java.lang.Object
- ai.djl.pytorch.jni.JniUtils

```
public final class JniUtils
extends java.lang.Object
```
A class containing utilities to interact with the PyTorch Engine's Java Native Interface (JNI) layer.

Method Summary

All Methods Static Methods Concrete Methods
Modifier and Type	Method	Description
`static PtNDArray`	`abs(PtNDArray ndArray)`
`static PtNDArray`	`acos(PtNDArray ndArray)`
`static void`	`adamUpdate(PtNDArray weight, PtNDArray grad, PtNDArray mean, PtNDArray variance, float lr, float learningRateBiasCorrection, float wd, float rescaleGrad, float clipGrad, float beta1, float beta2, float eps, boolean adamw)`
`static PtNDArray`	`adaptiveAvgPool(PtNDArray ndArray, ai.djl.ndarray.types.Shape outputSize)`
`static PtNDArray`	`adaptiveMaxPool(PtNDArray ndArray, ai.djl.ndarray.types.Shape outputSize)`
`static PtNDArray`	`add(PtNDArray ndArray1, PtNDArray ndArray2)`
`static void`	`addi(PtNDArray ndArray1, PtNDArray ndArray2)`
`static PtNDArray`	`all(PtNDArray ndArray)`
`static PtNDArray`	`any(PtNDArray ndArray)`
`static PtNDArray`	`arange(PtNDManager manager, float start, float stop, float step, ai.djl.ndarray.types.DataType dType, ai.djl.Device device, ai.djl.ndarray.types.SparseFormat fmt)`
`static PtNDArray`	`argMax(PtNDArray ndArray)`
`static PtNDArray`	`argMax(PtNDArray ndArray, long dim, boolean keepDim)`
`static PtNDArray`	`argMin(PtNDArray ndArray)`
`static PtNDArray`	`argMin(PtNDArray ndArray, long dim, boolean keepDim)`
`static PtNDArray`	`argSort(PtNDArray ndArray, long dim, boolean keepDim)`
`static PtNDArray`	`asin(PtNDArray ndArray)`
`static PtNDArray`	`atan(PtNDArray ndArray)`
`static void`	`attachGradient(PtNDArray ndArray, boolean requiresGrad)`
`static PtNDArray`	`avgPool(PtNDArray ndArray, ai.djl.ndarray.types.Shape kernelSize, ai.djl.ndarray.types.Shape stride, ai.djl.ndarray.types.Shape padding, boolean ceilMode, boolean countIncludePad)`
`static void`	`backward(PtNDArray ndArray, PtNDArray gradNd, boolean keepGraph, boolean createGraph)`
`static PtNDArray`	`batchNorm(PtNDArray ndArray, PtNDArray gamma, PtNDArray beta, PtNDArray runningMean, PtNDArray runningVar, boolean isTraining, double momentum, double eps)`
`static PtNDArray`	`bmm(PtNDArray ndArray1, PtNDArray ndArray2)`
`static PtNDArray`	`booleanMask(PtNDArray ndArray, PtNDArray indicesNd)`
`static void`	`booleanMaskSet(PtNDArray ndArray, PtNDArray value, PtNDArray indicesNd)`
`static PtNDArray`	`broadcast(PtNDArray ndArray, ai.djl.ndarray.types.Shape shape)`
`static PtNDArray`	`cat(PtNDArray[] arrays, long dim)`
`static PtNDArray`	`ceil(PtNDArray ndArray)`
`static PtNDArray`	`clip(PtNDArray ndArray, java.lang.Number min, java.lang.Number max)`
`static PtNDArray`	`clone(PtNDArray ndArray)`
`static PtNDArray`	`complex(PtNDArray ndArray)`
`static boolean`	`contentEqual(PtNDArray ndArray1, PtNDArray ndArray2)`
`static PtNDArray`	`convolution(PtNDArray ndArray, PtNDArray weight, PtNDArray bias, ai.djl.ndarray.types.Shape stride, ai.djl.ndarray.types.Shape padding, ai.djl.ndarray.types.Shape dilation, int groups)`
`static PtNDArray`	`cos(PtNDArray ndArray)`
`static PtNDArray`	`cosh(PtNDArray ndArray)`
`static PtNDArray`	`createEmptyNdArray(PtNDManager manager, ai.djl.ndarray.types.Shape shape, ai.djl.ndarray.types.DataType dType, ai.djl.Device device, ai.djl.ndarray.types.SparseFormat fmt)`
`static PtNDArray`	`createNdFromByteBuffer(PtNDManager manager, java.nio.ByteBuffer data, ai.djl.ndarray.types.Shape shape, ai.djl.ndarray.types.DataType dType, ai.djl.ndarray.types.SparseFormat fmt, ai.djl.Device device)`
`static PtNDArray`	`createOnesNdArray(PtNDManager manager, ai.djl.ndarray.types.Shape shape, ai.djl.ndarray.types.DataType dType, ai.djl.Device device, ai.djl.ndarray.types.SparseFormat fmt)`
`static PtNDArray`	`createSparseCoo(PtNDArray indices, PtNDArray values, ai.djl.ndarray.types.Shape shape)`
`static PtNDArray`	`createZerosNdArray(PtNDManager manager, ai.djl.ndarray.types.Shape shape, ai.djl.ndarray.types.DataType dType, ai.djl.Device device, ai.djl.ndarray.types.SparseFormat fmt)`
`static PtNDArray`	`cumProd(PtNDArray ndArray, long dim, ai.djl.ndarray.types.DataType dataType)`
`static PtNDArray`	`cumSum(PtNDArray ndArray, long dim)`
`static void`	`deleteModule(long pointer)`
`static void`	`deleteNDArray(long handle)`
`static PtNDArray`	`detachGradient(PtNDArray ndArray)`
`static PtNDArray`	`div(PtNDArray ndArray1, PtNDArray ndArray2)`
`static void`	`divi(PtNDArray ndArray1, PtNDArray ndArray2)`
`static PtNDArray`	`dot(PtNDArray ndArray1, PtNDArray ndArray2)`
`static PtNDArray`	`dropout(PtNDArray ndArray, double prob, boolean training)`
`static PtNDArray`	`elu(PtNDArray ndArray, double alpha)`
`static PtNDArray`	`embedding(PtNDArray input, PtNDArray weight, boolean sparse)`
`static void`	`emptyCudaCache()`
`static void`	`enableInferenceMode(PtSymbolBlock block)`
`static void`	`enableTrainingMode(PtSymbolBlock block)`
`static PtNDArray`	`eq(PtNDArray self, PtNDArray other)`
`static PtNDArray`	`erfinv(PtNDArray ndArray)`
`static PtNDArray`	`exp(PtNDArray ndArray)`
`static PtNDArray`	`eye(PtNDManager manager, int n, int m, ai.djl.ndarray.types.DataType dataType, ai.djl.Device device, ai.djl.ndarray.types.SparseFormat fmt)`
`static PtNDArray`	`fft(PtNDArray ndArray, long length, long axis)`
`static PtNDArray`	`flatten(PtNDArray ndArray, long startDim, long endDim)`
`static PtNDArray`	`flip(PtNDArray ndArray, long[] dims)`
`static PtNDArray`	`floor(PtNDArray ndArray)`
`static PtNDArray`	`full(PtNDManager manager, ai.djl.ndarray.types.Shape shape, double fillValue, ai.djl.ndarray.types.DataType dType, ai.djl.Device device, ai.djl.ndarray.types.SparseFormat fmt)`
`static PtNDArray`	`gather(PtNDArray ndArray, PtNDArray index, long dim)`
`static PtNDArray`	`gelu(PtNDArray ndArray)`
`static java.nio.ByteBuffer`	`getByteBuffer(PtNDArray ndArray)`
`static ai.djl.ndarray.types.DataType`	`getDataType(PtNDArray ndArray)`
`static ai.djl.Device`	`getDevice(PtNDArray ndArray)`
`static java.util.Set<java.lang.String>`	`getFeatures()`
`static PtNDArray`	`getGradient(PtNDArray ndArray)`
`static java.lang.String`	`getGradientFunctionNames(PtNDArray ndArray)`
`static PtNDArray`	`getItem(PtNDArray ndArray, long[] indices, PtNDManager manager)`
`static int`	`getLayout(PtNDArray array)`
`static java.lang.String[]`	`getMethodNames(PtSymbolBlock block)`
`static int`	`getNumInteropThreads()`
`static int`	`getNumThreads()`
`static ai.djl.ndarray.types.Shape`	`getShape(PtNDArray ndArray)`
`static ai.djl.ndarray.types.SparseFormat`	`getSparseFormat(PtNDArray ndArray)`
`static ai.djl.ndarray.NDList`	`gru(PtNDArray input, PtNDArray hx, ai.djl.ndarray.NDList params, boolean hasBiases, int numLayers, double dropRate, boolean training, boolean bidirectional, boolean batchFirst)`
`static PtNDArray`	`gt(PtNDArray self, PtNDArray other)`
`static PtNDArray`	`gte(PtNDArray self, PtNDArray other)`
`static PtNDArray`	`hannWindow(PtNDManager manager, long numPoints, boolean periodic, ai.djl.Device device)`
`static PtNDArray`	`index(PtNDArray ndArray, long[] minIndices, long[] maxIndices, long[] stepIndices, PtNDManager manager)`
`static PtNDArray`	`indexAdv(PtNDArray ndArray, ai.djl.ndarray.index.NDIndex index, PtNDManager manager)`
`static void`	`indexAdvPut(PtNDArray ndArray, ai.djl.ndarray.index.NDIndex index, PtNDArray data)`
`static void`	`indexSet(PtNDArray ndArray, PtNDArray value, long[] minIndices, long[] maxIndices, long[] stepIndices)`
`static PtNDArray`	`interpolate(PtNDArray ndArray, long[] size, int mode, boolean alignCorners)`
`static PtNDArray`	`inverse(PtNDArray ndArray)`
`static boolean`	`isGradMode()`
`static PtNDArray`	`isInf(PtNDArray ndArray)`
`static PtNDArray`	`isNaN(PtNDArray ndArray)`
`static PtNDArray`	`layerNorm(PtNDArray ndArray, ai.djl.ndarray.types.Shape normalizedShape, PtNDArray gamma, PtNDArray beta, double eps)`
`static PtNDArray`	`leakyRelu(PtNDArray ndArray, double negativeSlope)`
`static PtNDArray`	`linear(PtNDArray input, PtNDArray weight, PtNDArray bias)`
`static PtNDArray`	`linspace(PtNDManager manager, float start, float stop, int step, ai.djl.ndarray.types.DataType dType, ai.djl.Device device, ai.djl.ndarray.types.SparseFormat fmt)`
`static PtSymbolBlock`	`loadModule(PtNDManager manager, java.io.InputStream is, boolean mapLocation, boolean hasSize)`
`static PtSymbolBlock`	`loadModule(PtNDManager manager, java.nio.file.Path path, boolean mapLocation, java.lang.String[] extraFileKeys, java.lang.String[] extraFileValues, boolean trainParam)`
`static long`	`loadModuleHandle(java.io.InputStream is, ai.djl.Device device, boolean mapLocation, boolean hasSize)`
`static PtNDArray`	`log(PtNDArray ndArray)`
`static PtNDArray`	`log10(PtNDArray ndArray)`
`static PtNDArray`	`log2(PtNDArray ndArray)`
`static PtNDArray`	`logicalAnd(PtNDArray ndArray1, PtNDArray ndArray2)`
`static PtNDArray`	`logicalNot(PtNDArray ndArray)`
`static PtNDArray`	`logicalOr(PtNDArray ndArray1, PtNDArray ndArray2)`
`static PtNDArray`	`logicalXor(PtNDArray ndArray1, PtNDArray ndArray2)`
`static PtNDArray`	`logSoftmax(PtNDArray ndArray, long dim, ai.djl.ndarray.types.DataType dTpe)`
`static PtNDArray`	`lpPool(PtNDArray ndArray, double normType, ai.djl.ndarray.types.Shape kernelSize, ai.djl.ndarray.types.Shape stride, boolean ceilMode)`
`static ai.djl.ndarray.NDList`	`lstm(PtNDArray input, ai.djl.ndarray.NDList hx, ai.djl.ndarray.NDList params, boolean hasBiases, int numLayers, double dropRate, boolean training, boolean bidirectional, boolean batchFirst)`
`static PtNDArray`	`lt(PtNDArray self, PtNDArray other)`
`static PtNDArray`	`lte(PtNDArray self, PtNDArray other)`
`static PtNDArray`	`matmul(PtNDArray ndArray1, PtNDArray ndArray2)`
`static PtNDArray`	`max(PtNDArray ndArray)`
`static PtNDArray`	`max(PtNDArray ndArray, long dim, boolean keepDim)`
`static PtNDArray`	`max(PtNDArray ndArray1, PtNDArray ndArray2)`
`static PtNDArray`	`maxPool(PtNDArray ndArray, ai.djl.ndarray.types.Shape kernelSize, ai.djl.ndarray.types.Shape stride, ai.djl.ndarray.types.Shape padding, boolean ceilMode)`
`static PtNDArray`	`mean(PtNDArray ndArray)`
`static PtNDArray`	`mean(PtNDArray ndArray, long dim, boolean keepDim)`
`static ai.djl.ndarray.NDList`	`median(PtNDArray ndArray, long dim, boolean keepDim)`
`static PtNDArray`	`min(PtNDArray ndArray)`
`static PtNDArray`	`min(PtNDArray ndArray, long dim, boolean keepDim)`
`static PtNDArray`	`min(PtNDArray ndArray1, PtNDArray ndArray2)`
`static ai.djl.ndarray.NDList`	`moduleGetParams(PtSymbolBlock block, PtNDManager manager)`
`static PtNDArray`	`mul(PtNDArray ndArray1, PtNDArray ndArray2)`
`static void`	`muli(PtNDArray ndArray1, PtNDArray ndArray2)`
`static PtNDArray`	`neg(PtNDArray ndArray)`
`static void`	`negi(PtNDArray ndArray)`
`static PtNDArray`	`neq(PtNDArray self, PtNDArray other)`
`static PtNDArray`	`none(PtNDArray ndArray)`
`static PtNDArray`	`nonZeros(PtNDArray ndArray)`
`static PtNDArray`	`norm(PtNDArray ndArray, int ord, int[] axes, boolean keepDims)`
`static PtNDArray`	`normal(PtNDManager manager, double mean, double std, ai.djl.ndarray.types.Shape size, ai.djl.ndarray.types.DataType dataType, ai.djl.Device device)`
`static PtNDArray`	`normalize(PtNDArray ndArray, double p, long dim, double eps)`
`static PtNDArray`	`oneHot(PtNDArray ndArray, int depth, ai.djl.ndarray.types.DataType dataType)`
`static PtNDArray`	`onesLike(PtNDArray array, ai.djl.ndarray.types.DataType dType, ai.djl.Device device, ai.djl.ndarray.types.SparseFormat fmt)`
`static PtNDArray`	`permute(PtNDArray ndArray, long[] dims)`
`static PtNDArray`	`pick(PtNDArray ndArray, PtNDArray index, long dim)`
`static PtNDArray`	`pow(PtNDArray ndArray1, PtNDArray ndArray2)`
`static void`	`powi(PtNDArray ndArray1, PtNDArray ndArray2)`
`static PtNDArray`	`prod(PtNDArray ndArray)`
`static PtNDArray`	`prod(PtNDArray ndArray, long dim, boolean keepDim)`
`static PtNDArray`	`put(PtNDArray ndArray, PtNDArray index, PtNDArray value)`
`static PtNDArray`	`randint(PtNDManager manager, long low, long high, ai.djl.ndarray.types.Shape size, ai.djl.ndarray.types.DataType dataType, ai.djl.Device device)`
`static PtNDArray`	`randperm(PtNDManager manager, long n, ai.djl.ndarray.types.DataType dataType, ai.djl.Device device)`
`static PtNDArray`	`real(PtNDArray ndArray)`
`static PtNDArray`	`relu(PtNDArray ndArray)`
`static PtNDArray`	`remainder(PtNDArray ndArray1, PtNDArray ndArray2)`
`static void`	`remainderi(PtNDArray ndArray1, PtNDArray ndArray2)`
`static PtNDArray`	`repeat(PtNDArray ndArray, long repeat, long dim)`
`static boolean`	`requiresGrad(PtNDArray ndArray)`
`static PtNDArray`	`reshape(PtNDArray ndArray, long[] shape)`
`static ai.djl.ndarray.NDList`	`rnn(PtNDArray input, PtNDArray hx, ai.djl.ndarray.NDList params, boolean hasBiases, int numLayers, ai.djl.nn.recurrent.RNN.Activation activation, double dropRate, boolean training, boolean bidirectional, boolean batchFirst)`
`static PtNDArray`	`rot90(PtNDArray ndArray, int times, int[] axes)`
`static PtNDArray`	`round(PtNDArray ndArray)`
`static PtNDArray`	`scatter(PtNDArray ndArray, PtNDArray index, PtNDArray value, int axis)`
`static PtNDArray`	`selu(PtNDArray ndArray)`
`static void`	`set(PtNDArray self, java.nio.ByteBuffer data)`
`static void`	`setBenchmarkCuDNN(boolean enable)`
`static void`	`setGradMode(boolean enable)`
`static void`	`setGraphExecutorOptimize(boolean enabled)`
`static void`	`setNumInteropThreads(int threads)`
`static void`	`setNumThreads(int threads)`
`static void`	`setSeed(long seed)`
`static void`	`sgdUpdate(PtNDArray weight, PtNDArray grad, PtNDArray state, float lr, float wd, float rescaleGrad, float clipGrad, float momentum)`
`static PtNDArray`	`sigmoid(PtNDArray ndArray)`
`static PtNDArray`	`sign(PtNDArray ndArray)`
`static void`	`signi(PtNDArray ndArray)`
`static PtNDArray`	`sin(PtNDArray ndArray)`
`static PtNDArray`	`sinh(PtNDArray ndArray)`
`static PtNDArray`	`slice(PtNDArray ndArray, long dim, long start, long stop, long step)`
`static PtNDArray`	`softmax(PtNDArray ndArray, long dim, ai.djl.ndarray.types.DataType dTpe)`
`static PtNDArray`	`softPlus(PtNDArray ndArray)`
`static PtNDArray`	`softSign(PtNDArray ndArray)`
`static PtNDArray`	`sort(PtNDArray ndArray, long dim, boolean descending)`
`static ai.djl.ndarray.NDList`	`split(PtNDArray ndArray, long[] indices, long axis)`
`static ai.djl.ndarray.NDList`	`split(PtNDArray ndArray, long size, long axis)`
`static PtNDArray`	`sqrt(PtNDArray ndArray)`
`static PtNDArray`	`square(PtNDArray ndArray)`
`static PtNDArray`	`squeeze(PtNDArray ndArray)`
`static PtNDArray`	`squeeze(PtNDArray ndArray, long dim)`
`static PtNDArray`	`stack(PtNDArray[] arrays, int dim)`
`static void`	`startProfile(boolean useCuda, boolean recordShape, boolean profileMemory)`	Calls this method to start profile the area you are interested in.
`static PtNDArray`	`stft(PtNDArray ndArray, long nFft, long hopLength, PtNDArray window, boolean center, boolean normalize, boolean returnComplex)`
`static void`	`stopProfile(java.lang.String outputFile)`
`static PtNDArray`	`sub(PtNDArray ndArray1, PtNDArray ndArray2)`
`static void`	`subi(PtNDArray ndArray1, PtNDArray ndArray2)`
`static PtNDArray`	`sum(PtNDArray ndArray)`
`static PtNDArray`	`sum(PtNDArray ndArray, long[] dims, boolean keepDim)`
`static PtNDArray`	`take(PtNDArray ndArray, PtNDArray index, PtNDManager manager)`
`static PtNDArray`	`tan(PtNDArray ndArray)`
`static PtNDArray`	`tanh(PtNDArray ndArray)`
`static PtNDArray`	`tile(PtNDArray ndArray, long[] repeats)`
`static PtNDArray`	`to(PtNDArray ndArray, ai.djl.ndarray.types.DataType dataType, ai.djl.Device device)`
`static PtNDArray`	`toDense(PtNDArray ndArray)`
`static ai.djl.ndarray.NDList`	`topK(PtNDArray ndArray, long k, long axis, boolean largest, boolean sorted)`
`static PtNDArray`	`toSparse(PtNDArray ndArray)`
`static PtNDArray`	`transpose(PtNDArray ndArray, long dim1, long dim2)`
`static PtNDArray`	`trunc(PtNDArray ndArray)`
`static PtNDArray`	`uniform(PtNDManager manager, double low, double high, ai.djl.ndarray.types.Shape size, ai.djl.ndarray.types.DataType dataType, ai.djl.Device device)`
`static ai.djl.ndarray.NDList`	`unique(PtNDArray ndArray, java.lang.Integer dim, boolean sorted, boolean returnInverse, boolean returnCounts)`
`static PtNDArray`	`unsqueeze(PtNDArray ndArray, long dim)`
`static PtNDArray`	`where(PtNDArray condition, PtNDArray self, PtNDArray other)`
`static void`	`writeModule(PtSymbolBlock block, java.io.OutputStream os, boolean writeSize)`
`static PtNDArray`	`xlogy(PtNDArray ndArray1, PtNDArray ndArray2)`
`static void`	`zeroGrad(PtNDArray weight)`
`static PtNDArray`	`zerosLike(PtNDArray array, ai.djl.ndarray.types.DataType dType, ai.djl.Device device, ai.djl.ndarray.types.SparseFormat fmt)`

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

isGradMode
```
public static boolean isGradMode()
```

setGradMode

public static void setGradMode(boolean enable)

getNumInteropThreads

public static int getNumInteropThreads()

getNumThreads
```
public static int getNumThreads()
```

setNumInteropThreads

public static void setNumInteropThreads(int threads)

setNumThreads

public static void setNumThreads(int threads)

setBenchmarkCuDNN

public static void setBenchmarkCuDNN(boolean enable)

getFeatures

public static java.util.Set<java.lang.String> getFeatures()

setSeed

public static void setSeed(long seed)

startProfile
```
public static void startProfile(boolean useCuda,
                                boolean recordShape,
                                boolean profileMemory)
```
Calls this method to start profile the area you are interested in.
Example usage
```
      JniUtils.startProfile(false, true, true);
      Predictor.predict(img);
      JniUtils.stopProfile(outputFile)
 
```
Parameters:

useCuda - Enables timing of CUDA events as well using the cudaEvent API.

recordShape - If shapes recording is set, information about input dimensions will be collected

profileMemory - Whether to report memory usage

stopProfile

public static void stopProfile(java.lang.String outputFile)

createNdFromByteBuffer

public static PtNDArray createNdFromByteBuffer(PtNDManager manager,
                                               java.nio.ByteBuffer data,
                                               ai.djl.ndarray.types.Shape shape,
                                               ai.djl.ndarray.types.DataType dType,
                                               ai.djl.ndarray.types.SparseFormat fmt,
                                               ai.djl.Device device)

emptyCudaCache
```
public static void emptyCudaCache()
```

createEmptyNdArray

public static PtNDArray createEmptyNdArray(PtNDManager manager,
                                           ai.djl.ndarray.types.Shape shape,
                                           ai.djl.ndarray.types.DataType dType,
                                           ai.djl.Device device,
                                           ai.djl.ndarray.types.SparseFormat fmt)

createZerosNdArray

public static PtNDArray createZerosNdArray(PtNDManager manager,
                                           ai.djl.ndarray.types.Shape shape,
                                           ai.djl.ndarray.types.DataType dType,
                                           ai.djl.Device device,
                                           ai.djl.ndarray.types.SparseFormat fmt)

createOnesNdArray

public static PtNDArray createOnesNdArray(PtNDManager manager,
                                          ai.djl.ndarray.types.Shape shape,
                                          ai.djl.ndarray.types.DataType dType,
                                          ai.djl.Device device,
                                          ai.djl.ndarray.types.SparseFormat fmt)

full

public static PtNDArray full(PtNDManager manager,
                             ai.djl.ndarray.types.Shape shape,
                             double fillValue,
                             ai.djl.ndarray.types.DataType dType,
                             ai.djl.Device device,
                             ai.djl.ndarray.types.SparseFormat fmt)

zerosLike

public static PtNDArray zerosLike(PtNDArray array,
                                  ai.djl.ndarray.types.DataType dType,
                                  ai.djl.Device device,
                                  ai.djl.ndarray.types.SparseFormat fmt)

onesLike

public static PtNDArray onesLike(PtNDArray array,
                                 ai.djl.ndarray.types.DataType dType,
                                 ai.djl.Device device,
                                 ai.djl.ndarray.types.SparseFormat fmt)

arange

public static PtNDArray arange(PtNDManager manager,
                               float start,
                               float stop,
                               float step,
                               ai.djl.ndarray.types.DataType dType,
                               ai.djl.Device device,
                               ai.djl.ndarray.types.SparseFormat fmt)

linspace

public static PtNDArray linspace(PtNDManager manager,
                                 float start,
                                 float stop,
                                 int step,
                                 ai.djl.ndarray.types.DataType dType,
                                 ai.djl.Device device,
                                 ai.djl.ndarray.types.SparseFormat fmt)

createSparseCoo

public static PtNDArray createSparseCoo(PtNDArray indices,
                                        PtNDArray values,
                                        ai.djl.ndarray.types.Shape shape)

to

public static PtNDArray to(PtNDArray ndArray,
                           ai.djl.ndarray.types.DataType dataType,
                           ai.djl.Device device)

toSparse

public static PtNDArray toSparse(PtNDArray ndArray)

toDense

public static PtNDArray toDense(PtNDArray ndArray)

broadcast

public static PtNDArray broadcast(PtNDArray ndArray,
                                  ai.djl.ndarray.types.Shape shape)

slice

public static PtNDArray slice(PtNDArray ndArray,
                              long dim,
                              long start,
                              long stop,
                              long step)

index

public static PtNDArray index(PtNDArray ndArray,
                              long[] minIndices,
                              long[] maxIndices,
                              long[] stepIndices,
                              PtNDManager manager)

indexAdv

public static PtNDArray indexAdv(PtNDArray ndArray,
                                 ai.djl.ndarray.index.NDIndex index,
                                 PtNDManager manager)

indexAdvPut

public static void indexAdvPut(PtNDArray ndArray,
                               ai.djl.ndarray.index.NDIndex index,
                               PtNDArray data)

indexSet

public static void indexSet(PtNDArray ndArray,
                            PtNDArray value,
                            long[] minIndices,
                            long[] maxIndices,
                            long[] stepIndices)

set

public static void set(PtNDArray self,
                       java.nio.ByteBuffer data)

gather

public static PtNDArray gather(PtNDArray ndArray,
                               PtNDArray index,
                               long dim)

take

public static PtNDArray take(PtNDArray ndArray,
                             PtNDArray index,
                             PtNDManager manager)

put

public static PtNDArray put(PtNDArray ndArray,
                            PtNDArray index,
                            PtNDArray value)

scatter

public static PtNDArray scatter(PtNDArray ndArray,
                                PtNDArray index,
                                PtNDArray value,
                                int axis)

pick

public static PtNDArray pick(PtNDArray ndArray,
                             PtNDArray index,
                             long dim)

where

public static PtNDArray where(PtNDArray condition,
                              PtNDArray self,
                              PtNDArray other)

booleanMask

public static PtNDArray booleanMask(PtNDArray ndArray,
                                    PtNDArray indicesNd)

booleanMaskSet

public static void booleanMaskSet(PtNDArray ndArray,
                                  PtNDArray value,
                                  PtNDArray indicesNd)

getItem

public static PtNDArray getItem(PtNDArray ndArray,
                                long[] indices,
                                PtNDManager manager)

clone

public static PtNDArray clone(PtNDArray ndArray)

reshape

public static PtNDArray reshape(PtNDArray ndArray,
                                long[] shape)

stack

public static PtNDArray stack(PtNDArray[] arrays,
                              int dim)

cat

public static PtNDArray cat(PtNDArray[] arrays,
                            long dim)

tile

public static PtNDArray tile(PtNDArray ndArray,
                             long[] repeats)

repeat

public static PtNDArray repeat(PtNDArray ndArray,
                               long repeat,
                               long dim)

softmax

public static PtNDArray softmax(PtNDArray ndArray,
                                long dim,
                                ai.djl.ndarray.types.DataType dTpe)

logSoftmax

public static PtNDArray logSoftmax(PtNDArray ndArray,
                                   long dim,
                                   ai.djl.ndarray.types.DataType dTpe)

argMax

public static PtNDArray argMax(PtNDArray ndArray)

argMax

public static PtNDArray argMax(PtNDArray ndArray,
                               long dim,
                               boolean keepDim)

topK

public static ai.djl.ndarray.NDList topK(PtNDArray ndArray,
                                         long k,
                                         long axis,
                                         boolean largest,
                                         boolean sorted)

argMin

public static PtNDArray argMin(PtNDArray ndArray)

argMin

public static PtNDArray argMin(PtNDArray ndArray,
                               long dim,
                               boolean keepDim)

argSort

public static PtNDArray argSort(PtNDArray ndArray,
                                long dim,
                                boolean keepDim)

sort

public static PtNDArray sort(PtNDArray ndArray,
                             long dim,
                             boolean descending)

permute

public static PtNDArray permute(PtNDArray ndArray,
                                long[] dims)

flip

public static PtNDArray flip(PtNDArray ndArray,
                             long[] dims)

transpose

public static PtNDArray transpose(PtNDArray ndArray,
                                  long dim1,
                                  long dim2)

contentEqual

public static boolean contentEqual(PtNDArray ndArray1,
                                   PtNDArray ndArray2)

add

public static PtNDArray add(PtNDArray ndArray1,
                            PtNDArray ndArray2)

addi

public static void addi(PtNDArray ndArray1,
                        PtNDArray ndArray2)

sub

public static PtNDArray sub(PtNDArray ndArray1,
                            PtNDArray ndArray2)

subi

public static void subi(PtNDArray ndArray1,
                        PtNDArray ndArray2)

mul

public static PtNDArray mul(PtNDArray ndArray1,
                            PtNDArray ndArray2)

muli

public static void muli(PtNDArray ndArray1,
                        PtNDArray ndArray2)

div

public static PtNDArray div(PtNDArray ndArray1,
                            PtNDArray ndArray2)

divi

public static void divi(PtNDArray ndArray1,
                        PtNDArray ndArray2)

remainder

public static PtNDArray remainder(PtNDArray ndArray1,
                                  PtNDArray ndArray2)

remainderi

public static void remainderi(PtNDArray ndArray1,
                              PtNDArray ndArray2)

pow

public static PtNDArray pow(PtNDArray ndArray1,
                            PtNDArray ndArray2)

powi

public static void powi(PtNDArray ndArray1,
                        PtNDArray ndArray2)

sign

public static PtNDArray sign(PtNDArray ndArray)

signi

public static void signi(PtNDArray ndArray)

logicalAnd

public static PtNDArray logicalAnd(PtNDArray ndArray1,
                                   PtNDArray ndArray2)

logicalOr

public static PtNDArray logicalOr(PtNDArray ndArray1,
                                  PtNDArray ndArray2)

logicalXor

public static PtNDArray logicalXor(PtNDArray ndArray1,
                                   PtNDArray ndArray2)

logicalNot

public static PtNDArray logicalNot(PtNDArray ndArray)

matmul

public static PtNDArray matmul(PtNDArray ndArray1,
                               PtNDArray ndArray2)

bmm

public static PtNDArray bmm(PtNDArray ndArray1,
                            PtNDArray ndArray2)

xlogy

public static PtNDArray xlogy(PtNDArray ndArray1,
                              PtNDArray ndArray2)

dot

public static PtNDArray dot(PtNDArray ndArray1,
                            PtNDArray ndArray2)

max

public static PtNDArray max(PtNDArray ndArray1,
                            PtNDArray ndArray2)

max

public static PtNDArray max(PtNDArray ndArray)

max

public static PtNDArray max(PtNDArray ndArray,
                            long dim,
                            boolean keepDim)

min

public static PtNDArray min(PtNDArray ndArray1,
                            PtNDArray ndArray2)

min

public static PtNDArray min(PtNDArray ndArray)

min

public static PtNDArray min(PtNDArray ndArray,
                            long dim,
                            boolean keepDim)

median

public static ai.djl.ndarray.NDList median(PtNDArray ndArray,
                                           long dim,
                                           boolean keepDim)

mean

public static PtNDArray mean(PtNDArray ndArray)

mean

public static PtNDArray mean(PtNDArray ndArray,
                             long dim,
                             boolean keepDim)

rot90

public static PtNDArray rot90(PtNDArray ndArray,
                              int times,
                              int[] axes)

sum

public static PtNDArray sum(PtNDArray ndArray)

sum

public static PtNDArray sum(PtNDArray ndArray,
                            long[] dims,
                            boolean keepDim)

cumProd

public static PtNDArray cumProd(PtNDArray ndArray,
                                long dim,
                                ai.djl.ndarray.types.DataType dataType)

prod

public static PtNDArray prod(PtNDArray ndArray)

prod

public static PtNDArray prod(PtNDArray ndArray,
                             long dim,
                             boolean keepDim)

cumSum

public static PtNDArray cumSum(PtNDArray ndArray,
                               long dim)

oneHot

public static PtNDArray oneHot(PtNDArray ndArray,
                               int depth,
                               ai.djl.ndarray.types.DataType dataType)

split

public static ai.djl.ndarray.NDList split(PtNDArray ndArray,
                                          long size,
                                          long axis)

split

public static ai.djl.ndarray.NDList split(PtNDArray ndArray,
                                          long[] indices,
                                          long axis)

squeeze

public static PtNDArray squeeze(PtNDArray ndArray)

squeeze

public static PtNDArray squeeze(PtNDArray ndArray,
                                long dim)

unsqueeze

public static PtNDArray unsqueeze(PtNDArray ndArray,
                                  long dim)

unique

public static ai.djl.ndarray.NDList unique(PtNDArray ndArray,
                                           java.lang.Integer dim,
                                           boolean sorted,
                                           boolean returnInverse,
                                           boolean returnCounts)

flatten

public static PtNDArray flatten(PtNDArray ndArray,
                                long startDim,
                                long endDim)

fft

public static PtNDArray fft(PtNDArray ndArray,
                            long length,
                            long axis)

stft

public static PtNDArray stft(PtNDArray ndArray,
                             long nFft,
                             long hopLength,
                             PtNDArray window,
                             boolean center,
                             boolean normalize,
                             boolean returnComplex)

real

public static PtNDArray real(PtNDArray ndArray)

complex

public static PtNDArray complex(PtNDArray ndArray)

abs

public static PtNDArray abs(PtNDArray ndArray)

square

public static PtNDArray square(PtNDArray ndArray)

floor

public static PtNDArray floor(PtNDArray ndArray)

ceil

public static PtNDArray ceil(PtNDArray ndArray)

round

public static PtNDArray round(PtNDArray ndArray)

trunc

public static PtNDArray trunc(PtNDArray ndArray)

clip

public static PtNDArray clip(PtNDArray ndArray,
                             java.lang.Number min,
                             java.lang.Number max)

exp

public static PtNDArray exp(PtNDArray ndArray)

log

public static PtNDArray log(PtNDArray ndArray)

log10

public static PtNDArray log10(PtNDArray ndArray)

log2

public static PtNDArray log2(PtNDArray ndArray)

sin

public static PtNDArray sin(PtNDArray ndArray)

cos

public static PtNDArray cos(PtNDArray ndArray)

tan

public static PtNDArray tan(PtNDArray ndArray)

asin

public static PtNDArray asin(PtNDArray ndArray)

acos

public static PtNDArray acos(PtNDArray ndArray)

atan

public static PtNDArray atan(PtNDArray ndArray)

sqrt

public static PtNDArray sqrt(PtNDArray ndArray)

sinh

public static PtNDArray sinh(PtNDArray ndArray)

cosh

public static PtNDArray cosh(PtNDArray ndArray)

tanh

public static PtNDArray tanh(PtNDArray ndArray)

sigmoid

public static PtNDArray sigmoid(PtNDArray ndArray)

all

public static PtNDArray all(PtNDArray ndArray)

any

public static PtNDArray any(PtNDArray ndArray)

none

public static PtNDArray none(PtNDArray ndArray)

eq

public static PtNDArray eq(PtNDArray self,
                           PtNDArray other)

neq

public static PtNDArray neq(PtNDArray self,
                            PtNDArray other)

gt

public static PtNDArray gt(PtNDArray self,
                           PtNDArray other)

gte

public static PtNDArray gte(PtNDArray self,
                            PtNDArray other)

lt

public static PtNDArray lt(PtNDArray self,
                           PtNDArray other)

lte

public static PtNDArray lte(PtNDArray self,
                            PtNDArray other)

neg

public static PtNDArray neg(PtNDArray ndArray)

negi

public static void negi(PtNDArray ndArray)

isNaN

public static PtNDArray isNaN(PtNDArray ndArray)

isInf

public static PtNDArray isInf(PtNDArray ndArray)

randint

public static PtNDArray randint(PtNDManager manager,
                                long low,
                                long high,
                                ai.djl.ndarray.types.Shape size,
                                ai.djl.ndarray.types.DataType dataType,
                                ai.djl.Device device)

randperm

public static PtNDArray randperm(PtNDManager manager,
                                 long n,
                                 ai.djl.ndarray.types.DataType dataType,
                                 ai.djl.Device device)

normal

public static PtNDArray normal(PtNDManager manager,
                               double mean,
                               double std,
                               ai.djl.ndarray.types.Shape size,
                               ai.djl.ndarray.types.DataType dataType,
                               ai.djl.Device device)

uniform

public static PtNDArray uniform(PtNDManager manager,
                                double low,
                                double high,
                                ai.djl.ndarray.types.Shape size,
                                ai.djl.ndarray.types.DataType dataType,
                                ai.djl.Device device)

eye

public static PtNDArray eye(PtNDManager manager,
                            int n,
                            int m,
                            ai.djl.ndarray.types.DataType dataType,
                            ai.djl.Device device,
                            ai.djl.ndarray.types.SparseFormat fmt)

hannWindow

public static PtNDArray hannWindow(PtNDManager manager,
                                   long numPoints,
                                   boolean periodic,
                                   ai.djl.Device device)

erfinv

public static PtNDArray erfinv(PtNDArray ndArray)

inverse

public static PtNDArray inverse(PtNDArray ndArray)

interpolate

public static PtNDArray interpolate(PtNDArray ndArray,
                                    long[] size,
                                    int mode,
                                    boolean alignCorners)

linear

public static PtNDArray linear(PtNDArray input,
                               PtNDArray weight,
                               PtNDArray bias)

embedding

public static PtNDArray embedding(PtNDArray input,
                                  PtNDArray weight,
                                  boolean sparse)

relu

public static PtNDArray relu(PtNDArray ndArray)

softPlus

public static PtNDArray softPlus(PtNDArray ndArray)

softSign

public static PtNDArray softSign(PtNDArray ndArray)

leakyRelu

public static PtNDArray leakyRelu(PtNDArray ndArray,
                                  double negativeSlope)

elu

public static PtNDArray elu(PtNDArray ndArray,
                            double alpha)

selu

public static PtNDArray selu(PtNDArray ndArray)

gelu

public static PtNDArray gelu(PtNDArray ndArray)

convolution

public static PtNDArray convolution(PtNDArray ndArray,
                                    PtNDArray weight,
                                    PtNDArray bias,
                                    ai.djl.ndarray.types.Shape stride,
                                    ai.djl.ndarray.types.Shape padding,
                                    ai.djl.ndarray.types.Shape dilation,
                                    int groups)

batchNorm

public static PtNDArray batchNorm(PtNDArray ndArray,
                                  PtNDArray gamma,
                                  PtNDArray beta,
                                  PtNDArray runningMean,
                                  PtNDArray runningVar,
                                  boolean isTraining,
                                  double momentum,
                                  double eps)

layerNorm

public static PtNDArray layerNorm(PtNDArray ndArray,
                                  ai.djl.ndarray.types.Shape normalizedShape,
                                  PtNDArray gamma,
                                  PtNDArray beta,
                                  double eps)

normalize

public static PtNDArray normalize(PtNDArray ndArray,
                                  double p,
                                  long dim,
                                  double eps)

dropout

public static PtNDArray dropout(PtNDArray ndArray,
                                double prob,
                                boolean training)

rnn

public static ai.djl.ndarray.NDList rnn(PtNDArray input,
                                        PtNDArray hx,
                                        ai.djl.ndarray.NDList params,
                                        boolean hasBiases,
                                        int numLayers,
                                        ai.djl.nn.recurrent.RNN.Activation activation,
                                        double dropRate,
                                        boolean training,
                                        boolean bidirectional,
                                        boolean batchFirst)

gru

public static ai.djl.ndarray.NDList gru(PtNDArray input,
                                        PtNDArray hx,
                                        ai.djl.ndarray.NDList params,
                                        boolean hasBiases,
                                        int numLayers,
                                        double dropRate,
                                        boolean training,
                                        boolean bidirectional,
                                        boolean batchFirst)

lstm

public static ai.djl.ndarray.NDList lstm(PtNDArray input,
                                         ai.djl.ndarray.NDList hx,
                                         ai.djl.ndarray.NDList params,
                                         boolean hasBiases,
                                         int numLayers,
                                         double dropRate,
                                         boolean training,
                                         boolean bidirectional,
                                         boolean batchFirst)

avgPool

public static PtNDArray avgPool(PtNDArray ndArray,
                                ai.djl.ndarray.types.Shape kernelSize,
                                ai.djl.ndarray.types.Shape stride,
                                ai.djl.ndarray.types.Shape padding,
                                boolean ceilMode,
                                boolean countIncludePad)

maxPool

public static PtNDArray maxPool(PtNDArray ndArray,
                                ai.djl.ndarray.types.Shape kernelSize,
                                ai.djl.ndarray.types.Shape stride,
                                ai.djl.ndarray.types.Shape padding,
                                boolean ceilMode)

adaptiveMaxPool

public static PtNDArray adaptiveMaxPool(PtNDArray ndArray,
                                        ai.djl.ndarray.types.Shape outputSize)

adaptiveAvgPool

public static PtNDArray adaptiveAvgPool(PtNDArray ndArray,
                                        ai.djl.ndarray.types.Shape outputSize)

lpPool

public static PtNDArray lpPool(PtNDArray ndArray,
                               double normType,
                               ai.djl.ndarray.types.Shape kernelSize,
                               ai.djl.ndarray.types.Shape stride,
                               boolean ceilMode)

getDataType

public static ai.djl.ndarray.types.DataType getDataType(PtNDArray ndArray)

getDevice

public static ai.djl.Device getDevice(PtNDArray ndArray)

getSparseFormat

public static ai.djl.ndarray.types.SparseFormat getSparseFormat(PtNDArray ndArray)

getShape

public static ai.djl.ndarray.types.Shape getShape(PtNDArray ndArray)

getByteBuffer

public static java.nio.ByteBuffer getByteBuffer(PtNDArray ndArray)

deleteNDArray

public static void deleteNDArray(long handle)

requiresGrad

public static boolean requiresGrad(PtNDArray ndArray)

getGradientFunctionNames

public static java.lang.String getGradientFunctionNames(PtNDArray ndArray)

attachGradient

public static void attachGradient(PtNDArray ndArray,
                                  boolean requiresGrad)

detachGradient

public static PtNDArray detachGradient(PtNDArray ndArray)

getGradient

public static PtNDArray getGradient(PtNDArray ndArray)

backward

public static void backward(PtNDArray ndArray,
                            PtNDArray gradNd,
                            boolean keepGraph,
                            boolean createGraph)

deleteModule

public static void deleteModule(long pointer)

setGraphExecutorOptimize

public static void setGraphExecutorOptimize(boolean enabled)

loadModule

public static PtSymbolBlock loadModule(PtNDManager manager,
                                       java.nio.file.Path path,
                                       boolean mapLocation,
                                       java.lang.String[] extraFileKeys,
                                       java.lang.String[] extraFileValues,
                                       boolean trainParam)

loadModule

public static PtSymbolBlock loadModule(PtNDManager manager,
                                       java.io.InputStream is,
                                       boolean mapLocation,
                                       boolean hasSize)
                                throws java.io.IOException

Throws:: java.io.IOException

loadModuleHandle

public static long loadModuleHandle(java.io.InputStream is,
                                    ai.djl.Device device,
                                    boolean mapLocation,
                                    boolean hasSize)
                             throws java.io.IOException

Throws:: java.io.IOException

writeModule

public static void writeModule(PtSymbolBlock block,
                               java.io.OutputStream os,
                               boolean writeSize)

moduleGetParams

public static ai.djl.ndarray.NDList moduleGetParams(PtSymbolBlock block,
                                                    PtNDManager manager)

getMethodNames

public static java.lang.String[] getMethodNames(PtSymbolBlock block)

enableInferenceMode

public static void enableInferenceMode(PtSymbolBlock block)

enableTrainingMode

public static void enableTrainingMode(PtSymbolBlock block)

zeroGrad

public static void zeroGrad(PtNDArray weight)

adamUpdate

public static void adamUpdate(PtNDArray weight,
                              PtNDArray grad,
                              PtNDArray mean,
                              PtNDArray variance,
                              float lr,
                              float learningRateBiasCorrection,
                              float wd,
                              float rescaleGrad,
                              float clipGrad,
                              float beta1,
                              float beta2,
                              float eps,
                              boolean adamw)

sgdUpdate

public static void sgdUpdate(PtNDArray weight,
                             PtNDArray grad,
                             PtNDArray state,
                             float lr,
                             float wd,
                             float rescaleGrad,
                             float clipGrad,
                             float momentum)

getLayout

public static int getLayout(PtNDArray array)

norm

public static PtNDArray norm(PtNDArray ndArray,
                             int ord,
                             int[] axes,
                             boolean keepDims)

nonZeros

public static PtNDArray nonZeros(PtNDArray ndArray)

Class JniUtils

Method Summary

Methods inherited from class java.lang.Object

Method Detail

isGradMode

setGradMode

getNumInteropThreads

getNumThreads

setNumInteropThreads

setNumThreads

setBenchmarkCuDNN

getFeatures

setSeed

startProfile

stopProfile

createNdFromByteBuffer

emptyCudaCache

createEmptyNdArray

createZerosNdArray

createOnesNdArray

full

zerosLike

onesLike

arange

linspace

createSparseCoo

to

toSparse

toDense

broadcast

slice

index

indexAdv

indexAdvPut

indexSet

set

gather

take

put

scatter

pick

where

booleanMask

booleanMaskSet

getItem

clone

reshape

stack

cat

tile

repeat

softmax

logSoftmax

argMax

argMax

topK

argMin

argMin

argSort

sort

permute

flip

transpose

contentEqual

add

addi

sub

subi

mul

muli

div

divi

remainder

remainderi

pow

powi

sign

signi

logicalAnd

logicalOr