public class PtNDManager
extends ai.djl.ndarray.BaseNDManager
PtNDManager
is the PyTorch implementation of NDManager
.Modifier and Type | Method and Description |
---|---|
java.nio.ByteBuffer |
allocateDirect(int capacity) |
ai.djl.ndarray.NDArray |
arange(float start,
float stop,
float step,
ai.djl.ndarray.types.DataType dataType,
ai.djl.Device device) |
ai.djl.ndarray.NDArray |
arange(int start,
int stop,
int step,
ai.djl.ndarray.types.DataType dataType,
ai.djl.Device device) |
PtNDArray |
create(java.nio.Buffer data,
ai.djl.ndarray.types.Shape shape,
ai.djl.ndarray.types.DataType dataType) |
PtNDArray |
create(Pointer handle)
Creates an
PtNDArray with the given Native Memory Pointer and attaches to this
manager. |
PtNDArray |
create(ai.djl.ndarray.types.Shape shape,
ai.djl.ndarray.types.DataType dataType,
ai.djl.Device device) |
ai.djl.ndarray.NDArray |
createCSR(java.nio.Buffer data,
long[] indptr,
long[] indices,
ai.djl.ndarray.types.Shape shape,
ai.djl.Device device) |
ai.djl.ndarray.NDArray |
createRowSparse(java.nio.Buffer data,
ai.djl.ndarray.types.Shape dataShape,
long[] indices,
ai.djl.ndarray.types.Shape shape,
ai.djl.Device device) |
ai.djl.ndarray.NDArray |
eye(int rows,
int cols,
int k,
ai.djl.ndarray.types.DataType dataType,
ai.djl.Device device) |
ai.djl.engine.Engine |
getEngine() |
void |
invoke(java.lang.String operation,
ai.djl.ndarray.NDArray[] src,
ai.djl.ndarray.NDArray[] dest,
ai.djl.util.PairList<java.lang.String,?> params) |
ai.djl.ndarray.NDList |
invoke(java.lang.String operation,
ai.djl.ndarray.NDList src,
ai.djl.util.PairList<java.lang.String,?> params) |
ai.djl.ndarray.NDArray |
linspace(float start,
float stop,
int num,
boolean endpoint,
ai.djl.Device device) |
ai.djl.ndarray.NDList |
load(java.nio.file.Path path,
ai.djl.Device device) |
PtNDManager |
newSubManager() |
PtNDManager |
newSubManager(ai.djl.Device device) |
ai.djl.ndarray.NDArray |
ones(ai.djl.ndarray.types.Shape shape,
ai.djl.ndarray.types.DataType dataType,
ai.djl.Device device) |
ai.djl.ndarray.NDArray |
randomMultinomial(int n,
ai.djl.ndarray.NDArray pValues) |
ai.djl.ndarray.NDArray |
randomMultinomial(int n,
ai.djl.ndarray.NDArray pValues,
ai.djl.ndarray.types.Shape shape) |
ai.djl.ndarray.NDArray |
randomNormal(float loc,
float scale,
ai.djl.ndarray.types.Shape shape,
ai.djl.ndarray.types.DataType dataType,
ai.djl.Device device) |
ai.djl.ndarray.NDArray |
randomUniform(float low,
float high,
ai.djl.ndarray.types.Shape shape,
ai.djl.ndarray.types.DataType dataType,
ai.djl.Device device) |
ai.djl.ndarray.NDArray |
zeros(ai.djl.ndarray.types.Shape shape,
ai.djl.ndarray.types.DataType dataType,
ai.djl.Device device) |
attach, close, debugDump, detach, getDevice, getParentManager, isOpen, toString
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
arange, arange, arange, arange, arange, arange, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, create, createCSR, createRowSparse, eye, eye, eye, linspace, linspace, linspace, load, newBaseManager, newBaseManager, newBaseManager, ones, ones, randomNormal, randomNormal, randomUniform, zeros, zeros
public java.nio.ByteBuffer allocateDirect(int capacity)
public PtNDArray create(Pointer handle)
PtNDArray
with the given Native Memory Pointer and attaches to this
manager.handle
- the array's native memory pointerpublic PtNDArray create(ai.djl.ndarray.types.Shape shape, ai.djl.ndarray.types.DataType dataType, ai.djl.Device device)
public PtNDArray create(java.nio.Buffer data, ai.djl.ndarray.types.Shape shape, ai.djl.ndarray.types.DataType dataType)
public ai.djl.ndarray.NDArray createCSR(java.nio.Buffer data, long[] indptr, long[] indices, ai.djl.ndarray.types.Shape shape, ai.djl.Device device)
public ai.djl.ndarray.NDArray createRowSparse(java.nio.Buffer data, ai.djl.ndarray.types.Shape dataShape, long[] indices, ai.djl.ndarray.types.Shape shape, ai.djl.Device device)
public ai.djl.ndarray.NDList load(java.nio.file.Path path, ai.djl.Device device)
public ai.djl.ndarray.NDArray zeros(ai.djl.ndarray.types.Shape shape, ai.djl.ndarray.types.DataType dataType, ai.djl.Device device)
public ai.djl.ndarray.NDArray ones(ai.djl.ndarray.types.Shape shape, ai.djl.ndarray.types.DataType dataType, ai.djl.Device device)
public ai.djl.ndarray.NDArray arange(int start, int stop, int step, ai.djl.ndarray.types.DataType dataType, ai.djl.Device device)
public ai.djl.ndarray.NDArray arange(float start, float stop, float step, ai.djl.ndarray.types.DataType dataType, ai.djl.Device device)
public ai.djl.ndarray.NDArray eye(int rows, int cols, int k, ai.djl.ndarray.types.DataType dataType, ai.djl.Device device)
public ai.djl.ndarray.NDArray linspace(float start, float stop, int num, boolean endpoint, ai.djl.Device device)
public ai.djl.ndarray.NDArray randomUniform(float low, float high, ai.djl.ndarray.types.Shape shape, ai.djl.ndarray.types.DataType dataType, ai.djl.Device device)
public ai.djl.ndarray.NDArray randomNormal(float loc, float scale, ai.djl.ndarray.types.Shape shape, ai.djl.ndarray.types.DataType dataType, ai.djl.Device device)
public ai.djl.ndarray.NDArray randomMultinomial(int n, ai.djl.ndarray.NDArray pValues)
public ai.djl.ndarray.NDArray randomMultinomial(int n, ai.djl.ndarray.NDArray pValues, ai.djl.ndarray.types.Shape shape)
public PtNDManager newSubManager()
public PtNDManager newSubManager(ai.djl.Device device)
public void invoke(java.lang.String operation, ai.djl.ndarray.NDArray[] src, ai.djl.ndarray.NDArray[] dest, ai.djl.util.PairList<java.lang.String,?> params)
public ai.djl.ndarray.NDList invoke(java.lang.String operation, ai.djl.ndarray.NDList src, ai.djl.util.PairList<java.lang.String,?> params)
public ai.djl.engine.Engine getEngine()