static void scan_dim_launcher(Param &out,
Param &tmp,
const Param &in,
const uint groups_all[4])
{
try {
Kernel* ker = get_scan_dim_kernels<Ti, To, op, dim, isFinalPass, threads_y>(0);
NDRange local(THREADS_X, threads_y);
NDRange global(groups_all[0] * groups_all[2] * local[0],
groups_all[1] * groups_all[3] * local[1]);
uint lim = divup(out.info.dims[dim], (threads_y * groups_all[dim]));
auto scanOp = make_kernel<Buffer, KParam,
Buffer, KParam,
Buffer, KParam,
uint, uint,
uint, uint>(*ker);
scanOp(EnqueueArgs(getQueue(), global, local),
*out.data, out.info, *tmp.data, tmp.info, *in.data, in.info,
groups_all[0], groups_all[1], groups_all[dim], lim);
CL_DEBUG_FINISH(getQueue());
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
void convolve2(Param out, const Param signal, const Param filter)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static std::map<int, Program*> convProgs;
static std::map<int, Kernel*> convKernels;
int device = getActiveDeviceId();
std::call_once( compileFlags[device], [device] () {
const size_t C0_SIZE = (THREADS_X+2*(fLen-1))* THREADS_Y;
const size_t C1_SIZE = (THREADS_Y+2*(fLen-1))* THREADS_X;
size_t locSize = (conv_dim==0 ? C0_SIZE : C1_SIZE);
std::ostringstream options;
options << " -D T=" << dtype_traits<T>::getName()
<< " -D accType="<< dtype_traits<accType>::getName()
<< " -D CONV_DIM="<< conv_dim
<< " -D EXPAND="<< expand
<< " -D FLEN="<< fLen
<< " -D LOCAL_MEM_SIZE="<<locSize;
if (std::is_same<T, double>::value ||
std::is_same<T, cdouble>::value) {
options << " -D USE_DOUBLE";
}
Program prog;
buildProgram(prog, convolve_separable_cl, convolve_separable_cl_len, options.str());
convProgs[device] = new Program(prog);
convKernels[device] = new Kernel(*convProgs[device], "convolve");
});
auto convOp = make_kernel<Buffer, KParam, Buffer, KParam, Buffer,
int, int>(*convKernels[device]);
NDRange local(THREADS_X, THREADS_Y);
int blk_x = divup(out.info.dims[0], THREADS_X);
int blk_y = divup(out.info.dims[1], THREADS_Y);
NDRange global(blk_x*signal.info.dims[2]*THREADS_X,
blk_y*signal.info.dims[3]*THREADS_Y);
cl::Buffer *mBuff = bufferAlloc(fLen*sizeof(accType));
// FIX ME: if the filter array is strided, direct might cause issues
getQueue().enqueueCopyBuffer(*filter.data, *mBuff, 0, 0, fLen*sizeof(accType));
convOp(EnqueueArgs(getQueue(), global, local),
*out.data, out.info, *signal.data, signal.info, *mBuff, blk_x, blk_y);
bufferFree(mBuff);
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
void memcopy(cl::Buffer out, const dim_t *ostrides,
const cl::Buffer in, const dim_t *idims,
const dim_t *istrides, int offset, uint ndims)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static std::map<int, Program*> cpyProgs;
static std::map<int, Kernel*> cpyKernels;
int device = getActiveDeviceId();
std::call_once(compileFlags[device], [&]() {
std::ostringstream options;
options << " -D T=" << dtype_traits<T>::getName();
if (std::is_same<T, double>::value ||
std::is_same<T, cdouble>::value) {
options << " -D USE_DOUBLE";
}
Program prog;
buildProgram(prog, memcopy_cl, memcopy_cl_len, options.str());
cpyProgs[device] = new Program(prog);
cpyKernels[device] = new Kernel(*cpyProgs[device], "memcopy_kernel");
});
dims_t _ostrides = {{ostrides[0], ostrides[1], ostrides[2], ostrides[3]}};
dims_t _istrides = {{istrides[0], istrides[1], istrides[2], istrides[3]}};
dims_t _idims = {{idims[0], idims[1], idims[2], idims[3]}};
size_t local_size[2] = {DIM0, DIM1};
if (ndims == 1) {
local_size[0] *= local_size[1];
local_size[1] = 1;
}
int groups_0 = divup(idims[0], local_size[0]);
int groups_1 = divup(idims[1], local_size[1]);
NDRange local(local_size[0], local_size[1]);
NDRange global(groups_0 * idims[2] * local_size[0],
groups_1 * idims[3] * local_size[1]);
auto memcopy_kernel = KernelFunctor< Buffer, dims_t,
Buffer, dims_t,
dims_t, int,
int, int >(*cpyKernels[device]);
memcopy_kernel(EnqueueArgs(getQueue(), global, local),
out, _ostrides, in, _idims, _istrides, offset, groups_0, groups_1);
CL_DEBUG_FINISH(getQueue());
}
catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
void matchTemplate(Param out, const Param srch, const Param tmplt)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static std::map<int, Program*> mtProgs;
static std::map<int, Kernel*> mtKernels;
int device = getActiveDeviceId();
std::call_once( compileFlags[device], [device] () {
std::ostringstream options;
options << " -D inType=" << dtype_traits<inType>::getName()
<< " -D outType=" << dtype_traits<outType>::getName()
<< " -D MATCH_T=" << mType
<< " -D NEEDMEAN="<< needMean
<< " -D AF_SAD=" << AF_SAD
<< " -D AF_ZSAD=" << AF_ZSAD
<< " -D AF_LSAD=" << AF_LSAD
<< " -D AF_SSD=" << AF_SSD
<< " -D AF_ZSSD=" << AF_ZSSD
<< " -D AF_LSSD=" << AF_LSSD
<< " -D AF_NCC=" << AF_NCC
<< " -D AF_ZNCC=" << AF_ZNCC
<< " -D AF_SHD=" << AF_SHD;
if (std::is_same<outType, double>::value) {
options << " -D USE_DOUBLE";
}
Program prog;
buildProgram(prog, matchTemplate_cl, matchTemplate_cl_len, options.str());
mtProgs[device] = new Program(prog);
mtKernels[device] = new Kernel(*mtProgs[device], "matchTemplate");
});
NDRange local(THREADS_X, THREADS_Y);
int blk_x = divup(srch.info.dims[0], THREADS_X);
int blk_y = divup(srch.info.dims[1], THREADS_Y);
NDRange global(blk_x * srch.info.dims[2] * THREADS_X, blk_y * srch.info.dims[3] * THREADS_Y);
auto matchImgOp = make_kernel<Buffer, KParam,
Buffer, KParam,
Buffer, KParam,
int, int> (*mtKernels[device]);
matchImgOp(EnqueueArgs(getQueue(), global, local),
*out.data, out.info, *srch.data, srch.info, *tmplt.data, tmplt.info, blk_x, blk_y);
CL_DEBUG_FINISH(getQueue());
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
void histogram(Param out, const Param in, const Param minmax, dim_type nbins)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static std::map<int, Program*> histProgs;
static std::map<int, Kernel *> histKernels;
int device = getActiveDeviceId();
std::call_once( compileFlags[device], [device] () {
std::ostringstream options;
options << " -D inType=" << dtype_traits<inType>::getName()
<< " -D outType=" << dtype_traits<outType>::getName()
<< " -D THRD_LOAD=" << THRD_LOAD;
if (std::is_same<inType, double>::value ||
std::is_same<inType, cdouble>::value) {
options << " -D USE_DOUBLE";
}
Program prog;
buildProgram(prog, histogram_cl, histogram_cl_len, options.str());
histProgs[device] = new Program(prog);
histKernels[device] = new Kernel(*histProgs[device], "histogram");
});
auto histogramOp = make_kernel<Buffer, KParam, Buffer, KParam,
Buffer, cl::LocalSpaceArg,
dim_type, dim_type, dim_type
>(*histKernels[device]);
NDRange local(THREADS_X, 1);
dim_type numElements = in.info.dims[0]*in.info.dims[1];
dim_type blk_x = divup(numElements, THRD_LOAD*THREADS_X);
dim_type batchCount = in.info.dims[2];
NDRange global(blk_x*THREADS_X, batchCount);
dim_type locSize = nbins * sizeof(outType);
histogramOp(EnqueueArgs(getQueue(), global, local),
*out.data, out.info, *in.data, in.info, *minmax.data,
cl::Local(locSize), numElements, nbins, blk_x);
CL_DEBUG_FINISH(getQueue());
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
void gradient(Param grad0, Param grad1, const Param in)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static std::map<int, Program*> gradProgs;
static std::map<int, Kernel*> gradKernels;
int device = getActiveDeviceId();
std::call_once( compileFlags[device], [device] () {
std::ostringstream options;
options << " -D T=" << dtype_traits<T>::getName()
<< " -D TX=" << TX
<< " -D TY=" << TY;
if((af_dtype) dtype_traits<T>::af_type == c32 ||
(af_dtype) dtype_traits<T>::af_type == c64) {
options << " -D CPLX=1";
} else {
options << " -D CPLX=0";
}
if (std::is_same<T, double>::value ||
std::is_same<T, cdouble>::value) {
options << " -D USE_DOUBLE";
}
Program prog;
buildProgram(prog, gradient_cl, gradient_cl_len, options.str());
gradProgs[device] = new Program(prog);
gradKernels[device] = new Kernel(*gradProgs[device], "gradient_kernel");
});
auto gradOp = make_kernel<Buffer, const KParam, Buffer, const KParam,
const Buffer, const KParam, const dim_type, const dim_type>
(*gradKernels[device]);
NDRange local(TX, TY, 1);
dim_type blocksPerMatX = divup(in.info.dims[0], TX);
dim_type blocksPerMatY = divup(in.info.dims[1], TY);
NDRange global(local[0] * blocksPerMatX * in.info.dims[2],
local[1] * blocksPerMatY * in.info.dims[3],
1);
gradOp(EnqueueArgs(getQueue(), global, local),
*grad0.data, grad0.info, *grad1.data, grad1.info,
*in.data, in.info, blocksPerMatX, blocksPerMatY);
CL_DEBUG_FINISH(getQueue());
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
void transpose(Param out, const Param in)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static std::map<int, Program*> trsProgs;
static std::map<int, Kernel*> trsKernels;
int device = getActiveDeviceId();
std::call_once(compileFlags[device], [device] () {
std::ostringstream options;
options << " -D TILE_DIM=" << TILE_DIM
<< " -D THREADS_Y=" << THREADS_Y
<< " -D IS32MULTIPLE=" << IS32MULTIPLE
<< " -D DOCONJUGATE=" << (conjugate && af::iscplx<T>())
<< " -D T=" << dtype_traits<T>::getName();
if (std::is_same<T, double>::value ||
std::is_same<T, cdouble>::value) {
options << " -D USE_DOUBLE";
}
cl::Program prog;
buildProgram(prog, transpose_cl, transpose_cl_len, options.str());
trsProgs[device] = new Program(prog);
trsKernels[device] = new Kernel(*trsProgs[device], "transpose");
});
NDRange local(THREADS_X, THREADS_Y);
dim_type blk_x = divup(in.info.dims[0], TILE_DIM);
dim_type blk_y = divup(in.info.dims[1], TILE_DIM);
// launch batch * blk_x blocks along x dimension
NDRange global(blk_x * local[0] * in.info.dims[2],
blk_y * local[1]);
auto transposeOp = make_kernel<Buffer, const KParam,
const Buffer, const KParam,
const dim_type> (*trsKernels[device]);
transposeOp(EnqueueArgs(getQueue(), global, local),
*out.data, out.info, *in.data, in.info, blk_x);
CL_DEBUG_FINISH(getQueue());
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
void diff(Param out, const Param in, const unsigned indims)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static std::map<int, Program*> diffProgs;
static std::map<int, Kernel*> diffKernels;
int device = getActiveDeviceId();
std::call_once( compileFlags[device], [device] () {
std::ostringstream options;
options << " -D T=" << dtype_traits<T>::getName()
<< " -D DIM=" << dim
<< " -D isDiff2=" << isDiff2;
if (std::is_same<T, double>::value ||
std::is_same<T, cdouble>::value) {
options << " -D USE_DOUBLE";
}
Program prog;
buildProgram(prog, diff_cl, diff_cl_len, options.str());
diffProgs[device] = new Program(prog);
diffKernels[device] = new Kernel(*diffProgs[device], "diff_kernel");
});
auto diffOp = make_kernel<Buffer, const Buffer, const KParam, const KParam,
const dim_type, const dim_type, const dim_type>
(*diffKernels[device]);
NDRange local(TX, TY, 1);
if(dim == 0 && indims == 1) {
local = NDRange(TX * TY, 1, 1);
}
dim_type blocksPerMatX = divup(out.info.dims[0], local[0]);
dim_type blocksPerMatY = divup(out.info.dims[1], local[1]);
NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
local[1] * blocksPerMatY * out.info.dims[3],
1);
const dim_type oElem = out.info.dims[0] * out.info.dims[1]
* out.info.dims[2] * out.info.dims[3];
diffOp(EnqueueArgs(getQueue(), global, local),
*out.data, *in.data, out.info, in.info,
oElem, blocksPerMatX, blocksPerMatY);
CL_DEBUG_FINISH(getQueue());
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
void join(Param out, const Param X, const Param Y)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static std::map<int, Program*> joinProgs;
static std::map<int, Kernel *> joinKernels;
int device = getActiveDeviceId();
std::call_once( compileFlags[device], [device] () {
std::ostringstream options;
options << " -D Tx=" << dtype_traits<Tx>::getName()
<< " -D Ty=" << dtype_traits<Ty>::getName()
<< " -D dim=" << dim;
if (std::is_same<Tx, double>::value ||
std::is_same<Tx, cdouble>::value) {
options << " -D USE_DOUBLE";
} else if (std::is_same<Tx, double>::value ||
std::is_same<Tx, cdouble>::value) {
options << " -D USE_DOUBLE";
}
Program prog;
buildProgram(prog, join_cl, join_cl_len, options.str());
joinProgs[device] = new Program(prog);
joinKernels[device] = new Kernel(*joinProgs[device], "join_kernel");
});
auto joinOp = make_kernel<Buffer, const KParam, const Buffer, const KParam,
const Buffer, const KParam, const dim_type, const dim_type> (*joinKernels[device]);
NDRange local(TX, TY, 1);
dim_type blocksPerMatX = divup(out.info.dims[0], TILEX);
dim_type blocksPerMatY = divup(out.info.dims[1], TILEY);
NDRange global(local[0] * blocksPerMatX * out.info.dims[2],
local[1] * blocksPerMatY * out.info.dims[3],
1);
joinOp(EnqueueArgs(getQueue(), global, local), *out.data, out.info,
*X.data, X.info, *Y.data, Y.info, blocksPerMatX, blocksPerMatY);
CL_DEBUG_FINISH(getQueue());
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
static Kernel* get_scan_dim_kernels(int kerIdx)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static std::map<int, Program*> scanProgs;
static std::map<int, Kernel*> scanKerns;
static std::map<int, Kernel*> bcastKerns;
int device= getActiveDeviceId();
std::call_once(compileFlags[device], [device] () {
Binary<To, op> scan;
ToNum<To> toNum;
std::ostringstream options;
options << " -D To=" << dtype_traits<To>::getName()
<< " -D Ti=" << dtype_traits<Ti>::getName()
<< " -D T=To"
<< " -D dim=" << dim
<< " -D DIMY=" << threads_y
<< " -D THREADS_X=" << THREADS_X
<< " -D init=" << toNum(scan.init())
<< " -D " << binOpName<op>()
<< " -D CPLX=" << af::iscplx<Ti>()
<< " -D isFinalPass="******" -D USE_DOUBLE";
}
const char *ker_strs[] = {ops_cl, scan_dim_cl};
const int ker_lens[] = {ops_cl_len, scan_dim_cl_len};
cl::Program prog;
buildProgram(prog, 2, ker_strs, ker_lens, options.str());
scanProgs[device] = new Program(prog);
scanKerns[device] = new Kernel(*scanProgs[device], "scan_dim_kernel");
bcastKerns[device] = new Kernel(*scanProgs[device], "bcast_dim_kernel");
});
return (kerIdx == 0) ? scanKerns[device] : bcastKerns[device];
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
Array<T> cholesky(int *info, const Array<T> &in, const bool is_upper)
{
try {
Array<T> out = copyArray<T>(in);
*info = cholesky_inplace(out, is_upper);
if (is_upper) triangle<T, true , false>(out, out);
else triangle<T, false, false>(out, out);
return out;
} catch (cl::Error &err) {
CL_TO_AF_ERROR(err);
}
}
void random(cl::Buffer out, dim_type elements)
{
try {
static unsigned counter;
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static Program ranProgs[DeviceManager::MAX_DEVICES];
static Kernel ranKernels[DeviceManager::MAX_DEVICES];
int device = getActiveDeviceId();
std::call_once( compileFlags[device], [device] () {
Program::Sources setSrc;
setSrc.emplace_back(random_cl, random_cl_len);
std::ostringstream options;
options << " -D T=" << dtype_traits<T>::getName()
<< " -D repeat="<< REPEAT
<< " -D " << random_name<T, isRandu>().name();
if (std::is_same<T, double>::value) {
options << " -D USE_DOUBLE";
options << " -D IS_64";
}
if (std::is_same<T, char>::value) {
options << " -D IS_BOOL";
}
buildProgram(ranProgs[device], random_cl, random_cl_len, options.str());
ranKernels[device] = Kernel(ranProgs[device], "random");
});
auto randomOp = make_kernel<cl::Buffer, uint, uint, uint, uint>(ranKernels[device]);
uint groups = divup(elements, THREADS * REPEAT);
counter += divup(elements, THREADS * groups);
NDRange local(THREADS, 1);
NDRange global(THREADS * groups, 1);
randomOp(EnqueueArgs(getQueue(), global, local),
out, elements, counter, random_seed[0], random_seed[1]);
} catch(cl::Error ex) {
CL_TO_AF_ERROR(ex);
}
}
void select(Param out, Param cond, Param a, Param b, int ndims)
{
try {
bool is_same = true;
for (int i = 0; i < 4; i++) {
is_same &= (a.info.dims[i] == b.info.dims[i]);
}
if (is_same) {
select_launcher<T, true >(out, cond, a, b, ndims);
} else {
select_launcher<T, false>(out, cond, a, b, ndims);
}
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
}
}
void transpose(Param out, const Param in)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static Program trsProgs[DeviceManager::MAX_DEVICES];
static Kernel trsKernels[DeviceManager::MAX_DEVICES];
int device = getActiveDeviceId();
std::call_once( compileFlags[device], [device] () {
std::ostringstream options;
options << " -D T=" << dtype_traits<T>::getName()
<< " -D TILE_DIM=" << TILE_DIM;
buildProgram(trsProgs[device],
transpose_cl,
transpose_cl_len,
options.str());
trsKernels[device] = Kernel(trsProgs[device], "transpose");
});
NDRange local(THREADS_X, THREADS_Y);
dim_type blk_x = divup(in.info.dims[0], TILE_DIM);
dim_type blk_y = divup(in.info.dims[1], TILE_DIM);
// launch batch * blk_x blocks along x dimension
NDRange global(blk_x * TILE_DIM * in.info.dims[2], blk_y * TILE_DIM);
auto transposeOp = make_kernel<Buffer, KParam,
Buffer, KParam,
dim_type> (trsKernels[device]);
transposeOp(EnqueueArgs(getQueue(), global, local),
out.data, out.info, in.data, in.info, blk_x);
CL_DEBUG_FINISH(getQueue());
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
void index(Param out, const Param in, const IndexKernelParam_t& p, Buffer *bPtr[4])
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static std::map<int, Program*> idxProgs;
static std::map<int, Kernel*> idxKernels;
int device = getActiveDeviceId();
std::call_once( compileFlags[device], [device] () {
std::ostringstream options;
options << " -D T=" << dtype_traits<T>::getName();
if (std::is_same<T, double>::value ||
std::is_same<T, cdouble>::value) {
options << " -D USE_DOUBLE";
}
Program prog;
buildProgram(prog, index_cl, index_cl_len, options.str());
idxProgs[device] = new Program(prog);
idxKernels[device] = new Kernel(*idxProgs[device], "indexKernel");
});
NDRange local(THREADS_X, THREADS_Y);
int blk_x = divup(out.info.dims[0], THREADS_X);
int blk_y = divup(out.info.dims[1], THREADS_Y);
NDRange global(blk_x * out.info.dims[2] * THREADS_X,
blk_y * out.info.dims[3] * THREADS_Y);
auto indexOp = make_kernel<Buffer, KParam, Buffer, KParam, IndexKernelParam_t,
Buffer, Buffer, Buffer, Buffer, int, int>(*idxKernels[device]);
indexOp(EnqueueArgs(getQueue(), global, local),
*out.data, out.info, *in.data, in.info, p,
*bPtr[0], *bPtr[1], *bPtr[2], *bPtr[3], blk_x, blk_y);
CL_DEBUG_FINISH(getQueue());
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
void transform(Param out, const Param in, const Param tf)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static Program transformProgs[DeviceManager::MAX_DEVICES];
static Kernel transformKernels[DeviceManager::MAX_DEVICES];
int device = getActiveDeviceId();
std::call_once( compileFlags[device], [device] () {
std::ostringstream options;
options << " -D T=" << dtype_traits<T>::getName()
<< " -D INVERSE=" << (isInverse ? 1 : 0);
buildProgram(transformProgs[device],
transform_cl,
transform_cl_len,
options.str());
transformKernels[device] = Kernel(transformProgs[device], "transform_kernel");
});
auto transformOp = make_kernel<Buffer, const KParam,
const Buffer, const KParam, const Buffer, const KParam,
const dim_type, const dim_type>
(transformKernels[device]);
const dim_type nimages = in.info.dims[2];
// Multiplied in src/backend/transform.cpp
const dim_type ntransforms = out.info.dims[2] / in.info.dims[2];
NDRange local(TX, TY, 1);
NDRange global(local[0] * divup(out.info.dims[0], local[0]) * nimages,
local[1] * divup(out.info.dims[1], local[1]) * ntransforms,
1);
transformOp(EnqueueArgs(getQueue(), global, local),
out.data, out.info, in.data, in.info, tf.data, tf.info, nimages, ntransforms);
CL_DEBUG_FINISH(getQueue());
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
Array<T> solve(const Array<T> &a, const Array<T> &b, const af_mat_prop options)
{
try {
initBlas();
if (options & AF_MAT_UPPER ||
options & AF_MAT_LOWER) {
return triangleSolve<T>(a, b, options);
}
if(a.dims()[0] == a.dims()[1]) {
return generalSolve<T>(a, b);
} else {
return leastSquares<T>(a, b);
}
} catch(cl::Error &err) {
CL_TO_AF_ERROR(err);
}
}
void sort0_by_key(Param okey, Param oval)
{
try {
compute::command_queue c_queue(getQueue()());
compute::buffer okey_buf((*okey.data)());
compute::buffer oval_buf((*oval.data)());
for(dim_type w = 0; w < okey.info.dims[3]; w++) {
dim_type okeyW = w * okey.info.strides[3];
dim_type ovalW = w * oval.info.strides[3];
for(dim_type z = 0; z < okey.info.dims[2]; z++) {
dim_type okeyWZ = okeyW + z * okey.info.strides[2];
dim_type ovalWZ = ovalW + z * oval.info.strides[2];
for(dim_type y = 0; y < okey.info.dims[1]; y++) {
dim_type okeyOffset = okeyWZ + y * okey.info.strides[1];
dim_type ovalOffset = ovalWZ + y * oval.info.strides[1];
if(isAscending) {
compute::sort_by_key(
compute::make_buffer_iterator<Tk>(okey_buf, okeyOffset),
compute::make_buffer_iterator<Tk>(okey_buf, okeyOffset + okey.info.dims[0]),
compute::make_buffer_iterator<Tv>(oval_buf, ovalOffset),
compute::less<Tk>(), c_queue);
} else {
compute::sort_by_key(
compute::make_buffer_iterator<Tk>(okey_buf, okeyOffset),
compute::make_buffer_iterator<Tk>(okey_buf, okeyOffset + okey.info.dims[0]),
compute::make_buffer_iterator<Tv>(oval_buf, ovalOffset),
compute::greater<Tk>(), c_queue);
}
}
}
}
CL_DEBUG_FINISH(getQueue());
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
void convNHelper(const conv_kparam_t& param, Param& out, const Param& signal, const Param& filter)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static std::map<int, Program*> convProgs;
static std::map<int, Kernel*> convKernels;
int device = getActiveDeviceId();
std::call_once( compileFlags[device], [device] () {
std::ostringstream options;
options << " -D T=" << dtype_traits<T>::getName()
<< " -D accType="<< dtype_traits<aT>::getName()
<< " -D BASE_DIM="<< bDim
<< " -D EXPAND=" << expand;
if (std::is_same<T, double>::value ||
std::is_same<T, cdouble>::value) {
options << " -D USE_DOUBLE";
}
Program prog;
buildProgram(prog, convolve_cl, convolve_cl_len, options.str());
convProgs[device] = new Program(prog);
convKernels[device] = new Kernel(*convProgs[device], "convolve");
});
auto convOp = cl::KernelFunctor<Buffer, KParam, Buffer, KParam,
cl::LocalSpaceArg, Buffer, KParam,
int, int,
int, int, int,
int, int, int
>(*convKernels[device]);
convOp(EnqueueArgs(getQueue(), param.global, param.local),
*out.data, out.info, *signal.data, signal.info, cl::Local(param.loc_size),
*param.impulse, filter.info, param.nBBS0, param.nBBS1,
param.o[0], param.o[1], param.o[2], param.s[0], param.s[1], param.s[2]);
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
int cholesky_inplace(Array<T> &in, const bool is_upper)
{
try {
initBlas();
dim4 iDims = in.dims();
int N = iDims[0];
magma_uplo_t uplo = is_upper ? MagmaUpper : MagmaLower;
int info = 0;
cl::Buffer *in_buf = in.get();
magma_potrf_gpu<T>(uplo, N,
(*in_buf)(), in.getOffset(), in.strides()[1],
getQueue()(), &info);
return info;
} catch (cl::Error &err) {
CL_TO_AF_ERROR(err);
}
}
void sort0(Param val)
{
try {
compute::command_queue c_queue(getQueue()());
compute::buffer val_buf((*val.data)());
for(int w = 0; w < val.info.dims[3]; w++) {
int valW = w * val.info.strides[3];
for(int z = 0; z < val.info.dims[2]; z++) {
int valWZ = valW + z * val.info.strides[2];
for(int y = 0; y < val.info.dims[1]; y++) {
int valOffset = valWZ + y * val.info.strides[1];
if(isAscending) {
compute::stable_sort(
compute::make_buffer_iterator<T>(val_buf, valOffset),
compute::make_buffer_iterator<T>(val_buf, valOffset + val.info.dims[0]),
compute::less<T>(), c_queue);
} else {
compute::stable_sort(
compute::make_buffer_iterator<T>(val_buf, valOffset),
compute::make_buffer_iterator<T>(val_buf, valOffset + val.info.dims[0]),
compute::greater<T>(), c_queue);
}
}
}
}
CL_DEBUG_FINISH(getQueue());
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
static void scan_dim(Param &out, const Param &in)
{
try {
uint threads_y = std::min(THREADS_Y, nextpow2(out.info.dims[dim]));
uint threads_x = THREADS_X;
uint groups_all[] = {divup((uint)out.info.dims[0], threads_x),
(uint)out.info.dims[1],
(uint)out.info.dims[2],
(uint)out.info.dims[3]
};
groups_all[dim] = divup(out.info.dims[dim], threads_y * REPEAT);
if (groups_all[dim] == 1) {
scan_dim_fn<Ti, To, op, dim, true>(out, out, in,
threads_y,
groups_all);
} else {
Param tmp = out;
tmp.info.dims[dim] = groups_all[dim];
tmp.info.strides[0] = 1;
for (int k = 1; k < 4; k++) {
tmp.info.strides[k] = tmp.info.strides[k - 1] * tmp.info.dims[k - 1];
}
int tmp_elements = tmp.info.strides[3] * tmp.info.dims[3];
// FIXME: Do I need to free this ?
tmp.data = bufferAlloc(tmp_elements * sizeof(To));
scan_dim_fn<Ti, To, op, dim, false>(out, tmp, in,
threads_y,
groups_all);
int gdim = groups_all[dim];
groups_all[dim] = 1;
if (op == af_notzero_t) {
scan_dim_fn<To, To, af_add_t, dim, true>(tmp, tmp, tmp,
threads_y,
groups_all);
} else {
scan_dim_fn<To, To, op, dim, true>(tmp, tmp, tmp,
threads_y,
groups_all);
}
groups_all[dim] = gdim;
bcast_dim_fn<To, To, op, dim, true>(out, tmp,
threads_y,
groups_all);
bufferFree(tmp.data);
}
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
void exampleFunc(Param c, const Param a, const Param b, const af_someenum_t p)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static std::map<int, Program*> egProgs;
static std::map<int, Kernel*> egKernels;
int device = getActiveDeviceId();
// std::call_once is used to ensure OpenCL kernels
// are compiled only once for any given device and combination
// of template parameters to this kernel wrapper function 'exampleFunc<T>'
std::call_once( compileFlags[device], [device] () {
std::ostringstream options;
options << " -D T=" << dtype_traits<T>::getName();
// You can pass any template parameters as compile options
// to kernel the compilation step. This is equivalent of
// having templated kernels in CUDA
// The following option is passed to kernel compilation
// if template parameter T is double or complex double
// to enable FP64 extension
if (std::is_same<T, double>::value ||
std::is_same<T, cdouble>::value) {
options << " -D USE_DOUBLE";
}
Program prog;
// below helper function 'buildProgram' uses the option string
// we just created and compiles the kernel string
// 'example_cl' which was created by our opencl kernel code obfuscation
// stage
buildProgram(prog, example_cl, example_cl_len, options.str());
// create a cl::Program object on heap
egProgs[device] = new Program(prog);
// create a cl::Kernel object on heap
egKernels[device] = new Kernel(*egProgs[device], "example");
});
// configure work group parameters
NDRange local(THREADS_X, THREADS_Y);
int blk_x = divup(c.info.dims[0], THREADS_X);
int blk_y = divup(c.info.dims[1], THREADS_Y);
// configure global launch parameters
NDRange global(blk_x * THREADS_X, blk_y * THREADS_Y);
// create a kernel functor from the cl::Kernel object
// corresponding to the device on which current execution
// is happending.
auto exampleFuncOp = KernelFunctor<Buffer, KParam, Buffer, KParam,
Buffer, KParam, int>(*egKernels[device]);
// launch the kernel
exampleFuncOp(EnqueueArgs(getQueue(), global, local),
*c.data, c.info, *a.data, a.info, *b.data, b.info, (int)p);
// Below Macro activates validations ONLY in DEBUG
// mode as its name indicates
CL_DEBUG_FINISH(getQueue());
} catch (cl::Error err) { // Catch all cl::Errors and convert them
// to appropriate ArrayFire error codes
CL_TO_AF_ERROR(err);
}
}
void copy(Param dst, const Param src, int ndims, outType default_value, double factor)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static std::map<int, Program*> cpyProgs;
static std::map<int, Kernel*> cpyKernels;
int device = getActiveDeviceId();
std::call_once(compileFlags[device], [&]() {
std::ostringstream options;
options << " -D inType=" << dtype_traits<inType>::getName()
<< " -D outType=" << dtype_traits<outType>::getName()
<< " -D inType_" << dtype_traits<inType>::getName()
<< " -D outType_" << dtype_traits<outType>::getName()
<< " -D SAME_DIMS=" << same_dims;
if (std::is_same<inType, double>::value ||
std::is_same<inType, cdouble>::value ||
std::is_same<outType, double>::value ||
std::is_same<outType, cdouble>::value) {
options << " -D USE_DOUBLE";
}
Program prog;
buildProgram(prog, copy_cl, copy_cl_len, options.str());
cpyProgs[device] = new Program(prog);
cpyKernels[device] = new Kernel(*cpyProgs[device], "copy");
});
NDRange local(DIM0, DIM1);
size_t local_size[] = {DIM0, DIM1};
local_size[0] *= local_size[1];
if (ndims == 1) {
local_size[1] = 1;
}
int blk_x = divup(dst.info.dims[0], local_size[0]);
int blk_y = divup(dst.info.dims[1], local_size[1]);
NDRange global(blk_x * dst.info.dims[2] * DIM0,
blk_y * dst.info.dims[3] * DIM1);
dims_t trgt_dims;
if (same_dims) {
trgt_dims= {{dst.info.dims[0], dst.info.dims[1], dst.info.dims[2], dst.info.dims[3]}};
} else {
dim_t trgt_l = std::min(dst.info.dims[3], src.info.dims[3]);
dim_t trgt_k = std::min(dst.info.dims[2], src.info.dims[2]);
dim_t trgt_j = std::min(dst.info.dims[1], src.info.dims[1]);
dim_t trgt_i = std::min(dst.info.dims[0], src.info.dims[0]);
trgt_dims= {{trgt_i, trgt_j, trgt_k, trgt_l}};
}
auto copyOp = KernelFunctor<Buffer, KParam, Buffer, KParam,
outType, float, dims_t,
int, int
>(*cpyKernels[device]);
copyOp(EnqueueArgs(getQueue(), global, local),
*dst.data, dst.info, *src.data, src.info,
default_value, (float)factor, trgt_dims, blk_x, blk_y);
CL_DEBUG_FINISH(getQueue());
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
void packDataHelper(Param packed,
Param sig,
Param filter,
const int baseDim,
ConvolveBatchKind kind)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static std::map<int, Program*> fftconvolveProgs;
static std::map<int, Kernel*> pdKernel;
static std::map<int, Kernel*> paKernel;
int device = getActiveDeviceId();
std::call_once( compileFlags[device], [device] () {
std::ostringstream options;
options << " -D T=" << dtype_traits<T>::getName();
if ((af_dtype) dtype_traits<convT>::af_type == c32) {
options << " -D CONVT=float";
}
else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
options << " -D CONVT=double"
<< " -D USE_DOUBLE";
}
cl::Program prog;
buildProgram(prog, fftconvolve_pack_cl, fftconvolve_pack_cl_len, options.str());
fftconvolveProgs[device] = new Program(prog);
pdKernel[device] = new Kernel(*fftconvolveProgs[device], "pack_data");
paKernel[device] = new Kernel(*fftconvolveProgs[device], "pad_array");
});
Param sig_tmp, filter_tmp;
calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
int sig_packed_elem = sig_tmp.info.strides[3] * sig_tmp.info.dims[3];
int filter_packed_elem = filter_tmp.info.strides[3] * filter_tmp.info.dims[3];
// Number of packed complex elements in dimension 0
int sig_half_d0 = divup(sig.info.dims[0], 2);
int sig_half_d0_odd = sig.info.dims[0] % 2;
int blocks = divup(sig_packed_elem, THREADS);
// Locate features kernel sizes
NDRange local(THREADS);
NDRange global(blocks * THREADS);
// Pack signal in a complex matrix where first dimension is half the input
// (allows faster FFT computation) and pad array to a power of 2 with 0s
auto pdOp = make_kernel<Buffer, KParam,
Buffer, KParam,
const int, const int> (*pdKernel[device]);
pdOp(EnqueueArgs(getQueue(), global, local),
*sig_tmp.data, sig_tmp.info, *sig.data, sig.info,
sig_half_d0, sig_half_d0_odd);
CL_DEBUG_FINISH(getQueue());
blocks = divup(filter_packed_elem, THREADS);
global = NDRange(blocks * THREADS);
// Pad filter array with 0s
auto paOp = make_kernel<Buffer, KParam,
Buffer, KParam> (*paKernel[device]);
paOp(EnqueueArgs(getQueue(), global, local),
*filter_tmp.data, filter_tmp.info,
*filter.data, filter.info);
CL_DEBUG_FINISH(getQueue());
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
void complexMultiplyHelper(Param packed,
Param sig,
Param filter,
const int baseDim,
ConvolveBatchKind kind)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static std::map<int, Program*> fftconvolveProgs;
static std::map<int, Kernel*> cmKernel;
int device = getActiveDeviceId();
std::call_once( compileFlags[device], [device] () {
std::ostringstream options;
options << " -D T=" << dtype_traits<T>::getName()
<< " -D ONE2ONE=" << (int)ONE2ONE
<< " -D MANY2ONE=" << (int)MANY2ONE
<< " -D ONE2MANY=" << (int)ONE2MANY
<< " -D MANY2MANY=" << (int)MANY2MANY;
if ((af_dtype) dtype_traits<convT>::af_type == c32) {
options << " -D CONVT=float";
}
else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
options << " -D CONVT=double"
<< " -D USE_DOUBLE";
}
cl::Program prog;
buildProgram(prog,
fftconvolve_multiply_cl,
fftconvolve_multiply_cl_len,
options.str());
fftconvolveProgs[device] = new Program(prog);
cmKernel[device] = new Kernel(*fftconvolveProgs[device], "complex_multiply");
});
Param sig_tmp, filter_tmp;
calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
int sig_packed_elem = sig_tmp.info.strides[3] * sig_tmp.info.dims[3];
int filter_packed_elem = filter_tmp.info.strides[3] * filter_tmp.info.dims[3];
int mul_elem = (sig_packed_elem < filter_packed_elem) ?
filter_packed_elem : sig_packed_elem;
int blocks = divup(mul_elem, THREADS);
NDRange local(THREADS);
NDRange global(blocks * THREADS);
// Multiply filter and signal FFT arrays
auto cmOp = make_kernel<Buffer, KParam,
Buffer, KParam,
Buffer, KParam,
const int, const int> (*cmKernel[device]);
cmOp(EnqueueArgs(getQueue(), global, local),
*packed.data, packed.info,
*sig_tmp.data, sig_tmp.info,
*filter_tmp.data, filter_tmp.info,
mul_elem, (int)kind);
CL_DEBUG_FINISH(getQueue());
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
void reorderOutputHelper(Param out,
Param packed,
Param sig,
Param filter,
const int baseDim,
ConvolveBatchKind kind)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static std::map<int, Program*> fftconvolveProgs;
static std::map<int, Kernel*> roKernel;
int device = getActiveDeviceId();
std::call_once( compileFlags[device], [device] () {
std::ostringstream options;
options << " -D T=" << dtype_traits<T>::getName()
<< " -D ROUND_OUT=" << (int)roundOut
<< " -D EXPAND=" << (int)expand;
if ((af_dtype) dtype_traits<convT>::af_type == c32) {
options << " -D CONVT=float";
}
else if ((af_dtype) dtype_traits<convT>::af_type == c64 && isDouble) {
options << " -D CONVT=double"
<< " -D USE_DOUBLE";
}
cl::Program prog;
buildProgram(prog,
fftconvolve_reorder_cl,
fftconvolve_reorder_cl_len,
options.str());
fftconvolveProgs[device] = new Program(prog);
roKernel[device] = new Kernel(*fftconvolveProgs[device], "reorder_output");
});
Param sig_tmp, filter_tmp;
calcParamSizes(sig_tmp, filter_tmp, packed, sig, filter, baseDim, kind);
// Number of packed complex elements in dimension 0
int sig_half_d0 = divup(sig.info.dims[0], 2);
int blocks = divup(out.info.strides[3] * out.info.dims[3], THREADS);
NDRange local(THREADS);
NDRange global(blocks * THREADS);
auto roOp = make_kernel<Buffer, KParam,
Buffer, KParam,
KParam, const int,
const int> (*roKernel[device]);
if (kind == ONE2MANY) {
roOp(EnqueueArgs(getQueue(), global, local),
*out.data, out.info,
*filter_tmp.data, filter_tmp.info,
filter.info, sig_half_d0, baseDim);
}
else {
roOp(EnqueueArgs(getQueue(), global, local),
*out.data, out.info,
*sig_tmp.data, sig_tmp.info,
filter.info, sig_half_d0, baseDim);
}
CL_DEBUG_FINISH(getQueue());
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
void nearest_neighbour(Param idx,
Param dist,
Param query,
Param train,
const dim_t dist_dim,
const unsigned n_dist)
{
try {
const unsigned feat_len = query.info.dims[dist_dim];
const To max_dist = maxval<To>();
// Determine maximum feat_len capable of using shared memory (faster)
cl_ulong avail_lmem = getDevice().getInfo<CL_DEVICE_LOCAL_MEM_SIZE>();
size_t lmem_predef = 2 * THREADS * sizeof(unsigned) + feat_len * sizeof(T);
size_t ltrain_sz = THREADS * feat_len * sizeof(T);
bool use_lmem = (avail_lmem >= (lmem_predef + ltrain_sz)) ? true : false;
size_t lmem_sz = (use_lmem) ? lmem_predef + ltrain_sz : lmem_predef;
unsigned unroll_len = nextpow2(feat_len);
if (unroll_len != feat_len) unroll_len = 0;
std::string ref_name =
std::string("knn_") +
std::to_string(dist_type) +
std::string("_") +
std::to_string(use_lmem) +
std::string("_") +
std::string(dtype_traits<T>::getName()) +
std::string("_") +
std::to_string(unroll_len);
int device = getActiveDeviceId();
kc_t::iterator cache_idx = kernelCaches[device].find(ref_name);
kc_entry_t entry;
if (cache_idx == kernelCaches[device].end()) {
std::ostringstream options;
options << " -D T=" << dtype_traits<T>::getName()
<< " -D To=" << dtype_traits<To>::getName()
<< " -D THREADS=" << THREADS
<< " -D FEAT_LEN=" << unroll_len;
switch(dist_type) {
case AF_SAD: options <<" -D DISTOP=_sad_"; break;
case AF_SSD: options <<" -D DISTOP=_ssd_"; break;
case AF_SHD: options <<" -D DISTOP=_shd_ -D __SHD__";
break;
default: break;
}
if (std::is_same<T, double>::value ||
std::is_same<T, cdouble>::value) {
options << " -D USE_DOUBLE";
}
if (use_lmem)
options << " -D USE_LOCAL_MEM";
cl::Program prog;
buildProgram(prog,
nearest_neighbour_cl,
nearest_neighbour_cl_len,
options.str());
entry.prog = new Program(prog);
entry.ker = new Kernel[3];
entry.ker[0] = Kernel(*entry.prog, "nearest_neighbour_unroll");
entry.ker[1] = Kernel(*entry.prog, "nearest_neighbour");
entry.ker[2] = Kernel(*entry.prog, "select_matches");
kernelCaches[device][ref_name] = entry;
} else {
entry = cache_idx->second;
}
const dim_t sample_dim = (dist_dim == 0) ? 1 : 0;
const unsigned nquery = query.info.dims[sample_dim];
const unsigned ntrain = train.info.dims[sample_dim];
unsigned nblk = divup(ntrain, THREADS);
const NDRange local(THREADS, 1);
const NDRange global(nblk * THREADS, 1);
cl::Buffer *d_blk_idx = bufferAlloc(nblk * nquery * sizeof(unsigned));
cl::Buffer *d_blk_dist = bufferAlloc(nblk * nquery * sizeof(To));
// For each query vector, find training vector with smallest Hamming
// distance per CUDA block
if (unroll_len > 0) {
auto huOp = KernelFunctor<Buffer, Buffer,
Buffer, KParam,
Buffer, KParam,
const To,
LocalSpaceArg> (entry.ker[0]);
huOp(EnqueueArgs(getQueue(), global, local),
*d_blk_idx, *d_blk_dist,
*query.data, query.info, *train.data, train.info,
max_dist, cl::Local(lmem_sz));
}
else {
auto hmOp = KernelFunctor<Buffer, Buffer,
Buffer, KParam,
Buffer, KParam,
const To, const unsigned,
LocalSpaceArg> (entry.ker[1]);
hmOp(EnqueueArgs(getQueue(), global, local),
*d_blk_idx, *d_blk_dist,
*query.data, query.info, *train.data, train.info,
max_dist, feat_len, cl::Local(lmem_sz));
}
CL_DEBUG_FINISH(getQueue());
const NDRange local_sm(32, 8);
const NDRange global_sm(divup(nquery, 32) * 32, 8);
// Reduce all smallest Hamming distances from each block and store final
// best match
auto smOp = KernelFunctor<Buffer, Buffer, Buffer, Buffer,
const unsigned, const unsigned,
const To> (entry.ker[2]);
smOp(EnqueueArgs(getQueue(), global_sm, local_sm),
*idx.data, *dist.data,
*d_blk_idx, *d_blk_dist,
nquery, nblk, max_dist);
CL_DEBUG_FINISH(getQueue());
bufferFree(d_blk_idx);
bufferFree(d_blk_dist);
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
void orb(unsigned* out_feat,
Param& x_out,
Param& y_out,
Param& score_out,
Param& ori_out,
Param& size_out,
Param& desc_out,
Param image,
const float fast_thr,
const unsigned max_feat,
const float scl_fctr,
const unsigned levels,
const bool blur_img)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static Program orbProgs[DeviceManager::MAX_DEVICES];
static Kernel hrKernel[DeviceManager::MAX_DEVICES];
static Kernel kfKernel[DeviceManager::MAX_DEVICES];
static Kernel caKernel[DeviceManager::MAX_DEVICES];
static Kernel eoKernel[DeviceManager::MAX_DEVICES];
int device = getActiveDeviceId();
std::call_once( compileFlags[device], [device] () {
std::ostringstream options;
options << " -D T=" << dtype_traits<T>::getName()
<< " -D BLOCK_SIZE=" << ORB_THREADS_X;
if (std::is_same<T, double>::value ||
std::is_same<T, cdouble>::value) {
options << " -D USE_DOUBLE";
}
buildProgram(orbProgs[device],
orb_cl,
orb_cl_len,
options.str());
hrKernel[device] = Kernel(orbProgs[device], "harris_response");
kfKernel[device] = Kernel(orbProgs[device], "keep_features");
caKernel[device] = Kernel(orbProgs[device], "centroid_angle");
eoKernel[device] = Kernel(orbProgs[device], "extract_orb");
});
unsigned patch_size = REF_PAT_SIZE;
unsigned min_side = std::min(image.info.dims[0], image.info.dims[1]);
unsigned max_levels = 0;
float scl_sum = 0.f;
for (unsigned i = 0; i < levels; i++) {
min_side /= scl_fctr;
// Minimum image side for a descriptor to be computed
if (min_side < patch_size || max_levels == levels) break;
max_levels++;
scl_sum += 1.f / (float)pow(scl_fctr,(float)i);
}
std::vector<cl::Buffer*> d_x_pyr(max_levels);
std::vector<cl::Buffer*> d_y_pyr(max_levels);
std::vector<cl::Buffer*> d_score_pyr(max_levels);
std::vector<cl::Buffer*> d_ori_pyr(max_levels);
std::vector<cl::Buffer*> d_size_pyr(max_levels);
std::vector<cl::Buffer*> d_desc_pyr(max_levels);
std::vector<unsigned> feat_pyr(max_levels);
unsigned total_feat = 0;
// Compute number of features to keep for each level
std::vector<unsigned> lvl_best(max_levels);
unsigned feat_sum = 0;
for (unsigned i = 0; i < max_levels-1; i++) {
float lvl_scl = (float)pow(scl_fctr,(float)i);
lvl_best[i] = ceil((max_feat / scl_sum) / lvl_scl);
feat_sum += lvl_best[i];
}
lvl_best[max_levels-1] = max_feat - feat_sum;
// Maintain a reference to previous level image
Param prev_img;
Param lvl_img;
const unsigned gauss_len = 9;
T* h_gauss = nullptr;
Param gauss_filter;
gauss_filter.data = nullptr;
for (unsigned i = 0; i < max_levels; i++) {
const float lvl_scl = (float)pow(scl_fctr,(float)i);
if (i == 0) {
// First level is used in its original size
lvl_img = image;
prev_img = image;
}
else if (i > 0) {
// Resize previous level image to current level dimensions
lvl_img.info.dims[0] = round(image.info.dims[0] / lvl_scl);
lvl_img.info.dims[1] = round(image.info.dims[1] / lvl_scl);
lvl_img.info.strides[0] = 1;
lvl_img.info.strides[1] = lvl_img.info.dims[0];
for (int k = 2; k < 4; k++) {
lvl_img.info.dims[k] = 1;
lvl_img.info.strides[k] = lvl_img.info.dims[k - 1] * lvl_img.info.strides[k - 1];
}
lvl_img.info.offset = 0;
lvl_img.data = bufferAlloc(lvl_img.info.dims[3] * lvl_img.info.strides[3] * sizeof(T));
resize<T, AF_INTERP_BILINEAR>(lvl_img, prev_img);
if (i > 1)
bufferFree(prev_img.data);
prev_img = lvl_img;
}
unsigned lvl_feat = 0;
Param d_x_feat, d_y_feat, d_score_feat;
// Round feature size to nearest odd integer
float size = 2.f * floor(patch_size / 2.f) + 1.f;
// Avoid keeping features that might be too wide and might not fit on
// the image, sqrt(2.f) is the radius when angle is 45 degrees and
// represents widest case possible
unsigned edge = ceil(size * sqrt(2.f) / 2.f);
// Detect FAST features
fast<T, 9, true>(&lvl_feat, d_x_feat, d_y_feat, d_score_feat,
lvl_img, fast_thr, 0.15f, edge);
if (lvl_feat == 0) {
feat_pyr[i] = 0;
if (i > 0 && i == max_levels-1)
bufferFree(lvl_img.data);
continue;
}
bufferFree(d_score_feat.data);
unsigned usable_feat = 0;
cl::Buffer* d_usable_feat = bufferAlloc(sizeof(unsigned));
getQueue().enqueueWriteBuffer(*d_usable_feat, CL_TRUE, 0, sizeof(unsigned), &usable_feat);
cl::Buffer* d_x_harris = bufferAlloc(lvl_feat * sizeof(float));
cl::Buffer* d_y_harris = bufferAlloc(lvl_feat * sizeof(float));
cl::Buffer* d_score_harris = bufferAlloc(lvl_feat * sizeof(float));
// Calculate Harris responses
// Good block_size >= 7 (must be an odd number)
const dim_type blk_x = divup(lvl_feat, ORB_THREADS_X);
const NDRange local(ORB_THREADS_X, ORB_THREADS_Y);
const NDRange global(blk_x * ORB_THREADS_X, ORB_THREADS_Y);
unsigned block_size = 7;
float k_thr = 0.04f;
auto hrOp = make_kernel<Buffer, Buffer, Buffer,
Buffer, Buffer, const unsigned,
Buffer, Buffer, KParam,
const unsigned, const float, const unsigned> (hrKernel[device]);
hrOp(EnqueueArgs(getQueue(), global, local),
*d_x_harris, *d_y_harris, *d_score_harris,
*d_x_feat.data, *d_y_feat.data, lvl_feat,
*d_usable_feat, *lvl_img.data, lvl_img.info,
block_size, k_thr, patch_size);
CL_DEBUG_FINISH(getQueue());
getQueue().enqueueReadBuffer(*d_usable_feat, CL_TRUE, 0, sizeof(unsigned), &usable_feat);
bufferFree(d_x_feat.data);
bufferFree(d_y_feat.data);
bufferFree(d_usable_feat);
if (usable_feat == 0) {
feat_pyr[i] = 0;
bufferFree(d_x_harris);
bufferFree(d_y_harris);
bufferFree(d_score_harris);
if (i > 0 && i == max_levels-1)
bufferFree(lvl_img.data);
continue;
}
// Sort features according to Harris responses
Param d_harris_sorted;
Param d_harris_idx;
d_harris_sorted.info.dims[0] = usable_feat;
d_harris_idx.info.dims[0] = usable_feat;
d_harris_sorted.info.strides[0] = 1;
d_harris_idx.info.strides[0] = 1;
for (int k = 1; k < 4; k++) {
d_harris_sorted.info.dims[k] = 1;
d_harris_idx.info.dims[k] = 1;
d_harris_sorted.info.strides[k] = d_harris_sorted.info.dims[k - 1] * d_harris_sorted.info.strides[k - 1];
d_harris_idx.info.strides[k] = d_harris_idx.info.dims[k - 1] * d_harris_idx.info.strides[k - 1];
}
d_harris_sorted.info.offset = 0;
d_harris_idx.info.offset = 0;
d_harris_sorted.data = d_score_harris;
d_harris_idx.data = bufferAlloc((d_harris_idx.info.dims[0]) * sizeof(unsigned));
sort0_index<float, false>(d_harris_sorted, d_harris_idx);
cl::Buffer* d_x_lvl = bufferAlloc(usable_feat * sizeof(float));
cl::Buffer* d_y_lvl = bufferAlloc(usable_feat * sizeof(float));
cl::Buffer* d_score_lvl = bufferAlloc(usable_feat * sizeof(float));
usable_feat = min(usable_feat, lvl_best[i]);
// Keep only features with higher Harris responses
const dim_type keep_blk = divup(usable_feat, ORB_THREADS);
const NDRange local_keep(ORB_THREADS, 1);
const NDRange global_keep(keep_blk * ORB_THREADS, 1);
auto kfOp = make_kernel<Buffer, Buffer, Buffer,
Buffer, Buffer, Buffer, Buffer,
const unsigned> (kfKernel[device]);
kfOp(EnqueueArgs(getQueue(), global_keep, local_keep),
*d_x_lvl, *d_y_lvl, *d_score_lvl,
*d_x_harris, *d_y_harris, *d_harris_sorted.data, *d_harris_idx.data,
usable_feat);
CL_DEBUG_FINISH(getQueue());
bufferFree(d_x_harris);
bufferFree(d_y_harris);
bufferFree(d_harris_sorted.data);
bufferFree(d_harris_idx.data);
cl::Buffer* d_ori_lvl = bufferAlloc(usable_feat * sizeof(float));
cl::Buffer* d_size_lvl = bufferAlloc(usable_feat * sizeof(float));
// Compute orientation of features
const dim_type centroid_blk_x = divup(usable_feat, ORB_THREADS_X);
const NDRange local_centroid(ORB_THREADS_X, ORB_THREADS_Y);
const NDRange global_centroid(centroid_blk_x * ORB_THREADS_X, ORB_THREADS_Y);
auto caOp = make_kernel<Buffer, Buffer, Buffer,
const unsigned, Buffer, KParam,
const unsigned> (caKernel[device]);
caOp(EnqueueArgs(getQueue(), global_centroid, local_centroid),
*d_x_lvl, *d_y_lvl, *d_ori_lvl,
usable_feat, *lvl_img.data, lvl_img.info,
patch_size);
CL_DEBUG_FINISH(getQueue());
Param lvl_filt;
Param lvl_tmp;
if (blur_img) {
lvl_filt = lvl_img;
lvl_tmp = lvl_img;
lvl_filt.data = bufferAlloc(lvl_filt.info.dims[0] * lvl_filt.info.dims[1] * sizeof(T));
lvl_tmp.data = bufferAlloc(lvl_tmp.info.dims[0] * lvl_tmp.info.dims[1] * sizeof(T));
// Calculate a separable Gaussian kernel
if (h_gauss == nullptr) {
h_gauss = new T[gauss_len];
gaussian1D(h_gauss, gauss_len, 2.f);
gauss_filter.info.dims[0] = gauss_len;
gauss_filter.info.strides[0] = 1;
for (int k = 1; k < 4; k++) {
gauss_filter.info.dims[k] = 1;
gauss_filter.info.strides[k] = gauss_filter.info.dims[k - 1] * gauss_filter.info.strides[k - 1];
}
dim_type gauss_elem = gauss_filter.info.strides[3] * gauss_filter.info.dims[3];
gauss_filter.data = bufferAlloc(gauss_elem * sizeof(T));
getQueue().enqueueWriteBuffer(*gauss_filter.data, CL_TRUE, 0, gauss_elem * sizeof(T), h_gauss);
}
// Filter level image with Gaussian kernel to reduce noise sensitivity
convolve2<T, convAccT, 0, false, gauss_len>(lvl_tmp, lvl_img, gauss_filter);
convolve2<T, convAccT, 1, false, gauss_len>(lvl_filt, lvl_tmp, gauss_filter);
bufferFree(lvl_tmp.data);
}
// Compute ORB descriptors
cl::Buffer* d_desc_lvl = bufferAlloc(usable_feat * 8 * sizeof(unsigned));
unsigned* h_desc_lvl = new unsigned[usable_feat * 8];
for (int j = 0; j < (int)usable_feat * 8; j++)
h_desc_lvl[j] = 0;
getQueue().enqueueWriteBuffer(*d_desc_lvl, CL_TRUE, 0, usable_feat * 8 * sizeof(unsigned), h_desc_lvl);
delete[] h_desc_lvl;
auto eoOp = make_kernel<Buffer, const unsigned,
Buffer, Buffer, Buffer, Buffer,
Buffer, KParam,
const float, const unsigned> (eoKernel[device]);
if (blur_img) {
eoOp(EnqueueArgs(getQueue(), global_centroid, local_centroid),
*d_desc_lvl, usable_feat,
*d_x_lvl, *d_y_lvl, *d_ori_lvl, *d_size_lvl,
*lvl_filt.data, lvl_filt.info,
lvl_scl, patch_size);
CL_DEBUG_FINISH(getQueue());
bufferFree(lvl_filt.data);
}
else {
eoOp(EnqueueArgs(getQueue(), global_centroid, local_centroid),
*d_desc_lvl, usable_feat,
*d_x_lvl, *d_y_lvl, *d_ori_lvl, *d_size_lvl,
*lvl_img.data, lvl_img.info,
lvl_scl, patch_size);
CL_DEBUG_FINISH(getQueue());
}
// Store results to pyramids
total_feat += usable_feat;
feat_pyr[i] = usable_feat;
d_x_pyr[i] = d_x_lvl;
d_y_pyr[i] = d_y_lvl;
d_score_pyr[i] = d_score_lvl;
d_ori_pyr[i] = d_ori_lvl;
d_size_pyr[i] = d_size_lvl;
d_desc_pyr[i] = d_desc_lvl;
if (i > 0 && i == max_levels-1)
bufferFree(lvl_img.data);
}
if (gauss_filter.data != nullptr)
bufferFree(gauss_filter.data);
if (h_gauss != nullptr)
delete[] h_gauss;
// If no features are found, set found features to 0 and return
if (total_feat == 0) {
*out_feat = 0;
return;
}
// Allocate output memory
x_out.info.dims[0] = total_feat;
x_out.info.strides[0] = 1;
y_out.info.dims[0] = total_feat;
y_out.info.strides[0] = 1;
score_out.info.dims[0] = total_feat;
score_out.info.strides[0] = 1;
ori_out.info.dims[0] = total_feat;
ori_out.info.strides[0] = 1;
size_out.info.dims[0] = total_feat;
size_out.info.strides[0] = 1;
desc_out.info.dims[0] = 8;
desc_out.info.strides[0] = 1;
desc_out.info.dims[1] = total_feat;
desc_out.info.strides[1] = desc_out.info.dims[0];
for (int k = 1; k < 4; k++) {
x_out.info.dims[k] = 1;
x_out.info.strides[k] = x_out.info.dims[k - 1] * x_out.info.strides[k - 1];
y_out.info.dims[k] = 1;
y_out.info.strides[k] = y_out.info.dims[k - 1] * y_out.info.strides[k - 1];
score_out.info.dims[k] = 1;
score_out.info.strides[k] = score_out.info.dims[k - 1] * score_out.info.strides[k - 1];
ori_out.info.dims[k] = 1;
ori_out.info.strides[k] = ori_out.info.dims[k - 1] * ori_out.info.strides[k - 1];
size_out.info.dims[k] = 1;
size_out.info.strides[k] = size_out.info.dims[k - 1] * size_out.info.strides[k - 1];
if (k > 1) {
desc_out.info.dims[k] = 1;
desc_out.info.strides[k] = desc_out.info.dims[k - 1] * desc_out.info.strides[k - 1];
}
}
if (total_feat > 0) {
size_t out_sz = total_feat * sizeof(float);
x_out.data = bufferAlloc(out_sz);
y_out.data = bufferAlloc(out_sz);
score_out.data = bufferAlloc(out_sz);
ori_out.data = bufferAlloc(out_sz);
size_out.data = bufferAlloc(out_sz);
size_t desc_sz = total_feat * 8 * sizeof(unsigned);
desc_out.data = bufferAlloc(desc_sz);
}
unsigned offset = 0;
for (unsigned i = 0; i < max_levels; i++) {
if (feat_pyr[i] == 0)
continue;
if (i > 0)
offset += feat_pyr[i-1];
getQueue().enqueueCopyBuffer(*d_x_pyr[i], *x_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
getQueue().enqueueCopyBuffer(*d_y_pyr[i], *y_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
getQueue().enqueueCopyBuffer(*d_score_pyr[i], *score_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
getQueue().enqueueCopyBuffer(*d_ori_pyr[i], *ori_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
getQueue().enqueueCopyBuffer(*d_size_pyr[i], *size_out.data, 0, offset*sizeof(float), feat_pyr[i] * sizeof(float));
getQueue().enqueueCopyBuffer(*d_desc_pyr[i], *desc_out.data, 0, offset*8*sizeof(unsigned), feat_pyr[i] * 8 * sizeof(unsigned));
bufferFree(d_x_pyr[i]);
bufferFree(d_y_pyr[i]);
bufferFree(d_score_pyr[i]);
bufferFree(d_ori_pyr[i]);
bufferFree(d_size_pyr[i]);
bufferFree(d_desc_pyr[i]);
}
// Sets number of output features
*out_feat = total_feat;
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
void morph(Param out,
const Param in,
const Param mask)
{
try {
static std::once_flag compileFlags[DeviceManager::MAX_DEVICES];
static std::map<int, Program*> morProgs;
static std::map<int, Kernel*> morKernels;
int device = getActiveDeviceId();
std::call_once( compileFlags[device], [device] () {
ToNumStr<T> toNumStr;
T init = isDilation ? Binary<T, af_max_t>().init() : Binary<T, af_min_t>().init();
std::ostringstream options;
options << " -D T=" << dtype_traits<T>::getName()
<< " -D isDilation="<< isDilation
<< " -D init=" << toNumStr(init)
<< " -D windLen=" << windLen;
if (std::is_same<T, double>::value ||
std::is_same<T, cdouble>::value) {
options << " -D USE_DOUBLE";
}
Program prog;
buildProgram(prog, morph_cl, morph_cl_len, options.str());
morProgs[device] = new Program(prog);
morKernels[device] = new Kernel(*morProgs[device], "morph");
});
auto morphOp = KernelFunctor<Buffer, KParam,
Buffer, KParam,
Buffer, cl::LocalSpaceArg,
int, int
>(*morKernels[device]);
NDRange local(THREADS_X, THREADS_Y);
int blk_x = divup(in.info.dims[0], THREADS_X);
int blk_y = divup(in.info.dims[1], THREADS_Y);
// launch batch * blk_x blocks along x dimension
NDRange global(blk_x * THREADS_X * in.info.dims[2],
blk_y * THREADS_Y * in.info.dims[3]);
// copy mask/filter to constant memory
cl_int se_size = sizeof(T)*windLen*windLen;
cl::Buffer *mBuff = bufferAlloc(se_size);
getQueue().enqueueCopyBuffer(*mask.data, *mBuff, 0, 0, se_size);
// calculate shared memory size
const int halo = windLen/2;
const int padding = 2*halo;
const int locLen = THREADS_X + padding + 1;
const int locSize = locLen * (THREADS_Y+padding);
morphOp(EnqueueArgs(getQueue(), global, local),
*out.data, out.info, *in.data, in.info, *mBuff,
cl::Local(locSize*sizeof(T)), blk_x, blk_y);
bufferFree(mBuff);
CL_DEBUG_FINISH(getQueue());
} catch (cl::Error err) {
CL_TO_AF_ERROR(err);
throw;
}
}
