Java Code Examples for org.nd4j.linalg.factory.Nd4j#dataType()
The following examples show how to use
org.nd4j.linalg.factory.Nd4j#dataType() .
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Example 1
| Source File: TestGraphNodes.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testReshapeNode() {
Nd4j.getRandom().setSeed(12345);
GraphVertex reshapeVertex = new ReshapeVertex(null, "", -1, 'c', new int[] {-1, 736}, null, Nd4j.dataType());
val inputShape = new long[] {1, 1, 1, 736};
INDArray input = Nd4j.create(inputShape);
reshapeVertex.setInputs(input);
INDArray out = reshapeVertex.doForward(false, LayerWorkspaceMgr.noWorkspaces());
assertArrayEquals(new long[] {1, 736}, out.shape());
reshapeVertex.setEpsilon(out);
INDArray[] backward = reshapeVertex.doBackward(false, LayerWorkspaceMgr.noWorkspaces()).getSecond();
assertTrue(Arrays.equals(backward[0].shape(), inputShape));
}
Example 2
| Source File: JcublasLevel3.java From nd4j with Apache License 2.0 | 5 votes |
|
@Override
protected void dsymm(char Order, char Side, char Uplo, int M, int N, double alpha, INDArray A, int lda, INDArray B,
int ldb, double beta, INDArray C, int ldc) {
if (Nd4j.dataType() != DataBuffer.Type.DOUBLE)
logger.warn("DOUBLE symm called");
Nd4j.getExecutioner().push();
CudaContext ctx = allocator.getFlowController().prepareAction(C, A, B);
CublasPointer aPointer = new CublasPointer(A, ctx);
CublasPointer bPointer = new CublasPointer(B, ctx);
CublasPointer cPointer = new CublasPointer(C, ctx);
cublasHandle_t handle = ctx.getHandle();
synchronized (handle) {
cublasSetStream_v2(new cublasContext(handle), new CUstream_st(ctx.getOldStream()));
cublasDsymm_v2(new cublasContext(handle), convertSideMode(Side), convertUplo(Uplo), M, N,
new DoublePointer(alpha), (DoublePointer) aPointer.getDevicePointer(), lda,
(DoublePointer) bPointer.getDevicePointer(), ldb, new DoublePointer(beta),
(DoublePointer) cPointer.getDevicePointer(), ldc);
}
allocator.registerAction(ctx, C, A, B);
OpExecutionerUtil.checkForAny(C);
}
Example 3
| Source File: JcublasLevel3.java From nd4j with Apache License 2.0 | 5 votes |
|
@Override
protected void dsyr2k(char Order, char Uplo, char Trans, int N, int K, double alpha, INDArray A, int lda,
INDArray B, int ldb, double beta, INDArray C, int ldc) {
if (Nd4j.dataType() != DataBuffer.Type.DOUBLE)
logger.warn("DOUBLE syr2k called");
Nd4j.getExecutioner().push();
CudaContext ctx = allocator.getFlowController().prepareAction(C, A, B);
CublasPointer aPointer = new CublasPointer(A, ctx);
CublasPointer bPointer = new CublasPointer(B, ctx);
CublasPointer cPointer = new CublasPointer(C, ctx);
cublasHandle_t handle = ctx.getHandle();
synchronized (handle) {
cublasSetStream_v2(new cublasContext(handle), new CUstream_st(ctx.getOldStream()));
cublasDsyr2k_v2(new cublasContext(handle), convertUplo(Uplo), Trans, N, K, new DoublePointer(alpha),
(DoublePointer) aPointer.getDevicePointer(), lda,
(DoublePointer) bPointer.getDevicePointer(), ldb, new DoublePointer(beta),
(DoublePointer) cPointer.getDevicePointer(), ldc);
}
allocator.registerAction(ctx, C, A, B);
OpExecutionerUtil.checkForAny(C);
}
Example 4
| Source File: GradCheckTransforms.java From nd4j with Apache License 2.0 | 5 votes |
|
@Before
public void before() throws Exception {
Nd4j.create(1);
initialType = Nd4j.dataType();
Nd4j.setDataType(DataBuffer.Type.DOUBLE);
Nd4j.getRandom().setSeed(123);
}
Example 5
| Source File: JcublasLevel1.java From nd4j with Apache License 2.0 | 5 votes |
|
@Override
protected void saxpy(long N, float alpha, INDArray X, int incX, INDArray Y, int incY) {
if (Nd4j.dataType() != DataBuffer.Type.FLOAT)
logger.warn("FLOAT axpy called");
// CudaContext ctx = allocator.getFlowController().prepareAction(Y, X);
Nd4j.getExecutioner().exec(new Axpy(X, Y, alpha, N));
OpExecutionerUtil.checkForAny(Y);
/*
CublasPointer xAPointer = new CublasPointer(X, ctx);
CublasPointer xBPointer = new CublasPointer(Y, ctx);
cublasHandle_t handle = ctx.getHandle();
synchronized (handle) {
cublasSetStream_v2(new cublasContext(handle), new CUstream_st(ctx.getOldStream()));
PointerPointer p = new cublasContext(handle);
cublasSaxpy_v2(p,
N,
alpha,
xAPointer.getDevicePointer(),
incX,
xBPointer.getDevicePointer(),
incY);
}
*/
// allocator.registerAction(ctx, Y, X);
}
Example 6
| Source File: GradCheckReductions.java From nd4j with Apache License 2.0 | 5 votes |
|
@Before
public void before() throws Exception {
Nd4j.create(1);
initialType = Nd4j.dataType();
Nd4j.setDataType(DataBuffer.Type.DOUBLE);
Nd4j.getRandom().setSeed(123);
}
Example 7
| Source File: JCublasNDArrayFactory.java From nd4j with Apache License 2.0 | 4 votes |
|
public INDArray[] tear(INDArray tensor, int... dimensions) {
if (tensor.isCompressed())
Nd4j.getCompressor().decompressi(tensor);
Arrays.sort(dimensions);
Pair<DataBuffer, DataBuffer> tadBuffers = Nd4j.getExecutioner().getTADManager().getTADOnlyShapeInfo(tensor, dimensions);
long tadLength = 1;
val shape = new long[dimensions.length];
for (int i = 0; i < dimensions.length; i++) {
tadLength *= tensor.shape()[dimensions[i]];
shape[i] = tensor.shape()[dimensions[i]];
}
int numTads = (int)(tensor.lengthLong() / tadLength);
INDArray[] result = new INDArray[numTads];
long[] xPointers = new long[numTads];
CudaContext context = AtomicAllocator.getInstance().getFlowController().prepareAction(null, tensor);
for (int x = 0; x < numTads; x++) {
result[x] = Nd4j.createUninitialized(shape);
context = AtomicAllocator.getInstance().getFlowController().prepareAction(result[x]);
xPointers[x] = AtomicAllocator.getInstance().getPointer(result[x], context).address();
}
CudaDoubleDataBuffer tempX = new CudaDoubleDataBuffer(numTads);
AtomicAllocator.getInstance().memcpyBlocking(tempX, new LongPointer(xPointers), xPointers.length * 8, 0);
PointerPointer extraz = new PointerPointer(null, // not used
context.getOldStream(), AtomicAllocator.getInstance().getDeviceIdPointer());
if (Nd4j.dataType() == DataBuffer.Type.DOUBLE) {
nativeOps.tearDouble(extraz,
(DoublePointer) AtomicAllocator.getInstance().getPointer(tensor, context),
(LongPointer) AtomicAllocator.getInstance().getPointer(tensor.shapeInfoDataBuffer(), context),
new PointerPointer(AtomicAllocator.getInstance().getPointer(tempX, context)),
(LongPointer) AtomicAllocator.getInstance().getPointer(result[0].shapeInfoDataBuffer(), context),
(LongPointer) AtomicAllocator.getInstance().getPointer(tadBuffers.getFirst(), context),
new LongPointerWrapper(AtomicAllocator.getInstance().getPointer(tadBuffers.getSecond(), context))
);
} else if (Nd4j.dataType() == DataBuffer.Type.FLOAT) {
nativeOps.tearFloat(extraz,
(FloatPointer) AtomicAllocator.getInstance().getPointer(tensor, context),
(LongPointer) AtomicAllocator.getInstance().getPointer(tensor.shapeInfoDataBuffer(), context),
new PointerPointer(AtomicAllocator.getInstance().getPointer(tempX, context)),
(LongPointer) AtomicAllocator.getInstance().getPointer(result[0].shapeInfoDataBuffer(), context),
(LongPointer) AtomicAllocator.getInstance().getPointer(tadBuffers.getFirst(), context),
new LongPointerWrapper(AtomicAllocator.getInstance().getPointer(tadBuffers.getSecond(), context))
);
} else if (Nd4j.dataType() == DataBuffer.Type.HALF) {
nativeOps.tearHalf(extraz,
(ShortPointer) AtomicAllocator.getInstance().getPointer(tensor, context),
(LongPointer) AtomicAllocator.getInstance().getPointer(tensor.shapeInfoDataBuffer(), context),
new PointerPointer(AtomicAllocator.getInstance().getPointer(tempX, context)),
(LongPointer) AtomicAllocator.getInstance().getPointer(result[0].shapeInfoDataBuffer(), context),
(LongPointer) AtomicAllocator.getInstance().getPointer(tadBuffers.getFirst(), context),
new LongPointerWrapper(AtomicAllocator.getInstance().getPointer(tadBuffers.getSecond(), context))
);
}
AtomicAllocator.getInstance().getFlowController().registerActionAllWrite(context, result);
AtomicAllocator.getInstance().getFlowController().registerAction(context,null, result);
return result;
}
Example 8
| Source File: AbstractCompressor.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
protected DataTypeEx getGlobalTypeEx() {
DataType type = Nd4j.dataType();
return convertType(type);
}
Example 9
| Source File: ConvolutionTests.java From nd4j with Apache License 2.0 | 4 votes |
|
@Test
@Ignore
public void testCompareIm2ColImpl() {
int[] miniBatches = {1, 3, 5};
int[] depths = {1, 3, 5};
int[] inHeights = {5, 21};
int[] inWidths = {5, 21};
int[] strideH = {1, 2};
int[] strideW = {1, 2};
int[] sizeW = {1, 2, 3};
int[] sizeH = {1, 2, 3};
int[] padH = {0, 1, 2};
int[] padW = {0, 1, 2};
boolean[] coverall = {false, true};
DataBuffer.Type[] types = new DataBuffer.Type[] {DataBuffer.Type.FLOAT, DataBuffer.Type.DOUBLE,
DataBuffer.Type.FLOAT, DataBuffer.Type.DOUBLE};
DataBuffer.AllocationMode[] modes =
new DataBuffer.AllocationMode[] {DataBuffer.AllocationMode.HEAP, DataBuffer.AllocationMode.HEAP,
DataBuffer.AllocationMode.DIRECT, DataBuffer.AllocationMode.DIRECT};
String factoryClassName = Nd4j.factory().getClass().toString().toLowerCase();
if (factoryClassName.contains("jcublas") || factoryClassName.contains("cuda")) {
//Only test direct for CUDA; test all for CPU
types = new DataBuffer.Type[] {DataBuffer.Type.FLOAT, DataBuffer.Type.DOUBLE};
modes = new DataBuffer.AllocationMode[] {DataBuffer.AllocationMode.DIRECT,
DataBuffer.AllocationMode.DIRECT};
}
DataBuffer.Type initialType = Nd4j.dataType();
for (int i = 0; i < types.length; i++) {
DataBuffer.Type type = types[i];
DataBuffer.AllocationMode mode = modes[i];
DataTypeUtil.setDTypeForContext(type);
Nd4j.alloc = mode;
AllocUtil.setAllocationModeForContext(mode);
for (int m : miniBatches) {
for (int d : depths) {
for (int h : inHeights) {
for (int w : inWidths) {
for (int sh : strideH) {
for (int sw : strideW) {
for (int kh : sizeH) {
for (int kw : sizeW) {
for (int ph : padH) {
for (int pw : padW) {
if ((w - kw + 2 * pw) % sw != 0 || (h - kh + 2 * ph) % sh != 0)
continue; //(w-kp+2*pw)/sw + 1 is not an integer, i.e., number of outputs doesn't fit
System.out.println("Running " + m + " " + d + " " + h + " " + w);
for (boolean cAll : coverall) {
INDArray in = Nd4j.rand(new int[] {m, d, h, w});
//assertEquals(in.data().allocationMode(), mode);
//assertEquals(in.data().dataType(), opType);
INDArray outOrig = OldConvolution.im2col(in, kh, kw, sh, sw, ph,
pw, -1, cAll); //Old implementation
INDArray outNew = Convolution.im2col(in, kh, kw, sh, sw, ph, pw,
cAll); //Current implementation
assertArrayEquals(outOrig.data().asFloat(),
outNew.data().asFloat(), 0.01f);
assertEquals(outOrig, outNew);
}
}
}
}
}
}
}
}
}
}
}
}
DataTypeUtil.setDTypeForContext(initialType);
}
Example 10
| Source File: TestGraphNodes.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testStackUnstackNodeVariableLength() {
Nd4j.getRandom().setSeed(12345);
GraphVertex stack = new StackVertex(null, "", -1, Nd4j.dataType());
//Test stack with variable length + mask arrays
INDArray in0 = Nd4j.rand(new int[] {5, 2, 5});
INDArray in1 = Nd4j.rand(new int[] {5, 2, 6});
INDArray in2 = Nd4j.rand(new int[] {5, 2, 7});
INDArray mask0 = Nd4j.ones(5, 5);
INDArray mask1 = Nd4j.ones(5, 6);
INDArray mask2 = Nd4j.ones(5, 7);
stack.setInputs(in0, in1, in2);
Pair<INDArray, MaskState> p =
stack.feedForwardMaskArrays(new INDArray[] {mask0, mask1, mask2}, MaskState.Active, 5);
assertArrayEquals(new long[] {15, 7}, p.getFirst().shape());
assertEquals(MaskState.Active, p.getSecond());
INDArray out = stack.doForward(false, LayerWorkspaceMgr.noWorkspaces());
assertEquals(in0, out.get(NDArrayIndex.interval(0, 5), NDArrayIndex.all(), NDArrayIndex.interval(0, 5)));
assertEquals(in1, out.get(NDArrayIndex.interval(5, 10), NDArrayIndex.all(), NDArrayIndex.interval(0, 6)));
assertEquals(in2, out.get(NDArrayIndex.interval(10, 15), NDArrayIndex.all(), NDArrayIndex.interval(0, 7)));
stack.setEpsilon(out);
Pair<Gradient, INDArray[]> b = stack.doBackward(false, LayerWorkspaceMgr.noWorkspaces());
assertEquals(in0, b.getSecond()[0]);
assertEquals(in1, b.getSecond()[1]);
assertEquals(in2, b.getSecond()[2]);
//Test unstack with variable length + mask arrays
//Note that we don't actually need changes here - unstack has a single input, and the unstacked mask
//might be a bit longer than we really need, but it'll still be correct
GraphVertex unstack0 = new UnstackVertex(null, "u0", 0, 0, 3, Nd4j.dataType());
GraphVertex unstack1 = new UnstackVertex(null, "u1", 0, 1, 3, Nd4j.dataType());
GraphVertex unstack2 = new UnstackVertex(null, "u2", 0, 2, 3, Nd4j.dataType());
unstack0.setInputs(out);
unstack1.setInputs(out);
unstack2.setInputs(out);
INDArray f0 = unstack0.doForward(true, LayerWorkspaceMgr.noWorkspaces());
INDArray f1 = unstack1.doForward(true, LayerWorkspaceMgr.noWorkspaces());
INDArray f2 = unstack2.doForward(true, LayerWorkspaceMgr.noWorkspaces());
assertEquals(in0, f0.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 5)));
assertEquals(in1, f1.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 6)));
assertEquals(in2, f2.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 7)));
Pair<INDArray, MaskState> p0 =
unstack0.feedForwardMaskArrays(new INDArray[] {p.getFirst()}, MaskState.Active, 5);
Pair<INDArray, MaskState> p1 =
unstack1.feedForwardMaskArrays(new INDArray[] {p.getFirst()}, MaskState.Active, 5);
Pair<INDArray, MaskState> p2 =
unstack2.feedForwardMaskArrays(new INDArray[] {p.getFirst()}, MaskState.Active, 5);
assertEquals(mask0, p0.getFirst().get(NDArrayIndex.all(), NDArrayIndex.interval(0, 5)));
assertEquals(mask1, p1.getFirst().get(NDArrayIndex.all(), NDArrayIndex.interval(0, 6)));
assertEquals(mask2, p2.getFirst().get(NDArrayIndex.all(), NDArrayIndex.interval(0, 7)));
}
Example 11
| Source File: DataTypeValidationTests.java From nd4j with Apache License 2.0 | 4 votes |
|
@Before
public void setUp() {
initialType = Nd4j.dataType();
Nd4j.setDataType(DataBuffer.Type.FLOAT);
}
Example 12
| Source File: ConvolutionTestsC.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
@Ignore
public void testCompareIm2ColImpl() {
int[] miniBatches = {1, 3, 5};
int[] depths = {1, 3, 5};
int[] inHeights = {5, 21};
int[] inWidths = {5, 21};
int[] strideH = {1, 2};
int[] strideW = {1, 2};
int[] sizeW = {1, 2, 3};
int[] sizeH = {1, 2, 3};
int[] padH = {0, 1, 2};
int[] padW = {0, 1, 2};
boolean[] coverall = {false, true};
DataType[] types = new DataType[] {DataType.FLOAT, DataType.DOUBLE,
DataType.FLOAT, DataType.DOUBLE};
DataBuffer.AllocationMode[] modes =
new DataBuffer.AllocationMode[] {DataBuffer.AllocationMode.HEAP, DataBuffer.AllocationMode.HEAP,
DataBuffer.AllocationMode.DIRECT, DataBuffer.AllocationMode.DIRECT};
String factoryClassName = Nd4j.factory().getClass().toString().toLowerCase();
if (factoryClassName.contains("jcublas") || factoryClassName.contains("cuda")) {
//Only test direct for CUDA; test all for CPU
types = new DataType[] {DataType.FLOAT, DataType.DOUBLE};
modes = new DataBuffer.AllocationMode[] {DataBuffer.AllocationMode.DIRECT,
DataBuffer.AllocationMode.DIRECT};
}
DataType initialType = Nd4j.dataType();
for (int i = 0; i < types.length; i++) {
DataType type = types[i];
DataBuffer.AllocationMode mode = modes[i];
DataTypeUtil.setDTypeForContext(type);
Nd4j.alloc = mode;
AllocUtil.setAllocationModeForContext(mode);
for (int m : miniBatches) {
for (int d : depths) {
for (int h : inHeights) {
for (int w : inWidths) {
for (int sh : strideH) {
for (int sw : strideW) {
for (int kh : sizeH) {
for (int kw : sizeW) {
for (int ph : padH) {
for (int pw : padW) {
if ((w - kw + 2 * pw) % sw != 0 || (h - kh + 2 * ph) % sh != 0)
continue; //(w-kp+2*pW)/sw + 1 is not an integer, i.e., number of outputs doesn't fit
System.out.println("Running " + m + " " + d + " " + h + " " + w);
for (boolean cAll : coverall) {
INDArray in = Nd4j.rand(new int[] {m, d, h, w});
//assertEquals(in.data().allocationMode(), mode);
//assertEquals(in.data().dataType(), opType);
INDArray outOrig = OldConvolution.im2col(in, kh, kw, sh, sw, ph,
pw, -1, cAll); //Old implementation
INDArray outNew = Convolution.im2col(in, kh, kw, sh, sw, ph, pw,
cAll); //Current implementation
assertEquals(outOrig, outNew);
}
}
}
}
}
}
}
}
}
}
}
}
DataTypeUtil.setDTypeForContext(initialType);
}
Example 13
| Source File: LongTests.java From nd4j with Apache License 2.0 | 4 votes |
|
public LongTests(Nd4jBackend backend) {
super(backend);
this.initialType = Nd4j.dataType();
}
Example 14
| Source File: SpecialWorkspaceTests.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
public SpecialWorkspaceTests(Nd4jBackend backend) {
super(backend);
this.initialType = Nd4j.dataType();
}
Example 15
| Source File: TestEigen.java From nd4j with Apache License 2.0 | 4 votes |
|
public TestEigen(Nd4jBackend backend) {
super(backend);
initialType = Nd4j.dataType();
}
Example 16
| Source File: Nd4jTestsComparisonFortran.java From nd4j with Apache License 2.0 | 4 votes |
|
public Nd4jTestsComparisonFortran(Nd4jBackend backend) {
super(backend);
this.initialType = Nd4j.dataType();
}
Example 17
| Source File: BaseCudaDataBuffer.java From nd4j with Apache License 2.0 | 4 votes |
|
public BaseCudaDataBuffer(long length) {
this(length, Nd4j.dataType() == Type.DOUBLE ? 8 : Nd4j.dataType() == Type.FLOAT ? 4 : 2);
}
Example 18
| Source File: TestEigen.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
public TestEigen(Nd4jBackend backend) {
super(backend);
initialType = Nd4j.dataType();
}
Example 19
| Source File: BasicSerDeTests.java From nd4j with Apache License 2.0 | 3 votes |
|
@Test
public void testBasicDataTypeSwitch1() throws Exception {
DataBuffer.Type initialType = Nd4j.dataType();
Nd4j.setDataType(DataBuffer.Type.FLOAT);
INDArray array = Nd4j.create(new float[] {1, 2, 3, 4, 5, 6});
ByteArrayOutputStream bos = new ByteArrayOutputStream();
Nd4j.write(bos, array);
Nd4j.setDataType(DataBuffer.Type.DOUBLE);
INDArray restored = Nd4j.read(new ByteArrayInputStream(bos.toByteArray()));
assertEquals(Nd4j.create(new float[] {1, 2, 3, 4, 5, 6}), restored);
assertEquals(8, restored.data().getElementSize());
assertEquals(8, restored.shapeInfoDataBuffer().getElementSize());
Nd4j.setDataType(initialType);
}
Example 20
| Source File: CudaExecutioner.java From nd4j with Apache License 2.0 | 2 votes |
|
@Override
public INDArray thresholdDecode(INDArray encoded, INDArray target) {
DataBuffer buffer = encoded.data();
if (buffer.dataType() != DataBuffer.Type.INT)
throw new UnsupportedOperationException();
long compressedLength = buffer.getInt(0);
long originalLength = buffer.getInt(1);
if (target.lengthLong() != originalLength)
throw new ND4JIllegalStateException("originalLength ["+ originalLength+"] stored in encoded array doesn't match target length ["+ target.lengthLong()+"]");
DataBuffer result = target.data();
CudaContext context = (CudaContext) AtomicAllocator.getInstance().getDeviceContext().getContext();
//nativeOps.memsetAsync(AtomicAllocator.getInstance().getPointer(result), 0,result.length(), 0, context.getOldStream());
if (extraz.get() == null)
extraz.set(new PointerPointer(32));
PointerPointer extras = extraz.get().put(1, context.getOldStream());
//log.info("DEC Source length: {}", buffer.length());
//log.info("DEC Source: {}", Arrays.toString(buffer.asInt()));
if (Nd4j.dataType() == DataBuffer.Type.FLOAT) {
nativeOps.decodeThresholdFloat(extras, AtomicAllocator.getInstance().getPointer(buffer), compressedLength, (FloatPointer) AtomicAllocator.getInstance().getPointer(result));
} else if (Nd4j.dataType() == DataBuffer.Type.DOUBLE) {
nativeOps.decodeThresholdDouble(extras, AtomicAllocator.getInstance().getPointer(buffer), compressedLength, (DoublePointer) AtomicAllocator.getInstance().getPointer(result));
} else if (Nd4j.dataType() == DataBuffer.Type.HALF) {
nativeOps.decodeThresholdHalf(extras, AtomicAllocator.getInstance().getPointer(buffer), compressedLength, (ShortPointer) AtomicAllocator.getInstance().getPointer(result));
}
AtomicAllocator.getInstance().getAllocationPoint(result).tickDeviceWrite();
//DataBuffer result = Nd4j.getNDArrayFactory().convertDataEx(DataBuffer.TypeEx.THRESHOLD, buffer, getGlobalTypeEx());
return target;
}
