array constant(cfloat val, const dim4 &dims)
{
af_array res;
AF_THROW(af_constant_complex(&res, real(val), imag(val),
dims.ndims(), dims.get(), c32));
return array(res);
}
Array<T> convolve2(Array<T> const& signal, Array<accT> const& c_filter, Array<accT> const& r_filter)
{
const dim4 cfDims = c_filter.dims();
const dim4 rfDims = r_filter.dims();
const dim_t cfLen= cfDims.elements();
const dim_t rfLen= rfDims.elements();
const dim4 sDims = signal.dims();
dim4 tDims = sDims;
dim4 oDims = sDims;
if (expand) {
tDims[0] += cfLen - 1;
oDims[0] += cfLen - 1;
oDims[1] += rfLen - 1;
}
Array<T> temp= createEmptyArray<T>(tDims);
Array<T> out = createEmptyArray<T>(oDims);
kernel::convolve2<T, accT, 0, expand>(temp, signal, c_filter);
kernel::convolve2<T, accT, 1, expand>(out, temp, r_filter);
return out;
}
Array<T>::Array(dim4 dims, const T * const in_data):
ArrayInfo(getActiveDeviceId(), dims, dim4(0,0,0,0), calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
data(memAlloc<T>(dims.elements()), memFree<T>), data_dims(dims),
node(), ready(true), offset(0), owner(true)
{
std::copy(in_data, in_data + dims.elements(), data.get());
}
static
void assign(af_array &out, const unsigned &ndims, const af_seq *index, const af_array &in)
{
ArrayInfo iInfo = getInfo(in);
ArrayInfo oInfo = getInfo(out);
af_dtype iType = iInfo.getType();
dim4 const outDs = oInfo.dims();
dim4 const iDims = iInfo.dims();
ARG_ASSERT(0, (outDs.ndims()>=iDims.ndims()));
ARG_ASSERT(1, (outDs.ndims()>=(int)ndims));
AF_CHECK(af_eval(out));
vector<af_seq> index_(index, index+ndims);
dim4 const oStrides = af::toStride(index_, outDs);
dim4 oDims = af::toDims(index_, outDs);
dim4 oOffsets = af::toOffset(index_, outDs);
Array<T> *dst = createRefArray<T>(getArray<T>(out), oDims, oOffsets, oStrides);
for (int i = 0; i < 4; i++) {
if (oDims[i] != iDims[i])
AF_ERROR("Size mismatch between input and output", AF_ERR_SIZE);
}
bool noCaseExecuted = true;
if (isComplex) {
noCaseExecuted = false;
switch(iType) {
case c64: copy<cdouble, T>(*dst, getArray<cdouble>(in), scalar<T>(0), 1.0); break;
case c32: copy<cfloat , T>(*dst, getArray<cfloat >(in), scalar<T>(0), 1.0); break;
default : noCaseExecuted = true; break;
}
}
static const T ZERO = scalar<T>(0);
if(noCaseExecuted) {
noCaseExecuted = false;
switch(iType) {
case f64: copy<double , T>(*dst, getArray<double>(in), ZERO, 1.0); break;
case f32: copy<float , T>(*dst, getArray<float >(in), ZERO, 1.0); break;
case s32: copy<int , T>(*dst, getArray<int >(in), ZERO, 1.0); break;
case u32: copy<uint , T>(*dst, getArray<uint >(in), ZERO, 1.0); break;
case u8 : copy<uchar , T>(*dst, getArray<uchar >(in), ZERO, 1.0); break;
case b8 : copy<char , T>(*dst, getArray<char >(in), ZERO, 1.0); break;
default : noCaseExecuted = true; break;
}
}
if (noCaseExecuted)
TYPE_ERROR(1, iType);
delete dst;
}
AFAPI array constant(cdouble val, const dim4 &dims, const af::dtype type)
{
if (type != c32 && type != c64) {
return constant(real(val), dims, type);
}
af_array res;
AF_THROW(af_constant_complex(&res,
real(val),
imag(val),
dims.ndims(),
dims.get(), type));
return array(res);
}
Array<T>::Array(dim4 dims, const T * const in_data, bool is_device, bool copy_device):
info(getActiveDeviceId(), dims, 0, calcStrides(dims), (af_dtype)dtype_traits<T>::af_type),
data((is_device & !copy_device) ? (T*)in_data : memAlloc<T>(dims.elements()).release(), memFree<T>), data_dims(dims),
node(bufferNodePtr<T>()), ready(true), owner(true)
{
static_assert(is_standard_layout<Array<T>>::value, "Array<T> must be a standard layout type");
static_assert(offsetof(Array<T>, info) == 0, "Array<T>::info must be the first member variable of Array<T>");
if (!is_device || copy_device) {
// Ensure the memory being written to isnt used anywhere else.
getQueue().sync();
copy(in_data, in_data + dims.elements(), data.get());
}
}
static
void assign(Array<Tout> &out, const unsigned &ndims, const af_seq *index, const Array<Tin> &in_)
{
dim4 const outDs = out.dims();
dim4 const iDims = in_.dims();
DIM_ASSERT(0, (outDs.ndims()>=iDims.ndims()));
DIM_ASSERT(0, (outDs.ndims()>=(dim_t)ndims));
out.eval();
vector<af_seq> index_(index, index+ndims);
dim4 oDims = toDims(index_, outDs);
bool is_vector = true;
for (int i = 0; is_vector && i < (int)oDims.ndims() - 1; i++) {
is_vector &= oDims[i] == 1;
}
is_vector &= in_.isVector() || in_.isScalar();
for (dim_t i = ndims; i < (int)in_.ndims(); i++) {
oDims[i] = 1;
}
if (is_vector) {
if (oDims.elements() != (dim_t)in_.elements() &&
in_.elements() != 1) {
AF_ERROR("Size mismatch between input and output", AF_ERR_SIZE);
}
// If both out and in are vectors of equal elements, reshape in to out dims
Array<Tin> in = in_.elements() == 1 ? tile(in_, oDims) : modDims(in_, oDims);
Array<Tout> dst = createSubArray<Tout>(out, index_, false);
copyArray<Tin , Tout>(dst, in);
} else {
for (int i = 0; i < 4; i++) {
if (oDims[i] != iDims[i]) {
AF_ERROR("Size mismatch between input and output", AF_ERR_SIZE);
}
}
Array<Tout> dst = createSubArray<Tout>(out, index_, false);
copyArray<Tin , Tout>(dst, in_);
}
}
Array<T>::Array(dim4 dims)
: info(getActiveDeviceId(), dims, 0, calcStrides(dims),
(af_dtype)dtype_traits<T>::af_type)
, data(memAlloc<T>(dims.elements()).release(), memFree<T>)
, data_dims(dims)
, node(bufferNodePtr<T>())
, ready(true)
, owner(true) {}
dim4 calcStrides(const dim4 &parentDim)
{
dim4 out(1, 1, 1, 1);
dim_t *out_dims = out.get();
const dim_t *parent_dims = parentDim.get();
for (dim_t i=1; i < 4; i++) {
out_dims[i] = out_dims[i - 1] * parent_dims[i-1];
}
return out;
}
Array<in_t> lookup(const Array<in_t> &input,
const Array<idx_t> &indices, const unsigned dim)
{
const dim4 iDims = input.dims();
dim4 oDims(1);
for (int d=0; d<4; ++d)
oDims[d] = (d==int(dim) ? indices.elements() : iDims[d]);
Array<in_t> out = createEmptyArray<in_t>(oDims);
dim_t nDims = iDims.ndims();
switch(dim) {
case 0: kernel::lookup<in_t, idx_t, 0>(out, input, indices, nDims); break;
case 1: kernel::lookup<in_t, idx_t, 1>(out, input, indices, nDims); break;
case 2: kernel::lookup<in_t, idx_t, 2>(out, input, indices, nDims); break;
case 3: kernel::lookup<in_t, idx_t, 3>(out, input, indices, nDims); break;
}
return out;
}
AF_BATCH_KIND identifyBatchKind(const dim4 &sDims, const dim4 &fDims) {
dim_t sn = sDims.ndims();
dim_t fn = fDims.ndims();
if (sn == baseDim && fn == baseDim)
return AF_BATCH_NONE;
else if (sn == baseDim && (fn > baseDim && fn <= 4))
return AF_BATCH_RHS;
else if ((sn > baseDim && sn <= 4) && fn == baseDim)
return AF_BATCH_LHS;
else if ((sn > baseDim && sn <= 4) && (fn > baseDim && fn <= 4)) {
bool doesDimensionsMatch = true;
bool isInterleaved = true;
for (dim_t i = baseDim; i < 4; i++) {
doesDimensionsMatch &= (sDims[i] == fDims[i]);
isInterleaved &=
(sDims[i] == 1 || fDims[i] == 1 || sDims[i] == fDims[i]);
}
if (doesDimensionsMatch) return AF_BATCH_SAME;
return (isInterleaved ? AF_BATCH_DIFF : AF_BATCH_UNSUPPORTED);
} else
return AF_BATCH_UNSUPPORTED;
}
ConvolveBatchKind identifyBatchKind(const dim4 &sDims, const dim4 &fDims)
{
dim_t sn = sDims.ndims();
dim_t fn = fDims.ndims();
if (sn==baseDim && fn==baseDim)
return ONE2ONE;
else if (sn==baseDim && (fn>baseDim && fn<=4))
return ONE2MANY;
else if ((sn>baseDim && sn<=4) && fn==baseDim)
return MANY2ONE;
else if ((sn>baseDim && sn<=4) && (fn>baseDim && fn<=4)) {
bool doesDimensionsMatch = true;
for (dim_t i=baseDim; i<4; i++) {
if (sDims[i]!=fDims[i]) {
doesDimensionsMatch = false;
break;
}
}
return (doesDimensionsMatch ? MANY2MANY : CONVOLVE_UNSUPPORTED_BATCH_MODE);
}
else
return CONVOLVE_UNSUPPORTED_BATCH_MODE;
}
array
constant(T val, const dim4 &dims, const af::dtype type)
{
af_array res;
if (type != s64 && type != u64) {
AF_THROW(af_constant(&res, (double)val,
dims.ndims(), dims.get(), type));
}
else if (type == s64) {
AF_THROW(af_constant_long (&res, ( intl)val,
dims.ndims(),
dims.get()));
} else {
AF_THROW(af_constant_ulong(&res, (uintl)val,
dims.ndims(),
dims.get()));
}
return array(res);
}
array identity(const dim4 &dims, const af::dtype type)
{
af_array res;
AF_THROW(af_identity(&res, dims.ndims(), dims.get(), type));
return array(res);
}
void fast_pyramid(std::vector<unsigned>& feat_pyr,
std::vector<float*>& d_x_pyr,
std::vector<float*>& d_y_pyr,
std::vector<unsigned>& lvl_best,
std::vector<float>& lvl_scl,
std::vector<CParam<T> >& img_pyr,
CParam<T> in,
const float fast_thr,
const unsigned max_feat,
const float scl_fctr,
const unsigned levels,
const unsigned patch_size)
{
unsigned min_side = std::min(in.dims[0], in.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)std::pow(scl_fctr,(float)i);
}
// Compute number of features to keep for each level
lvl_best.resize(max_levels);
lvl_scl.resize(max_levels);
unsigned feat_sum = 0;
for (unsigned i = 0; i < max_levels-1; i++) {
float scl = (float)std::pow(scl_fctr,(float)i);
lvl_scl[i] = scl;
lvl_best[i] = ceil((max_feat / scl_sum) / lvl_scl[i]);
feat_sum += lvl_best[i];
}
lvl_scl[max_levels-1] = (float)std::pow(scl_fctr,(float)max_levels-1);
lvl_best[max_levels-1] = max_feat - feat_sum;
// Hold multi-scale image pyramids
static const dim4 dims0;
static const CParam<T> emptyCParam(NULL, dims0.get(), dims0.get());
// Need to do this as CParam does not have a default constructor
// And resize needs a default constructor or default value prior to C++11
img_pyr.resize(max_levels, emptyCParam);
// Create multi-scale image pyramid
for (unsigned i = 0; i < max_levels; i++) {
if (i == 0) {
// First level is used in its original size
img_pyr[i].ptr = in.ptr;
for (int k = 0; k < 4; k++) {
img_pyr[i].dims[k] = in.dims[k];
img_pyr[i].strides[k] = in.strides[k];
}
}
else {
// Resize previous level image to current level dimensions
Param<T> lvl_img;
lvl_img.dims[0] = round(in.dims[0] / lvl_scl[i]);
lvl_img.dims[1] = round(in.dims[1] / lvl_scl[i]);
lvl_img.strides[0] = 1;
lvl_img.strides[1] = lvl_img.dims[0] * lvl_img.strides[0];
for (int k = 2; k < 4; k++) {
lvl_img.dims[k] = 1;
lvl_img.strides[k] = lvl_img.dims[k - 1] * lvl_img.strides[k - 1];
}
int lvl_elem = lvl_img.strides[3] * lvl_img.dims[3];
lvl_img.ptr = memAlloc<T>(lvl_elem);
resize<T, AF_INTERP_BILINEAR>(lvl_img, img_pyr[i-1]);
img_pyr[i].ptr = lvl_img.ptr;
for (int k = 0; k < 4; k++) {
img_pyr[i].dims[k] = lvl_img.dims[k];
img_pyr[i].strides[k] = lvl_img.strides[k];
}
}
}
feat_pyr.resize(max_levels);
d_x_pyr.resize(max_levels);
d_y_pyr.resize(max_levels);
for (unsigned i = 0; i < max_levels; i++) {
unsigned lvl_feat = 0;
float* d_x_feat = NULL;
float* d_y_feat = NULL;
float* d_score_feat = NULL;
// Round feature size to nearest odd integer
float size = 2.f * floor(patch_size / 2.f) + 1.f;
// Avoid keeping features that are too wide and might not fit 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);
// Detects FAST features
fast(&lvl_feat, &d_x_feat, &d_y_feat, &d_score_feat,
img_pyr[i], fast_thr, 9, 1, 0.15f, edge);
// FAST score is not used
memFree(d_score_feat);
if (lvl_feat == 0) {
feat_pyr[i] = 0;
d_x_pyr[i] = NULL;
d_x_pyr[i] = NULL;
}
else {
feat_pyr[i] = lvl_feat;
d_x_pyr[i] = d_x_feat;
d_y_pyr[i] = d_y_feat;
}
}
}
array randn(const dim4 &dims, const dtype ty, randomEngine &r)
{
af_array out;
AF_THROW(af_random_normal(&out, dims.ndims(), dims.get(), ty, r.get()));
return array(out);
}
array constant(double val, const dim4 &dims, af_dtype type)
{
af_array res;
AF_THROW(af_constant(&res, val, dims.ndims(), dims.get(), type));
return array(res);
}
array iota(const dim4 &dims, const unsigned rep, af_dtype ty)
{
af_array out;
AF_THROW(af_iota(&out, dims.ndims(), dims.get(), rep, ty));
return array(out);
}
// Assign values to an array
array::array_proxy&
af::array::array_proxy::operator=(const array &other)
{
unsigned nd = numDims(impl->parent_->get());
const dim4 this_dims = getDims(impl->parent_->get());
const dim4 other_dims = other.dims();
int dim = gforDim(impl->indices_);
af_array other_arr = other.get();
bool batch_assign = false;
bool is_reordered = false;
if (dim >= 0) {
//FIXME: Figure out a faster, cleaner way to do this
dim4 out_dims = seqToDims(impl->indices_, this_dims, false);
batch_assign = true;
for (int i = 0; i < AF_MAX_DIMS; i++) {
if (this->impl->indices_[i].isBatch) batch_assign &= (other_dims[i] == 1);
else batch_assign &= (other_dims[i] == out_dims[i]);
}
if (batch_assign) {
af_array out;
AF_THROW(af_tile(&out, other_arr,
out_dims[0] / other_dims[0],
out_dims[1] / other_dims[1],
out_dims[2] / other_dims[2],
out_dims[3] / other_dims[3]));
other_arr = out;
} else if (out_dims != other_dims) {
// HACK: This is a quick check to see if other has been reordered inside gfor
// TODO: Figure out if this breaks and implement a cleaner method
other_arr = gforReorder(other_arr, dim);
is_reordered = true;
}
}
af_array par_arr = 0;
if (impl->is_linear_) {
AF_THROW(af_flat(&par_arr, impl->parent_->get()));
nd = 1;
} else {
par_arr = impl->parent_->get();
}
af_array tmp = 0;
AF_THROW(af_assign_gen(&tmp, par_arr, nd, impl->indices_, other_arr));
af_array res = 0;
if (impl->is_linear_) {
AF_THROW(af_moddims(&res, tmp, this_dims.ndims(), this_dims.get()));
AF_THROW(af_release_array(par_arr));
AF_THROW(af_release_array(tmp));
} else {
res = tmp;
}
impl->parent_->set(res);
if (dim >= 0 && (is_reordered || batch_assign)) {
if (other_arr) AF_THROW(af_release_array(other_arr));
}
return *this;
}
array::array(const array& input, const dim4& dims) : arr(0)
{
AF_THROW(af_moddims(&arr, input.get(), AF_MAX_DIMS, dims.get()));
}
af_err af_approx1_uniform(af_array *yo, const af_array yi,
const af_array xo, const int xdim,
const double xi_beg, const double xi_step,
const af_interp_type method, const float offGrid)
{
try {
const ArrayInfo& yi_info = getInfo(yi);
const ArrayInfo& xo_info = getInfo(xo);
const dim4 yi_dims = yi_info.dims();
const dim4 xo_dims = xo_info.dims();
ARG_ASSERT(1, yi_info.isFloating()); // Only floating and complex types
ARG_ASSERT(2, xo_info.isRealFloating()) ; // Only floating types
ARG_ASSERT(1, yi_info.isSingle() == xo_info.isSingle()); // Must have same precision
ARG_ASSERT(1, yi_info.isDouble() == xo_info.isDouble()); // Must have same precision
ARG_ASSERT(3, xdim >= 0 && xdim < 4);
// POS should either be (x, 1, 1, 1) or (1, yi_dims[1], yi_dims[2], yi_dims[3])
if (xo_dims[xdim] != xo_dims.elements()) {
for (int i = 0; i < 4; i++) {
if (xdim != i) DIM_ASSERT(2, xo_dims[i] == yi_dims[i]);
}
}
ARG_ASSERT(5, xi_step != 0);
ARG_ASSERT(6, (method == AF_INTERP_CUBIC ||
method == AF_INTERP_CUBIC_SPLINE ||
method == AF_INTERP_LINEAR ||
method == AF_INTERP_LINEAR_COSINE ||
method == AF_INTERP_LOWER ||
method == AF_INTERP_NEAREST));
if (yi_dims.ndims() == 0 || xo_dims.ndims() == 0) {
*yo = createHandle(dim4(0,0,0,0), yi_info.getType());
return AF_SUCCESS;
}
dim4 yo_dims = yi_dims;
yo_dims[xdim] = xo_dims[xdim];
if (*yo == 0) {
*yo = createHandle(yo_dims, yi_info.getType());
}
DIM_ASSERT(1, getInfo(*yo).dims() == yo_dims);
switch(yi_info.getType()) {
case f32: approx1<float , float >(yo, yi, xo, xdim,
xi_beg, xi_step,
method, offGrid); break;
case f64: approx1<double , double>(yo, yi, xo, xdim,
xi_beg, xi_step,
method, offGrid); break;
case c32: approx1<cfloat , float >(yo, yi, xo, xdim,
xi_beg, xi_step,
method, offGrid); break;
case c64: approx1<cdouble, double>(yo, yi, xo, xdim,
xi_beg, xi_step,
method, offGrid); break;
default: TYPE_ERROR(1, yi_info.getType());
}
}
CATCHALL;
return AF_SUCCESS;
}
int num = (int)dims.elements();
vector<float> hb(num);
vector<float> hc(num);
b.host(&hb[0]);
c.host(&hc[0]);
for (int i = 0; i < num; i++) {
EXPECT_NEAR(hc[i], hb[i], 1e-7) << "at " << i;
}
}
TEST(Select, 4D)
{
dim4 dims(2, 3, 4, 2);
array cond = randu(dims) > 0.5;
array a = randu(dims);
array b = select(cond, a - a * 0.9, a);
array c = a - a * cond * 0.9;
int num = (int)dims.elements();
vector<float> hb(num);
vector<float> hc(num);
b.host(&hb[0]);
c.host(&hc[0]);
for (int i = 0; i < num; i++) {
EXPECT_NEAR(hc[i], hb[i], 1e-7) << "at " << i;
}
array randn(const dim4 &dims, const af::dtype type)
{
af_array res;
AF_THROW(af_randn(&res, dims.ndims(), dims.get(), type));
return array(res);
}
array range(const dim4 &dims, const int seq_dim, const af::dtype ty)
{
af_array out;
AF_THROW(af_range(&out, dims.ndims(), dims.get(), seq_dim, ty));
return array(out);
}
array moddims(const array& in, const dim4& dims)
{
return af::moddims(in, dims.ndims(), dims.get());
}
array iota(const dim4 &dims, const dim4 &tile_dims, const af::dtype ty)
{
af_array out;
AF_THROW(af_iota(&out, dims.ndims(), dims.get(), tile_dims.ndims(), tile_dims.get(), ty));
return array(out);
}
