOutput);
        } finally {
            Arrays.stream(randomized).forEach(ReferenceCountingBase::freeRef);
            Arrays.stream(controlInput).forEach(ReferenceCounting::freeRef);
            Arrays.stream(irregularInput).forEach(x -> x.freeRef());
            controlResult.freeRef();
            testResult.freeRef();
        }
    });
}