Java Code Examples for org.nd4j.linalg.api.buffer.DataType#BFLOAT16
The following examples show how to use
org.nd4j.linalg.api.buffer.DataType#BFLOAT16 .
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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: PythonNumpyBasicTest.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testExecution(){
List<PythonVariable> inputs = new ArrayList<>();
INDArray x = Nd4j.ones(dataType, shape);
INDArray y = Nd4j.zeros(dataType, shape);
INDArray z = (dataType == DataType.BOOL)?x:x.mul(y.add(2));
z = (dataType == DataType.BFLOAT16)? z.castTo(DataType.FLOAT): z;
PythonType<INDArray> arrType = PythonTypes.get("numpy.ndarray");
inputs.add(new PythonVariable<>("x", arrType, x));
inputs.add(new PythonVariable<>("y", arrType, y));
List<PythonVariable> outputs = new ArrayList<>();
PythonVariable<INDArray> output = new PythonVariable<>("z", arrType);
outputs.add(output);
String code = (dataType == DataType.BOOL)?"z = x":"z = x * (y + 2)";
if (shape.length == 0){ // scalar special case
code += "\nimport numpy as np\nz = np.asarray(float(z), dtype=x.dtype)";
}
PythonExecutioner.exec(code, inputs, outputs);
INDArray z2 = output.getValue();
Assert.assertEquals(z.dataType(), z2.dataType());
Assert.assertEquals(z, z2);
}
Example 4
| 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 5
| Source File: SameDiffTests.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testRngSanityCheck(){
Nd4j.getRandom().setSeed(12345);
for(DataType dt : new DataType[]{DataType.FLOAT, DataType.DOUBLE,DataType.BFLOAT16}) {
if (!dt.isNumerical())
continue;
SameDiff sameDiff = SameDiff.create();
INDArray indaShape = Nd4j.createFromArray(3, 10);
SDVariable sdShape = sameDiff.constant(indaShape);
SDVariable random = sameDiff.random().uniform("data", 0.0, 10.0, dt, 3, 10);
INDArray out = random.eval();
String s = out.toString();
}
}
Example 6
| Source File: PythonNumpyJobTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testNumpyJobReturnAllVariables(){
PythonContextManager.deleteNonMainContexts();
List<PythonVariable> inputs = new ArrayList<>();
INDArray x = Nd4j.ones(dataType, 2, 3);
INDArray y = Nd4j.zeros(dataType, 2, 3);
INDArray z = (dataType == DataType.BOOL)?x:x.mul(y.add(2));
PythonType<INDArray> arrType = PythonTypes.get("numpy.ndarray");
inputs.add(new PythonVariable<>("x", arrType, x));
inputs.add(new PythonVariable<>("y", arrType, y));
String code = (dataType == DataType.BOOL)?"z = x":"z = x * (y + 2)";
PythonJob job = new PythonJob("job1", code, false);
List<PythonVariable> outputs = job.execAndReturnAllVariables(inputs);
INDArray x2 = (INDArray) outputs.get(0).getValue();
INDArray y2 = (INDArray) outputs.get(1).getValue();
INDArray z2 = (INDArray) outputs.get(2).getValue();
if (dataType == DataType.BFLOAT16){
x = x.castTo(DataType.FLOAT);
y = y.castTo(DataType.FLOAT);
z = z.castTo(DataType.FLOAT);
}
Assert.assertEquals(x, x2);
Assert.assertEquals(y, y2);
Assert.assertEquals(z, z2);
}
Example 7
| Source File: PythonNumpyJobTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testNumpyJobBasic(){
PythonContextManager.deleteNonMainContexts();
List<PythonVariable> inputs = new ArrayList<>();
INDArray x = Nd4j.ones(dataType, 2, 3);
INDArray y = Nd4j.zeros(dataType, 2, 3);
INDArray z = (dataType == DataType.BOOL)?x:x.mul(y.add(2));
z = (dataType == DataType.BFLOAT16)? z.castTo(DataType.FLOAT): z;
PythonType<INDArray> arrType = PythonTypes.get("numpy.ndarray");
inputs.add(new PythonVariable<>("x", arrType, x));
inputs.add(new PythonVariable<>("y", arrType, y));
List<PythonVariable> outputs = new ArrayList<>();
PythonVariable<INDArray> output = new PythonVariable<>("z", arrType);
outputs.add(output);
String code = (dataType == DataType.BOOL)?"z = x":"z = x * (y + 2)";
PythonJob job = new PythonJob("job1", code, false);
job.exec(inputs, outputs);
INDArray z2 = output.getValue();
if (dataType == DataType.BFLOAT16){
z2 = z2.castTo(DataType.FLOAT);
}
Assert.assertEquals(z, z2);
}
Example 8
| 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 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: PythonNumpyBasicTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testInplaceExecution(){
if (dataType == DataType.BOOL || dataType == DataType.BFLOAT16)return;
if (shape.length == 0) return;
List<PythonVariable> inputs = new ArrayList<>();
INDArray x = Nd4j.ones(dataType, shape);
INDArray y = Nd4j.zeros(dataType, shape);
INDArray z = x.mul(y.add(2));
// Nd4j.getAffinityManager().ensureLocation(z, AffinityManager.Location.HOST);
PythonType<INDArray> arrType = PythonTypes.get("numpy.ndarray");
inputs.add(new PythonVariable<>("x", arrType, x));
inputs.add(new PythonVariable<>("y", arrType, y));
List<PythonVariable> outputs = new ArrayList<>();
PythonVariable<INDArray> output = new PythonVariable<>("x", arrType);
outputs.add(output);
String code = "x *= y + 2";
PythonExecutioner.exec(code, inputs, outputs);
INDArray z2 = output.getValue();
Assert.assertEquals(x.dataType(), z2.dataType());
Assert.assertEquals(z.dataType(), z2.dataType());
Assert.assertEquals(x, z2);
Assert.assertEquals(z, z2);
Assert.assertEquals(x.data().pointer().address(), z2.data().pointer().address());
if("CUDA".equalsIgnoreCase(Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend"))){
Assert.assertEquals(getDeviceAddress(x), getDeviceAddress(z2));
}
}
Example 11
| Source File: PythonNumpyJobTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testMultipleNumpyJobsParallel(){
PythonContextManager.deleteNonMainContexts();
String code1 =(dataType == DataType.BOOL)?"z = x":"z = x + y";
PythonJob job1 = new PythonJob("job1", code1, false);
String code2 =(dataType == DataType.BOOL)?"z = y":"z = x - y";
PythonJob job2 = new PythonJob("job2", code2, false);
List<PythonVariable> inputs = new ArrayList<>();
INDArray x = Nd4j.ones(dataType, 2, 3);
INDArray y = Nd4j.zeros(dataType, 2, 3);
INDArray z1 = (dataType == DataType.BOOL)?x:x.add(y);
z1 = (dataType == DataType.BFLOAT16)? z1.castTo(DataType.FLOAT): z1;
INDArray z2 = (dataType == DataType.BOOL)?y:x.sub(y);
z2 = (dataType == DataType.BFLOAT16)? z2.castTo(DataType.FLOAT): z2;
PythonType<INDArray> arrType = PythonTypes.get("numpy.ndarray");
inputs.add(new PythonVariable<>("x", arrType, x));
inputs.add(new PythonVariable<>("y", arrType, y));
List<PythonVariable> outputs = new ArrayList<>();
outputs.add(new PythonVariable<>("z", arrType));
job1.exec(inputs, outputs);
assertEquals(z1, outputs.get(0).getValue());
job2.exec(inputs, outputs);
assertEquals(z2, outputs.get(0).getValue());
}
Example 12
| Source File: PythonNumpyBasicTest.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testConversion(){
INDArray arr = Nd4j.zeros(dataType, shape);
PythonObject npArr = PythonTypes.convert(arr);
INDArray arr2 = PythonTypes.<INDArray>getPythonTypeForPythonObject(npArr).toJava(npArr);
if (dataType == DataType.BFLOAT16){
arr = arr.castTo(DataType.FLOAT);
}
Assert.assertEquals(arr,arr2);
}
Example 13
| 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 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: 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 16
| Source File: BFloat16Buffer.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
/**
* Initialize the opType of this buffer
*/
@Override
protected void initTypeAndSize() {
type = DataType.BFLOAT16;
elementSize = 2;
}
Example 17
| 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 18
| 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 19
| Source File: CudaBfloat16DataBuffer.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
/**
* Initialize the opType of this buffer
*/
@Override
protected void initTypeAndSize() {
elementSize = 2;
type = DataType.BFLOAT16;
}
Example 20
| Source File: PythonObject.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
public PythonObject(NumpyArray npArray) {
int numpyType;
INDArray indArray = npArray.getNd4jArray();
DataType dataType = indArray.dataType();
switch (dataType) {
case DOUBLE:
numpyType = NPY_DOUBLE;
break;
case FLOAT:
case BFLOAT16:
numpyType = NPY_FLOAT;
break;
case SHORT:
numpyType = NPY_SHORT;
break;
case INT:
numpyType = NPY_INT;
break;
case LONG:
numpyType = NPY_INT64;
break;
case UINT16:
numpyType = NPY_USHORT;
break;
case UINT32:
numpyType = NPY_UINT;
break;
case UINT64:
numpyType = NPY_UINT64;
break;
case BOOL:
numpyType = NPY_BOOL;
break;
case BYTE:
numpyType = NPY_BYTE;
break;
case UBYTE:
numpyType = NPY_UBYTE;
break;
case HALF:
numpyType = NPY_HALF;
break;
default:
throw new RuntimeException("Unsupported dtype: " + npArray.getDtype());
}
long[] shape = indArray.shape();
INDArray inputArray = indArray;
if(dataType == DataType.BFLOAT16) {
log.warn("\n\nThe given nd4j array \n\n{}\n\n is of BFLOAT16 datatype. " +
"Casting a copy of it to FLOAT and creating the respective numpy array from it.\n", indArray);
inputArray = indArray.castTo(DataType.FLOAT);
}
//Sync to host memory in the case of CUDA, before passing the host memory pointer to Python
if(inputArray.data() instanceof BaseDataBuffer){
((BaseDataBuffer)inputArray.data()).syncToPrimary();
}
nativePythonObject = PyArray_New(PyArray_Type(), shape.length, new SizeTPointer(shape),
numpyType, null,
inputArray.data().addressPointer(),
0, NPY_ARRAY_CARRAY, null);
}
