Java Code Examples for org.nd4j.linalg.api.buffer.DataType#UINT16
The following examples show how to use
org.nd4j.linalg.api.buffer.DataType#UINT16 .
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Example 1
| Source File: PythonNumpyCollectionsTest.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Parameterized.Parameters(name = "{index}: Testing with DataType={0}")
public static DataType[] params() {
return new DataType[]{
DataType.BOOL,
DataType.FLOAT16,
//DataType.BFLOAT16,
DataType.FLOAT,
DataType.DOUBLE,
DataType.INT8,
DataType.INT16,
DataType.INT32,
DataType.INT64,
DataType.UINT8,
DataType.UINT16,
DataType.UINT32,
DataType.UINT64
};
}
Example 2
| Source File: PythonNumpyTest.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Parameterized.Parameters(name = "{index}: Testing with DataType={0}")
public static DataType[] data() {
return new DataType[] {
DataType.BOOL,
DataType.FLOAT16,
DataType.BFLOAT16,
DataType.FLOAT,
DataType.DOUBLE,
DataType.INT8,
DataType.INT16,
DataType.INT32,
DataType.INT64,
DataType.UINT8,
DataType.UINT16,
DataType.UINT32,
DataType.UINT64
};
}
Example 3
| Source File: TensorflowConversion.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
private DataType typeFor(int tensorflowType) {
switch(tensorflowType) {
case DT_DOUBLE: return DataType.DOUBLE;
case DT_FLOAT: return DataType.FLOAT;
case DT_HALF: return DataType.HALF;
case DT_INT16: return DataType.SHORT;
case DT_INT32: return DataType.INT;
case DT_INT64: return DataType.LONG;
case DT_STRING: return DataType.UTF8;
case DT_INT8: return DataType.BYTE;
case DT_UINT8: return DataType.UBYTE;
case DT_UINT16: return DataType.UINT16;
case DT_UINT32: return DataType.UINT32;
case DT_UINT64: return DataType.UINT64;
case DT_BFLOAT16: return DataType.BFLOAT16;
case DT_BOOL: return DataType.BOOL;
default: throw new IllegalArgumentException("Illegal type " + tensorflowType);
}
}
Example 4
| Source File: PythonNumpyJobTest.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Parameterized.Parameters(name = "{index}: Testing with DataType={0}")
public static DataType[] params() {
return new DataType[]{
DataType.BOOL,
DataType.FLOAT16,
DataType.BFLOAT16,
DataType.FLOAT,
DataType.DOUBLE,
DataType.INT8,
DataType.INT16,
DataType.INT32,
DataType.INT64,
DataType.UINT8,
DataType.UINT16,
DataType.UINT32,
DataType.UINT64
};
}
Example 5
| Source File: ArrayOptionsHelper.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
public static DataType dataType(long opt) {
if (hasBitSet(opt, DTYPE_COMPRESSED_BIT))
return DataType.COMPRESSED;
else if (hasBitSet(opt, DTYPE_HALF_BIT))
return DataType.HALF;
else if (hasBitSet(opt, DTYPE_BFLOAT16_BIT))
return DataType.BFLOAT16;
else if (hasBitSet(opt, DTYPE_FLOAT_BIT))
return DataType.FLOAT;
else if (hasBitSet(opt, DTYPE_DOUBLE_BIT))
return DataType.DOUBLE;
else if (hasBitSet(opt, DTYPE_INT_BIT))
return hasBitSet(opt, DTYPE_UNSIGNED_BIT) ? DataType.UINT32 : DataType.INT;
else if (hasBitSet(opt, DTYPE_LONG_BIT))
return hasBitSet(opt, DTYPE_UNSIGNED_BIT) ? DataType.UINT64 : DataType.LONG;
else if (hasBitSet(opt, DTYPE_BOOL_BIT))
return DataType.BOOL;
else if (hasBitSet(opt, DTYPE_BYTE_BIT)) {
return hasBitSet(opt, DTYPE_UNSIGNED_BIT) ? DataType.UBYTE : DataType.BYTE; //Byte bit set for both UBYTE and BYTE
} else if (hasBitSet(opt, DTYPE_SHORT_BIT))
return hasBitSet(opt, DTYPE_UNSIGNED_BIT) ? DataType.UINT16 : DataType.SHORT;
else if (hasBitSet(opt, DTYPE_UTF8_BIT))
return DataType.UTF8;
else
throw new ND4JUnknownDataTypeException("Unknown extras set: [" + opt + "]");
}
Example 6
| Source File: PythonNumpyBasicTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Parameterized.Parameters(name = "{index}: Testing with DataType={0}, shape={2}")
public static Collection params() {
DataType[] types = new DataType[] {
DataType.BOOL,
DataType.FLOAT16,
DataType.BFLOAT16,
DataType.FLOAT,
DataType.DOUBLE,
DataType.INT8,
DataType.INT16,
DataType.INT32,
DataType.INT64,
DataType.UINT8,
DataType.UINT16,
DataType.UINT32,
DataType.UINT64
};
long[][] shapes = new long[][]{
new long[]{2, 3},
new long[]{3},
new long[]{1},
new long[]{} // scalar
};
List<Object[]> ret = new ArrayList<>();
for (DataType type: types){
for (long[] shape: shapes){
ret.add(new Object[]{type, shape, Arrays.toString(shape)});
}
}
return ret;
}
Example 7
| Source File: ArrayOptionsHelper.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
public static DataType convertToDataType(org.tensorflow.framework.DataType dataType) {
switch (dataType) {
case DT_UINT16:
return DataType.UINT16;
case DT_UINT32:
return DataType.UINT32;
case DT_UINT64:
return DataType.UINT64;
case DT_BOOL:
return DataType.BOOL;
case DT_BFLOAT16:
return DataType.BFLOAT16;
case DT_FLOAT:
return DataType.FLOAT;
case DT_INT32:
return DataType.INT;
case DT_INT64:
return DataType.LONG;
case DT_INT8:
return DataType.BYTE;
case DT_INT16:
return DataType.SHORT;
case DT_DOUBLE:
return DataType.DOUBLE;
case DT_UINT8:
return DataType.UBYTE;
case DT_HALF:
return DataType.HALF;
case DT_STRING:
return DataType.UTF8;
default:
throw new UnsupportedOperationException("Unknown TF data type: [" + dataType.name() + "]");
}
}
Example 8
| Source File: ND4JUtil.java From konduit-serving with Apache License 2.0 | 5 votes |
|
public static DataType typeNDArrayTypeToNd4j(@NonNull NDArrayType type){
switch (type){
case DOUBLE:
return DataType.DOUBLE;
case FLOAT:
return DataType.FLOAT;
case FLOAT16:
return DataType.FLOAT16;
case BFLOAT16:
return DataType.BFLOAT16;
case INT64:
return DataType.INT64;
case INT32:
return DataType.INT32;
case INT16:
return DataType.INT16;
case INT8:
return DataType.INT8;
case UINT64:
return DataType.UINT64;
case UINT32:
return DataType.UINT32;
case UINT16:
return DataType.UINT16;
case UINT8:
return DataType.UINT8;
case BOOL:
return DataType.BOOL;
case UTF8:
return DataType.UTF8;
default:
throw new UnsupportedOperationException("Unable to convert datatype to ND4J datatype: " + type);
}
}
Example 9
| Source File: CustomOpsTests.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testSizeTypes(){
List<DataType> failed = new ArrayList<>();
for(DataType dt : new DataType[]{DataType.LONG, DataType.INT, DataType.SHORT, DataType.BYTE,
DataType.UINT64, DataType.UINT32, DataType.UINT16, DataType.UBYTE,
DataType.DOUBLE, DataType.FLOAT, DataType.HALF, DataType.BFLOAT16}) {
INDArray in = Nd4j.create(DataType.FLOAT, 100);
INDArray out = Nd4j.scalar(dt, 0);
INDArray e = Nd4j.scalar(dt, 100);
DynamicCustomOp op = DynamicCustomOp.builder("size")
.addInputs(in)
.addOutputs(out)
.build();
try {
Nd4j.exec(op);
assertEquals(e, out);
} catch (Throwable t){
failed.add(dt);
}
}
if(!failed.isEmpty()){
fail("Failed datatypes: " + failed.toString());
}
}
Example 10
| Source File: BaseCpuDataBuffer.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
public void actualizePointerAndIndexer() {
val cptr = ptrDataBuffer.primaryBuffer();
// skip update if pointers are equal
if (cptr != null && pointer != null && cptr.address() == pointer.address())
return;
val t = dataType();
if (t == DataType.BOOL) {
pointer = new PagedPointer(cptr, length).asBoolPointer();
setIndexer(BooleanIndexer.create((BooleanPointer) pointer));
} else if (t == DataType.UBYTE) {
pointer = new PagedPointer(cptr, length).asBytePointer();
setIndexer(UByteIndexer.create((BytePointer) pointer));
} else if (t == DataType.BYTE) {
pointer = new PagedPointer(cptr, length).asBytePointer();
setIndexer(ByteIndexer.create((BytePointer) pointer));
} else if (t == DataType.UINT16) {
pointer = new PagedPointer(cptr, length).asShortPointer();
setIndexer(UShortIndexer.create((ShortPointer) pointer));
} else if (t == DataType.SHORT) {
pointer = new PagedPointer(cptr, length).asShortPointer();
setIndexer(ShortIndexer.create((ShortPointer) pointer));
} else if (t == DataType.UINT32) {
pointer = new PagedPointer(cptr, length).asIntPointer();
setIndexer(UIntIndexer.create((IntPointer) pointer));
} else if (t == DataType.INT) {
pointer = new PagedPointer(cptr, length).asIntPointer();
setIndexer(IntIndexer.create((IntPointer) pointer));
} else if (t == DataType.UINT64) {
pointer = new PagedPointer(cptr, length).asLongPointer();
setIndexer(LongIndexer.create((LongPointer) pointer));
} else if (t == DataType.LONG) {
pointer = new PagedPointer(cptr, length).asLongPointer();
setIndexer(LongIndexer.create((LongPointer) pointer));
} else if (t == DataType.BFLOAT16) {
pointer = new PagedPointer(cptr, length).asShortPointer();
setIndexer(Bfloat16Indexer.create((ShortPointer) pointer));
} else if (t == DataType.HALF) {
pointer = new PagedPointer(cptr, length).asShortPointer();
setIndexer(HalfIndexer.create((ShortPointer) pointer));
} else if (t == DataType.FLOAT) {
pointer = new PagedPointer(cptr, length).asFloatPointer();
setIndexer(FloatIndexer.create((FloatPointer) pointer));
} else if (t == DataType.DOUBLE) {
pointer = new PagedPointer(cptr, length).asDoublePointer();
setIndexer(DoubleIndexer.create((DoublePointer) pointer));
} else if (t == DataType.UTF8) {
pointer = new PagedPointer(cptr, length()).asBytePointer();
setIndexer(ByteIndexer.create((BytePointer) pointer));
} else
throw new IllegalArgumentException("Unknown datatype: " + dataType());
}
Example 11
| Source File: NumpyArray.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Override
public INDArray toJava(PythonObject pythonObject) {
log.info("Converting PythonObject to INDArray...");
PyObject np = PyImport_ImportModule("numpy");
PyObject ndarray = PyObject_GetAttrString(np, "ndarray");
if (PyObject_IsInstance(pythonObject.getNativePythonObject(), ndarray) != 1) {
Py_DecRef(ndarray);
Py_DecRef(np);
throw new PythonException("Object is not a numpy array! Use Python.ndarray() to convert object to a numpy array.");
}
Py_DecRef(ndarray);
Py_DecRef(np);
PyArrayObject npArr = new PyArrayObject(pythonObject.getNativePythonObject());
long[] shape = new long[PyArray_NDIM(npArr)];
SizeTPointer shapePtr = PyArray_SHAPE(npArr);
if (shapePtr != null)
shapePtr.get(shape, 0, shape.length);
long[] strides = new long[shape.length];
SizeTPointer stridesPtr = PyArray_STRIDES(npArr);
if (stridesPtr != null)
stridesPtr.get(strides, 0, strides.length);
int npdtype = PyArray_TYPE(npArr);
DataType dtype;
switch (npdtype) {
case NPY_DOUBLE:
dtype = DataType.DOUBLE;
break;
case NPY_FLOAT:
dtype = DataType.FLOAT;
break;
case NPY_SHORT:
dtype = DataType.SHORT;
break;
case NPY_INT:
dtype = DataType.INT32;
break;
case NPY_LONG:
dtype = DataType.INT64;
break;
case NPY_UINT:
dtype = DataType.UINT32;
break;
case NPY_BYTE:
dtype = DataType.INT8;
break;
case NPY_UBYTE:
dtype = DataType.UINT8;
break;
case NPY_BOOL:
dtype = DataType.BOOL;
break;
case NPY_HALF:
dtype = DataType.FLOAT16;
break;
case NPY_LONGLONG:
dtype = DataType.INT64;
break;
case NPY_USHORT:
dtype = DataType.UINT16;
break;
case NPY_ULONG:
case NPY_ULONGLONG:
dtype = DataType.UINT64;
break;
default:
throw new PythonException("Unsupported array data type: " + npdtype);
}
long size = 1;
for (int i = 0; i < shape.length; size *= shape[i++]) ;
INDArray ret;
long address = PyArray_DATA(npArr).address();
String key = address + "_" + size + "_" + dtype;
DataBuffer buff = cache.get(key);
if (buff == null) {
try (MemoryWorkspace ws = Nd4j.getMemoryManager().scopeOutOfWorkspaces()) {
Pointer ptr = NativeOpsHolder.getInstance().getDeviceNativeOps().pointerForAddress(address);
ptr = ptr.limit(size);
ptr = ptr.capacity(size);
buff = Nd4j.createBuffer(ptr, size, dtype);
cache.put(key, buff);
}
}
int elemSize = buff.getElementSize();
long[] nd4jStrides = new long[strides.length];
for (int i = 0; i < strides.length; i++) {
nd4jStrides[i] = strides[i] / elemSize;
}
ret = Nd4j.create(buff, shape, nd4jStrides, 0, Shape.getOrder(shape, nd4jStrides, 1), dtype);
Nd4j.getAffinityManager().tagLocation(ret, AffinityManager.Location.HOST);
log.info("Done.");
return ret;
}
Example 12
| Source File: GlobalPoolingMaskingTests.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testMaskLayerDataTypes(){
for(DataType dt : new DataType[]{DataType.FLOAT16, DataType.BFLOAT16, DataType.FLOAT, DataType.DOUBLE,
DataType.INT8, DataType.INT16, DataType.INT32, DataType.INT64,
DataType.UINT8, DataType.UINT16, DataType.UINT32, DataType.UINT64}){
INDArray mask = Nd4j.rand(DataType.FLOAT, 2, 10).addi(0.3).castTo(dt);
for(DataType networkDtype : new DataType[]{DataType.FLOAT16, DataType.BFLOAT16, DataType.FLOAT, DataType.DOUBLE}){
INDArray in = Nd4j.rand(networkDtype, 2, 5, 10);
INDArray label1 = Nd4j.rand(networkDtype, 2, 5);
INDArray label2 = Nd4j.rand(networkDtype, 2, 5, 10);
for(PoolingType pt : PoolingType.values()) {
//System.out.println("Net: " + networkDtype + ", mask: " + dt + ", pt=" + pt);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.list()
.layer(new GlobalPoolingLayer(pt))
.layer(new OutputLayer.Builder().nIn(5).nOut(5).activation(Activation.TANH).lossFunction(LossFunctions.LossFunction.MSE).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
net.output(in, false, mask, null);
net.output(in, false, mask, null);
MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder()
.list()
.layer(new RnnOutputLayer.Builder().nIn(5).nOut(5).activation(Activation.TANH).lossFunction(LossFunctions.LossFunction.MSE).build())
.build();
MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
net2.init();
net2.output(in, false, mask, mask);
net2.output(in, false, mask, mask);
net.fit(in, label1, mask, null);
net2.fit(in, label2, mask, mask);
}
}
}
}
Example 13
| Source File: ExecDebuggingListener.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
private static String createString(INDArray arr){
StringBuilder sb = new StringBuilder();
if(arr.isEmpty()){
sb.append("Nd4j.empty(DataType.").append(arr.dataType()).append(");");
} else {
sb.append("Nd4j.createFromArray(");
DataType dt = arr.dataType();
switch (dt){
case DOUBLE:
double[] dArr = arr.dup().data().asDouble();
sb.append(Arrays.toString(dArr).replaceAll("[\\[\\]]", ""));
break;
case FLOAT:
case HALF:
case BFLOAT16:
float[] fArr = arr.dup().data().asFloat();
sb.append(Arrays.toString(fArr)
.replaceAll(",", "f,")
.replaceAll("]", "f")
.replaceAll("[\\[\\]]", ""));
break;
case LONG:
case UINT32:
case UINT64:
long[] lArr = arr.dup().data().asLong();
sb.append(Arrays.toString(lArr)
.replaceAll(",", "L,")
.replaceAll("]", "L")
.replaceAll("[\\[\\]]", ""));
break;
case INT:
case SHORT:
case UBYTE:
case BYTE:
case UINT16:
case BOOL:
int[] iArr = arr.dup().data().asInt();
sb.append(Arrays.toString(iArr).replaceAll("[\\[\\]]", ""));
break;
case UTF8:
break;
case COMPRESSED:
case UNKNOWN:
break;
}
sb.append(").reshape(").append(Arrays.toString(arr.shape()).replaceAll("[\\[\\]]", ""))
.append(")");
if(dt == DataType.HALF || dt == DataType.BFLOAT16 || dt == DataType.UINT32 || dt == DataType.UINT64 ||
dt == DataType.SHORT || dt == DataType.UBYTE || dt == DataType.BYTE || dt == DataType.UINT16 || dt == DataType.BOOL){
sb.append(".cast(DataType.").append(arr.dataType()).append(")");
}
}
return sb.toString();
}
Example 14
| Source File: BaseCpuDataBuffer.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
/**
*
* @param length
* @param elementSize
*/
public BaseCpuDataBuffer(long length, int elementSize) {
if (length < 1)
throw new IllegalArgumentException("Length must be >= 1");
initTypeAndSize();
allocationMode = AllocUtil.getAllocationModeFromContext();
this.length = length;
this.underlyingLength = length;
this.elementSize = (byte) elementSize;
if (dataType() != DataType.UTF8)
ptrDataBuffer = OpaqueDataBuffer.allocateDataBuffer(length, dataType(), false);
if (dataType() == DataType.DOUBLE) {
pointer = new PagedPointer(ptrDataBuffer.primaryBuffer(), length).asDoublePointer();
indexer = DoubleIndexer.create((DoublePointer) pointer);
} else if (dataType() == DataType.FLOAT) {
pointer = new PagedPointer(ptrDataBuffer.primaryBuffer(), length).asFloatPointer();
setIndexer(FloatIndexer.create((FloatPointer) pointer));
} else if (dataType() == DataType.INT32) {
pointer = new PagedPointer(ptrDataBuffer.primaryBuffer(), length).asIntPointer();
setIndexer(IntIndexer.create((IntPointer) pointer));
} else if (dataType() == DataType.LONG) {
pointer = new PagedPointer(ptrDataBuffer.primaryBuffer(), length).asLongPointer();
setIndexer(LongIndexer.create((LongPointer) pointer));
} else if (dataType() == DataType.SHORT) {
pointer = new PagedPointer(ptrDataBuffer.primaryBuffer(), length).asShortPointer();
setIndexer(ShortIndexer.create((ShortPointer) pointer));
} else if (dataType() == DataType.BYTE) {
pointer = new PagedPointer(ptrDataBuffer.primaryBuffer(), length).asBytePointer();
setIndexer(ByteIndexer.create((BytePointer) pointer));
} else if (dataType() == DataType.UBYTE) {
pointer = new PagedPointer(ptrDataBuffer.primaryBuffer(), length).asBytePointer();
setIndexer(UByteIndexer.create((BytePointer) pointer));
} else if (dataType() == DataType.UTF8) {
ptrDataBuffer = OpaqueDataBuffer.allocateDataBuffer(length, INT8, false);
pointer = new PagedPointer(ptrDataBuffer.primaryBuffer(), length).asBytePointer();
setIndexer(ByteIndexer.create((BytePointer) pointer));
} else if(dataType() == DataType.FLOAT16){
pointer = new PagedPointer(ptrDataBuffer.primaryBuffer(), length).asShortPointer();
setIndexer(HalfIndexer.create((ShortPointer) pointer));
} else if(dataType() == DataType.BFLOAT16){
pointer = new PagedPointer(ptrDataBuffer.primaryBuffer(), length).asShortPointer();
setIndexer(Bfloat16Indexer.create((ShortPointer) pointer));
} else if(dataType() == DataType.BOOL){
pointer = new PagedPointer(ptrDataBuffer.primaryBuffer(), length).asBoolPointer();
setIndexer(BooleanIndexer.create((BooleanPointer) pointer));
} else if(dataType() == DataType.UINT16){
pointer = new PagedPointer(ptrDataBuffer.primaryBuffer(), length).asShortPointer();
setIndexer(UShortIndexer.create((ShortPointer) pointer));
} else if(dataType() == DataType.UINT32){
pointer = new PagedPointer(ptrDataBuffer.primaryBuffer(), length).asIntPointer();
setIndexer(UIntIndexer.create((IntPointer) pointer));
} else if (dataType() == DataType.UINT64) {
pointer = new PagedPointer(ptrDataBuffer.primaryBuffer(), length).asLongPointer();
setIndexer(LongIndexer.create((LongPointer) pointer));
}
Nd4j.getDeallocatorService().pickObject(this);
}
Example 15
| Source File: UInt16Buffer.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
/**
* Initialize the opType of this buffer
*/
@Override
protected void initTypeAndSize() {
type = DataType.UINT16;
elementSize = 2;
}
Example 16
| Source File: BaseCudaDataBuffer.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
public void actualizePointerAndIndexer() {
val cptr = ptrDataBuffer.primaryBuffer();
// skip update if pointers are equal
if (cptr != null && pointer != null && cptr.address() == pointer.address())
return;
val t = dataType();
if (t == DataType.BOOL) {
pointer = new PagedPointer(cptr, length).asBoolPointer();
setIndexer(BooleanIndexer.create((BooleanPointer) pointer));
} else if (t == DataType.UBYTE) {
pointer = new PagedPointer(cptr, length).asBytePointer();
setIndexer(UByteIndexer.create((BytePointer) pointer));
} else if (t == DataType.BYTE) {
pointer = new PagedPointer(cptr, length).asBytePointer();
setIndexer(ByteIndexer.create((BytePointer) pointer));
} else if (t == DataType.UINT16) {
pointer = new PagedPointer(cptr, length).asShortPointer();
setIndexer(UShortIndexer.create((ShortPointer) pointer));
} else if (t == DataType.SHORT) {
pointer = new PagedPointer(cptr, length).asShortPointer();
setIndexer(ShortIndexer.create((ShortPointer) pointer));
} else if (t == DataType.UINT32) {
pointer = new PagedPointer(cptr, length).asIntPointer();
setIndexer(UIntIndexer.create((IntPointer) pointer));
} else if (t == DataType.INT) {
pointer = new PagedPointer(cptr, length).asIntPointer();
setIndexer(IntIndexer.create((IntPointer) pointer));
} else if (t == DataType.UINT64) {
pointer = new PagedPointer(cptr, length).asLongPointer();
setIndexer(LongIndexer.create((LongPointer) pointer));
} else if (t == DataType.LONG) {
pointer = new PagedPointer(cptr, length).asLongPointer();
setIndexer(LongIndexer.create((LongPointer) pointer));
} else if (t == DataType.BFLOAT16) {
pointer = new PagedPointer(cptr, length).asShortPointer();
setIndexer(Bfloat16Indexer.create((ShortPointer) pointer));
} else if (t == DataType.HALF) {
pointer = new PagedPointer(cptr, length).asShortPointer();
setIndexer(HalfIndexer.create((ShortPointer) pointer));
} else if (t == DataType.FLOAT) {
pointer = new PagedPointer(cptr, length).asFloatPointer();
setIndexer(FloatIndexer.create((FloatPointer) pointer));
} else if (t == DataType.DOUBLE) {
pointer = new PagedPointer(cptr, length).asDoublePointer();
setIndexer(DoubleIndexer.create((DoublePointer) pointer));
} else if (t == DataType.UTF8) {
pointer = new PagedPointer(cptr, length()).asBytePointer();
setIndexer(ByteIndexer.create((BytePointer) pointer));
} else
throw new IllegalArgumentException("Unknown datatype: " + dataType());
}
Example 17
| Source File: CudaUInt16DataBuffer.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Override
public DataType dataType() {
return DataType.UINT16;
}
Example 18
| Source File: CudaUInt16DataBuffer.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
/**
* Initialize the opType of this buffer
*/
@Override
protected void initTypeAndSize() {
elementSize = 2;
type = DataType.UINT16;
}
Example 19
| Source File: PythonObject.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
public NumpyArray toNumpy() throws PythonException{
PyObject np = PyImport_ImportModule("numpy");
PyObject ndarray = PyObject_GetAttrString(np, "ndarray");
if (PyObject_IsInstance(nativePythonObject, ndarray) != 1){
throw new PythonException("Object is not a numpy array! Use Python.ndarray() to convert object to a numpy array.");
}
Py_DecRef(ndarray);
Py_DecRef(np);
Pointer objPtr = new Pointer(nativePythonObject);
PyArrayObject npArr = new PyArrayObject(objPtr);
Pointer ptr = PyArray_DATA(npArr);
long[] shape = new long[PyArray_NDIM(npArr)];
SizeTPointer shapePtr = PyArray_SHAPE(npArr);
if (shapePtr != null)
shapePtr.get(shape, 0, shape.length);
long[] strides = new long[shape.length];
SizeTPointer stridesPtr = PyArray_STRIDES(npArr);
if (stridesPtr != null)
stridesPtr.get(strides, 0, strides.length);
int npdtype = PyArray_TYPE(npArr);
DataType dtype;
switch (npdtype){
case NPY_DOUBLE:
dtype = DataType.DOUBLE; break;
case NPY_FLOAT:
dtype = DataType.FLOAT; break;
case NPY_SHORT:
dtype = DataType.SHORT; break;
case NPY_INT:
dtype = DataType.INT32; break;
case NPY_LONG:
dtype = DataType.LONG; break;
case NPY_UINT:
dtype = DataType.UINT32; break;
case NPY_BYTE:
dtype = DataType.INT8; break;
case NPY_UBYTE:
dtype = DataType.UINT8; break;
case NPY_BOOL:
dtype = DataType.BOOL; break;
case NPY_HALF:
dtype = DataType.FLOAT16; break;
case NPY_LONGLONG:
dtype = DataType.INT64; break;
case NPY_USHORT:
dtype = DataType.UINT16; break;
case NPY_ULONG:
case NPY_ULONGLONG:
dtype = DataType.UINT64; break;
default:
throw new PythonException("Unsupported array data type: " + npdtype);
}
return new NumpyArray(ptr.address(), shape, strides, dtype);
}
