Java Code Examples for org.nd4j.linalg.api.ndarray.INDArray#isScalar()
The following examples show how to use
org.nd4j.linalg.api.ndarray.INDArray#isScalar() .
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Example 1
| Source File: RegressionMetrics.java From konduit-serving with Apache License 2.0 | 6 votes |
|
private void handleNdArray(INDArray output) {
if(output.isVector()) {
for(int i = 0; i < output.length(); i++) {
statCounters.get(i).add(output.getDouble(i));
}
}
else if(output.isMatrix() && output.length() > 1) {
for(int i = 0; i < output.rows(); i++) {
for(int j = 0; j < output.columns(); j++) {
statCounters.get(i).add(output.getDouble(i,j));
}
}
}
else if(output.isScalar()) {
statCounters.get(0).add(output.sumNumber().doubleValue());
}
else {
throw new IllegalArgumentException("Only vectors and matrices supported right now");
}
}
Example 2
| Source File: BaseComplexNDArray.java From nd4j with Apache License 2.0 | 6 votes |
|
/**
* in place (element wise) multiplication of two matrices
*
* @param other the second ndarray to multiply
* @param result the result ndarray
* @return the result of the multiplication
*/
@Override
public IComplexNDArray muli(INDArray other, INDArray result) {
IComplexNDArray cOther = (IComplexNDArray) other;
IComplexNDArray cResult = (IComplexNDArray) result;
IComplexNDArray linear = linearView();
IComplexNDArray cOtherLinear = cOther.linearView();
IComplexNDArray cResultLinear = cResult.linearView();
if (other.isScalar())
return muli(cOther.getComplex(0), result);
IComplexNumber c = Nd4j.createComplexNumber(0, 0);
IComplexNumber d = Nd4j.createComplexNumber(0, 0);
for (int i = 0; i < length(); i++)
cResultLinear.putScalar(i, linear.getComplex(i, c).muli(cOtherLinear.getComplex(i, d)));
return cResult;
}
Example 3
| Source File: CpuNDArrayFactory.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Override
public INDArray sort(INDArray x, boolean descending, int... dimension) {
if (x.isScalar())
return x;
Arrays.sort(dimension);
Pair<DataBuffer, DataBuffer> tadBuffers = Nd4j.getExecutioner().getTADManager().getTADOnlyShapeInfo(x, dimension);
NativeOpsHolder.getInstance().getDeviceNativeOps().sortTad(null,
x.data().addressPointer(), (LongPointer) x.shapeInfoDataBuffer().addressPointer(),
null, null,
(IntPointer) Nd4j.getConstantHandler().getConstantBuffer(dimension, DataType.INT).addressPointer(),
dimension.length,
(LongPointer) tadBuffers.getFirst().addressPointer(),
new LongPointerWrapper(tadBuffers.getSecond().addressPointer()),
descending);
return x;
}
Example 4
| Source File: BaseNDArrayFactory.java From nd4j with Apache License 2.0 | 6 votes |
|
/**
* Generate a linearly spaced vector
*
* @param lower upper bound
* @param upper lower bound
* @param num the step size
* @return the linearly spaced vector
*/
@Override
public INDArray linspace(int lower, int upper, int num) {
double[] data = new double[num];
for (int i = 0; i < num; i++) {
double t = (double) i / (num - 1);
data[i] = lower * (1 - t) + t * upper;
}
//edge case for scalars
INDArray ret = Nd4j.create(data.length);
if (ret.isScalar())
return ret;
for (int i = 0; i < ret.length(); i++)
ret.putScalar(i, data[i]);
return ret;
}
Example 5
| Source File: NDArrayPreconditionsFormat.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Override
public String format(String tag, Object arg) {
if(arg == null)
return "null";
INDArray arr = (INDArray)arg;
switch (tag){
case "%ndRank":
return String.valueOf(arr.rank());
case "%ndShape":
return Arrays.toString(arr.shape());
case "%ndStride":
return Arrays.toString(arr.stride());
case "%ndLength":
return String.valueOf(arr.length());
case "%ndSInfo":
return arr.shapeInfoToString().replaceAll("\n","");
case "%nd10":
if(arr.isScalar() || arr.isEmpty()){
return arr.toString();
}
INDArray sub = arr.reshape(arr.length()).get(NDArrayIndex.interval(0, Math.min(arr.length(), 10)));
return sub.toString();
default:
//Should never happen
throw new IllegalStateException("Unknown format tag: " + tag);
}
}
Example 6
| Source File: FirstAxisIterator.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Override
public Object next() {
INDArray s = iterateOver.slice(i++);
if (s.isScalar()) {
return s.getDouble(0);
} else {
return s;
}
}
Example 7
| Source File: BaseComplexNDArray.java From nd4j with Apache License 2.0 | 5 votes |
|
/**
* Copy imaginary numbers to the given
* ndarray
* @param arr the array to copy imaginary numbers to
*/
protected void copyImagTo(INDArray arr) {
INDArray linear = arr.linearView();
IComplexNDArray thisLinear = linearView();
if (arr.isScalar())
arr.putScalar(0, getReal(0));
else
for (int i = 0; i < linear.length(); i++) {
arr.putScalar(i, thisLinear.getImag(i));
}
}
Example 8
| Source File: BaseComplexNDArray.java From nd4j with Apache License 2.0 | 5 votes |
|
/**
* Copy real numbers to arr
* @param arr the arr to copy to
*/
protected void copyRealTo(INDArray arr) {
INDArray linear = arr.linearView();
IComplexNDArray thisLinear = linearView();
if (arr.isScalar())
arr.putScalar(0, getReal(0));
else
for (int i = 0; i < linear.length(); i++) {
arr.putScalar(i, thisLinear.getReal(i));
}
}
Example 9
| Source File: JCublasNDArrayFactory.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Override
public INDArray sort(INDArray x, boolean descending, int... dimension) {
if (x.isScalar())
return x;
Arrays.sort(dimension);
Nd4j.getExecutioner().push();
val tadBuffers = Nd4j.getExecutioner().getTADManager().getTADOnlyShapeInfo(x, dimension);
val context = AtomicAllocator.getInstance().getFlowController().prepareAction(x);
val extraz = new PointerPointer(AtomicAllocator.getInstance().getHostPointer(x.shapeInfoDataBuffer()), // not used
context.getOldStream(), AtomicAllocator.getInstance().getDeviceIdPointer());
val dimensionPointer = AtomicAllocator.getInstance()
.getHostPointer(AtomicAllocator.getInstance().getConstantBuffer(dimension));
nativeOps.sortTad(extraz,
null,
(LongPointer) x.shapeInfoDataBuffer().addressPointer(),
AtomicAllocator.getInstance().getPointer(x, context),
(LongPointer) AtomicAllocator.getInstance().getPointer(x.shapeInfoDataBuffer(), context),
(IntPointer) dimensionPointer,
dimension.length,
(LongPointer) AtomicAllocator.getInstance().getPointer(tadBuffers.getFirst(), context),
new LongPointerWrapper(AtomicAllocator.getInstance().getPointer(tadBuffers.getSecond(), context)),
descending
);
if (nativeOps.lastErrorCode() != 0)
throw new RuntimeException(nativeOps.lastErrorMessage());
AtomicAllocator.getInstance().getFlowController().registerAction(context, x);
return x;
}
Example 10
| Source File: CpuNDArrayFactory.java From nd4j with Apache License 2.0 | 5 votes |
|
@Override
public INDArray sort(INDArray x, boolean descending) {
if (x.isScalar())
return x;
if (x.data().dataType() == DataBuffer.Type.FLOAT) {
NativeOpsHolder.getInstance().getDeviceNativeOps().sortFloat(null, (FloatPointer) x.data().addressPointer(), (LongPointer) x.shapeInfoDataBuffer().addressPointer(), descending);
} else if (x.data().dataType() == DataBuffer.Type.DOUBLE) {
NativeOpsHolder.getInstance().getDeviceNativeOps().sortDouble(null, (DoublePointer) x.data().addressPointer(), (LongPointer) x.shapeInfoDataBuffer().addressPointer(), descending);
} else {
throw new UnsupportedOperationException("Unknown dataype " + x.data().dataType());
}
return x;
}
Example 11
| Source File: CpuSparseNDArrayFactory.java From nd4j with Apache License 2.0 | 5 votes |
|
@Override
public INDArray sort(INDArray x, boolean descending, int... dimension) {
if (x.isScalar())
return x;
Arrays.sort(dimension);
Pair<DataBuffer, DataBuffer> tadBuffers = Nd4j.getExecutioner().getTADManager().getTADOnlyShapeInfo(x, dimension);
if (x.data().dataType() == DataBuffer.Type.FLOAT) {
NativeOpsHolder.getInstance().getDeviceNativeOps().sortTadFloat(null,
(FloatPointer) x.data().addressPointer(),
(LongPointer) x.shapeInfoDataBuffer().addressPointer(),
new IntPointer(dimension),
dimension.length,
(LongPointer) tadBuffers.getFirst().addressPointer(),
new LongPointerWrapper(tadBuffers.getSecond().addressPointer()),
descending);
} else if (x.data().dataType() == DataBuffer.Type.DOUBLE) {
NativeOpsHolder.getInstance().getDeviceNativeOps().sortTadDouble(null,
(DoublePointer) x.data().addressPointer(),
(LongPointer) x.shapeInfoDataBuffer().addressPointer(),
new IntPointer(dimension),
dimension.length,
(LongPointer) tadBuffers.getFirst().addressPointer(),
new LongPointerWrapper(tadBuffers.getSecond().addressPointer()),
descending);
} else {
throw new UnsupportedOperationException("Unknown datatype " + x.data().dataType());
}
return x;
}
Example 12
| Source File: CpuSparseNDArrayFactory.java From nd4j with Apache License 2.0 | 5 votes |
|
@Override
public INDArray sort(INDArray x, boolean descending) {
if (x.isScalar())
return x;
if (x.data().dataType() == DataBuffer.Type.FLOAT) {
NativeOpsHolder.getInstance().getDeviceNativeOps().sortFloat(null, (FloatPointer) x.data().addressPointer(), (LongPointer) x.shapeInfoDataBuffer().addressPointer(), descending);
} else if (x.data().dataType() == DataBuffer.Type.DOUBLE) {
NativeOpsHolder.getInstance().getDeviceNativeOps().sortDouble(null, (DoublePointer) x.data().addressPointer(), (LongPointer) x.shapeInfoDataBuffer().addressPointer(), descending);
} else {
throw new UnsupportedOperationException("Unknown dataype " + x.data().dataType());
}
return x;
}
Example 13
| Source File: CoverageModelEMWorkspaceMathUtils.java From gatk-protected with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
/**
* Solves a linear system using Apache commons methods [mat].[x] = [vec]
*
* @param mat the coefficients matrix (must be square and full-rank)
* @param vec the right hand side vector
* @param singularityThreshold a threshold for detecting singularity
* @return solution of the linear system
*/
public static INDArray linsolve(@Nonnull final INDArray mat, @Nonnull final INDArray vec,
final double singularityThreshold) {
if (mat.isScalar()) {
return vec.div(mat.getDouble(0));
}
if (!mat.isSquare()) {
throw new IllegalArgumentException("invalid array: must be a square matrix");
}
final RealVector sol = new LUDecomposition(Nd4jApacheAdapterUtils.convertINDArrayToApacheMatrix(mat),
singularityThreshold).getSolver().solve(Nd4jApacheAdapterUtils.convertINDArrayToApacheVector(vec));
return Nd4j.create(sol.toArray(), vec.shape());
}
Example 14
| Source File: CoverageModelEMWorkspaceMathUtils.java From gatk-protected with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
/**
* Calculates log abs determinant of a matrix via LU decomposition.
*
* @param mat a square matrix
* @return log abs determinant of {@code mat}
*/
public static double logdet(@Nonnull final INDArray mat) {
if (mat.isScalar()) {
return FastMath.log(FastMath.abs(mat.getDouble(0)));
}
if (!mat.isSquare()) {
throw new IllegalArgumentException("Invalid array: must be square matrix");
}
final LUDecomposition decomp = new LUDecomposition(Nd4jApacheAdapterUtils.convertINDArrayToApacheMatrix(mat),
DEFAULT_LU_DECOMPOSITION_SINGULARITY_THRESHOLD);
final double[] diagL = diag(decomp.getL());
final double[] diagU = diag(decomp.getU());
return Arrays.stream(diagL).map(FastMath::abs).map(FastMath::log).sum() +
Arrays.stream(diagU).map(FastMath::abs).map(FastMath::log).sum();
}
Example 15
| Source File: CpuNDArrayFactory.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Override
public INDArray sort(INDArray x, boolean descending) {
if (x.isScalar())
return x;
NativeOpsHolder.getInstance().getDeviceNativeOps().sort(null,
x.data().addressPointer(), (LongPointer) x.shapeInfoDataBuffer().addressPointer(),
null, null,
descending);
return x;
}
Example 16
| Source File: NativeOpExecutioner.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
public INDArray exec(IndexAccumulation op, OpContext oc) {
checkForCompression(op);
INDArray x = getX(op, oc);
INDArray z = getZ(op, oc);
if (extraz.get() == null)
extraz.set(new PointerPointer(32));
val dimension = Shape.normalizeAxis(x.rank(), op.dimensions().toIntVector());
if (x.isEmpty()) {
for (val d:dimension) {
Preconditions.checkArgument(x.shape()[d] != 0, "IndexReduce can't be issued along axis with 0 in shape");
}
}
boolean keepDims = op.isKeepDims();
long[] retShape = Shape.reductionShape(x, dimension, true, keepDims);
if(z == null || x == z) {
val ret = Nd4j.createUninitialized(DataType.LONG, retShape);
setZ(ret, op, oc);
z = ret;
} else if(!Arrays.equals(retShape, z.shape())){
throw new IllegalStateException("Z array shape does not match expected return type for op " + op
+ ": expected shape " + Arrays.toString(retShape) + ", z.shape()=" + Arrays.toString(z.shape()));
}
op.validateDataTypes();
Pointer dimensionAddress = constantHandler.getConstantBuffer(dimension, DataType.INT).addressPointer();
Pair<DataBuffer, DataBuffer> tadBuffers = tadManager.getTADOnlyShapeInfo(x, dimension);
Pointer hostTadShapeInfo = tadBuffers.getFirst().addressPointer();
DataBuffer offsets = tadBuffers.getSecond();
Pointer hostTadOffsets = offsets == null ? null : offsets.addressPointer();
PointerPointer dummy = extraz.get().put(hostTadShapeInfo, hostTadOffsets);
long st = profilingConfigurableHookIn(op, tadBuffers.getFirst());
val xb = ((BaseCpuDataBuffer) x.data()).getOpaqueDataBuffer();
val zb = ((BaseCpuDataBuffer) z.data()).getOpaqueDataBuffer();
if (z.isScalar()) {
loop.execIndexReduceScalar(dummy, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, x.dataType()),
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null);
} else {
loop.execIndexReduce(dummy, op.opNum(),
xb, (LongPointer) x.shapeInfoDataBuffer().addressPointer(), null,
getPointerForExtraArgs(op, x.dataType()),
zb, (LongPointer) z.shapeInfoDataBuffer().addressPointer(), null,
((BaseCpuDataBuffer) op.dimensions().data()).getOpaqueDataBuffer(), (LongPointer) op.dimensions().shapeInfoDataBuffer().addressPointer(), null);
}
if (loop.lastErrorCode() != 0)
throw new RuntimeException(loop.lastErrorMessage());
profilingConfigurableHookOut(op, oc, st);
return getZ(op, oc);
}
Example 17
| Source File: JCublasNDArrayFactory.java From nd4j with Apache License 2.0 | 4 votes |
|
@Override
public INDArray sort(INDArray x, boolean descending, int... dimension) {
if (x.isScalar())
return x;
Arrays.sort(dimension);
Nd4j.getExecutioner().push();
Pair<DataBuffer, DataBuffer> tadBuffers = Nd4j.getExecutioner().getTADManager().getTADOnlyShapeInfo(x, dimension);
CudaContext context = AtomicAllocator.getInstance().getFlowController().prepareAction(x);
PointerPointer extraz = new PointerPointer(AtomicAllocator.getInstance().getHostPointer(x.shapeInfoDataBuffer()), // not used
context.getOldStream(), AtomicAllocator.getInstance().getDeviceIdPointer());
Pointer dimensionPointer = AtomicAllocator.getInstance()
.getPointer(AtomicAllocator.getInstance().getConstantBuffer(dimension), context);
if (x.data().dataType() == DataBuffer.Type.FLOAT) {
nativeOps.sortTadFloat(extraz,
(FloatPointer) AtomicAllocator.getInstance().getPointer(x, context),
(LongPointer) AtomicAllocator.getInstance().getPointer(x.shapeInfoDataBuffer(), context),
(IntPointer) dimensionPointer,
dimension.length,
(LongPointer) AtomicAllocator.getInstance().getPointer(tadBuffers.getFirst(), context),
new LongPointerWrapper(AtomicAllocator.getInstance().getPointer(tadBuffers.getSecond(), context)),
descending
);
} else if (x.data().dataType() == DataBuffer.Type.DOUBLE) {
nativeOps.sortTadDouble(extraz,
(DoublePointer) AtomicAllocator.getInstance().getPointer(x, context),
(LongPointer) AtomicAllocator.getInstance().getPointer(x.shapeInfoDataBuffer(), context),
(IntPointer) dimensionPointer,
dimension.length,
(LongPointer) AtomicAllocator.getInstance().getPointer(tadBuffers.getFirst(), context),
new LongPointerWrapper(AtomicAllocator.getInstance().getPointer(tadBuffers.getSecond(), context)),
descending
);
} else if (x.data().dataType() == DataBuffer.Type.HALF) {
nativeOps.sortTadHalf(extraz,
(ShortPointer) AtomicAllocator.getInstance().getPointer(x, context),
(LongPointer) AtomicAllocator.getInstance().getPointer(x.shapeInfoDataBuffer(), context),
(IntPointer) dimensionPointer,
dimension.length,
(LongPointer) AtomicAllocator.getInstance().getPointer(tadBuffers.getFirst(), context),
new LongPointerWrapper(AtomicAllocator.getInstance().getPointer(tadBuffers.getSecond(), context)),
descending
);
} else {
throw new UnsupportedOperationException("Unknown dataType " + x.data().dataType());
}
AtomicAllocator.getInstance().getFlowController().registerAction(context, x);
return x;
}
Example 18
| Source File: JCublasNDArrayFactory.java From nd4j with Apache License 2.0 | 4 votes |
|
@Override
public INDArray sort(INDArray x, boolean descending) {
if (x.isScalar())
return x;
Nd4j.getExecutioner().push();
CudaContext context = AtomicAllocator.getInstance().getFlowController().prepareAction(x);
Pointer ptr = AtomicAllocator.getInstance().getHostPointer(x.shapeInfoDataBuffer());
PointerPointer extraz = new PointerPointer(ptr, // 0
context.getOldStream(), // 1
AtomicAllocator.getInstance().getDeviceIdPointer(), // 2
context.getBufferAllocation(), // 3
context.getBufferReduction(), // 4
context.getBufferScalar(), // 5
context.getBufferSpecial(), // 6
ptr, // 7
AtomicAllocator.getInstance().getHostPointer(x.shapeInfoDataBuffer()), // 8
ptr, // 9
ptr, // 10
ptr, // 11
ptr, // 12
ptr, // 13
ptr, // 14
ptr, // special pointer for IsMax // 15
ptr, // special pointer for IsMax // 16
ptr, // special pointer for IsMax // 17
new CudaPointer(0));
// we're sending > 10m elements to radixSort
boolean isRadix = !x.isView() && (x.lengthLong() > 1024 * 1024 * 10);
INDArray tmpX = x;
// we need to guarantee all threads are finished here
if (isRadix)
Nd4j.getExecutioner().commit();
if (x.data().dataType() == DataBuffer.Type.FLOAT) {
nativeOps.sortFloat(extraz,
(FloatPointer) AtomicAllocator.getInstance().getPointer(tmpX, context),
(LongPointer) AtomicAllocator.getInstance().getPointer(tmpX.shapeInfoDataBuffer(), context),
descending
);
} else if (x.data().dataType() == DataBuffer.Type.DOUBLE) {
nativeOps.sortDouble(extraz,
(DoublePointer) AtomicAllocator.getInstance().getPointer(tmpX, context),
(LongPointer) AtomicAllocator.getInstance().getPointer(tmpX.shapeInfoDataBuffer(), context),
descending
);
} else if (x.data().dataType() == DataBuffer.Type.HALF) {
nativeOps.sortHalf(extraz,
(ShortPointer) AtomicAllocator.getInstance().getPointer(tmpX, context),
(LongPointer) AtomicAllocator.getInstance().getPointer(tmpX.shapeInfoDataBuffer(), context),
descending
);
} else {
throw new UnsupportedOperationException("Unknown dataType " + x.data().dataType());
}
AtomicAllocator.getInstance().getFlowController().registerAction(context, x);
return x;
}
Example 19
| Source File: BaseComplexNDArray.java From nd4j with Apache License 2.0 | 4 votes |
|
/**
* Perform an copy matrix multiplication
*
* @param other the other matrix to perform matrix multiply with
* @param result the result ndarray
* @return the result of the matrix multiplication
*/
@Override
public IComplexNDArray mmuli(INDArray other, INDArray result) {
IComplexNDArray otherArray = (IComplexNDArray) other;
IComplexNDArray resultArray = (IComplexNDArray) result;
if (other.shape().length > 2) {
for (int i = 0; i < other.slices(); i++) {
resultArray.putSlice(i, slice(i).mmul(otherArray.slice(i)));
}
return resultArray;
}
LinAlgExceptions.assertMultiplies(this, other);
if (other.isScalar()) {
return muli(otherArray.getComplex(0), resultArray);
}
if (isScalar()) {
return otherArray.muli(getComplex(0), resultArray);
}
/* check sizes and resize if necessary */
//assertMultipliesWith(other);
if (result == this || result == other) {
/* actually, blas cannot do multiplications in-place. Therefore, we will fake by
* allocating a temporary object on the side and copy the result later.
*/
IComplexNDArray temp = Nd4j.createComplex(resultArray.shape());
if (otherArray.columns() == 1) {
Nd4j.getBlasWrapper().level2().gemv(BlasBufferUtil.getCharForTranspose(temp),
BlasBufferUtil.getCharForTranspose(this), Nd4j.UNIT, this, otherArray, Nd4j.ZERO, temp);
} else {
Nd4j.getBlasWrapper().level3().gemm(BlasBufferUtil.getCharForTranspose(temp),
BlasBufferUtil.getCharForTranspose(this), BlasBufferUtil.getCharForTranspose(other),
Nd4j.UNIT, this, otherArray, Nd4j.ZERO, temp);
}
Nd4j.getBlasWrapper().copy(temp, resultArray);
} else {
if (otherArray.columns() == 1) {
Nd4j.getBlasWrapper().level2().gemv(BlasBufferUtil.getCharForTranspose(resultArray),
BlasBufferUtil.getCharForTranspose(this), Nd4j.UNIT, this, otherArray, Nd4j.ZERO,
resultArray);
}
else {
Nd4j.getBlasWrapper().level3().gemm(BlasBufferUtil.getCharForTranspose(resultArray),
BlasBufferUtil.getCharForTranspose(this), BlasBufferUtil.getCharForTranspose(other),
Nd4j.UNIT, this, otherArray, Nd4j.ZERO, resultArray);
}
}
return resultArray;
}
Example 20
| Source File: MtcnnService.java From mtcnn-java with Apache License 2.0 | 4 votes |
|
/**
* STAGE 2
*
* @param image
* @param totalBoxes
* @param padResult
* @return
* @throws IOException
*/
private INDArray refinementStage(INDArray image, INDArray totalBoxes, MtcnnUtil.PadResult padResult) throws IOException {
// num_boxes = total_boxes.shape[0]
int numBoxes = totalBoxes.isEmpty() ? 0 : (int) totalBoxes.shape()[0];
// if num_boxes == 0:
// return total_boxes, stage_status
if (numBoxes == 0) {
return totalBoxes;
}
INDArray tempImg1 = computeTempImage(image, numBoxes, padResult, 24);
//this.refineNetGraph.associateArrayWithVariable(tempImg1, this.refineNetGraph.variableMap().get("rnet/input"));
//List<DifferentialFunction> refineNetResults = this.refineNetGraph.exec().getRight();
//INDArray out0 = refineNetResults.stream().filter(df -> df.getOwnName().equalsIgnoreCase("rnet/fc2-2/fc2-2"))
// .findFirst().get().outputVariable().getArr();
//INDArray out1 = refineNetResults.stream().filter(df -> df.getOwnName().equalsIgnoreCase("rnet/prob1"))
// .findFirst().get().outputVariable().getArr();
Map<String, INDArray> resultMap = this.refineNetGraphRunner.run(Collections.singletonMap("rnet/input", tempImg1));
//INDArray out0 = resultMap.get("rnet/fc2-2/fc2-2"); // for ipazc/mtcnn model
INDArray out0 = resultMap.get("rnet/conv5-2/conv5-2");
INDArray out1 = resultMap.get("rnet/prob1");
// score = out1[1, :]
INDArray score = out1.get(all(), point(1)).transposei();
// ipass = np.where(score > self.__steps_threshold[1])
INDArray ipass = MtcnnUtil.getIndexWhereVector(score.transpose(), s -> s > stepsThreshold[1]);
//INDArray ipass = MtcnnUtil.getIndexWhereVector2(score.transpose(), Conditions.greaterThan(stepsThreshold[1]));
if (ipass.isEmpty()) {
totalBoxes = Nd4j.empty();
return totalBoxes;
}
// total_boxes = np.hstack([total_boxes[ipass[0], 0:4].copy(), np.expand_dims(score[ipass].copy(), 1)])
INDArray b1 = totalBoxes.get(new SpecifiedIndex(ipass.toLongVector()), interval(0, 4));
INDArray b2 = ipass.isScalar() ? score.get(ipass).reshape(1, 1)
: Nd4j.expandDims(score.get(ipass), 1);
totalBoxes = Nd4j.hstack(b1, b2);
// mv = out0[:, ipass[0]]
INDArray mv = out0.get(new SpecifiedIndex(ipass.toLongVector()), all()).transposei();
// if total_boxes.shape[0] > 0:
if (!totalBoxes.isEmpty() && totalBoxes.shape()[0] > 0) {
// pick = self.__nms(total_boxes, 0.7, 'Union')
INDArray pick = MtcnnUtil.nonMaxSuppression(totalBoxes.dup(), 0.7, MtcnnUtil.NonMaxSuppressionType.Union).transpose();
// total_boxes = total_boxes[pick, :]
totalBoxes = totalBoxes.get(new SpecifiedIndex(pick.toLongVector()), all());
// total_boxes = self.__bbreg(total_boxes.copy(), np.transpose(mv[:, pick]))
totalBoxes = MtcnnUtil.bbreg(totalBoxes, mv.get(all(), new SpecifiedIndex(pick.toLongVector())).transpose());
// total_boxes = self.__rerec(total_boxes.copy())
totalBoxes = MtcnnUtil.rerec(totalBoxes, false);
}
return totalBoxes;
}
