Java Code Examples for org.nd4j.linalg.api.buffer.DataType#INT32
The following examples show how to use
org.nd4j.linalg.api.buffer.DataType#INT32 .
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Example 1
| 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 2
| 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 3
| Source File: PythonNumpyMultiThreadTest.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 4
| 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 5
| 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 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: 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);
}
Example 8
| 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 9
| 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 10
| 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;
}
