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opencv/modules/core/test/test_intrin_utils.hpp
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HAN Liutong 1d9ca7160b Merge pull request #25796 from hanliutong:hfloat 
Use hfloat instead of __fp16. #25796

Related: #25743

Currently, the type for the half-precision floating point data in the OpenCV source code is `__fp16`, which is a unique(?) type supported by the ARM compiler. Other compilers have very limited support for `__fp16`, so in order to introduce more backends that support FP16 (such as RISC-V), we may need a the more general FP16 type.

In this patch, we use `hfloat` instead of `__fp16` in non-ARM code blocks, mainly affected parts are:
- `core/hal/intrin.hpp`: Type Traits, REG Traits and `vx_` interface.
- `core/hal/intrin_neon.hpp`: Universal Intrinsic API for FP16 type.
- `core/test/test_intrin_utils.hpp`: Usage of Univseral Intrinsic
- `core/include/opencv2/core/cvdef.h`: Definition of class `hfloat`

If I understand correctly, class `hfloat` acts as a wrapper around FP16 types in different platform (`__fp16` for ARM and `_Float16` for RISC-V). Any OpenCV generic interface/source code should use `hfloat`, while platform-specific FP16 types only used in macro-guarded code blocks.

/cc @fengyuentau  @mshabunin 

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [ ] I agree to contribute to the project under Apache 2 License.
- [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2024-07-07 11:38:02 +03:00

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
// This file is not standalone.
// It is included with these active namespaces:
//namespace opencv_test { namespace hal { namespace intrinXXX {
//CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN
void test_hal_intrin_uint8();
void test_hal_intrin_int8();
void test_hal_intrin_uint16();
void test_hal_intrin_int16();
void test_hal_intrin_uint32();
void test_hal_intrin_int32();
void test_hal_intrin_uint64();
void test_hal_intrin_int64();
void test_hal_intrin_float32();
void test_hal_intrin_float64();
void test_hal_intrin_float16();
#ifndef CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY
//==================================================================================================
template <typename R> struct Data
{
typedef typename VTraits<R>::lane_type LaneType;
typedef typename V_TypeTraits<LaneType>::int_type int_type;
Data()
{
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
d[i] = (LaneType)(i + 1);
}
Data(LaneType val)
{
fill(val);
}
Data(const R & r)
{
*this = r;
}
operator R () const
{
CV_Assert(VTraits<R>::vlanes() <= VTraits<R>::max_nlanes);
return vx_load(d);
}
Data<R> & operator=(const R & r)
{
v_store(d, r);
return *this;
}
template <typename T> Data<R> & operator*=(T m)
{
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
d[i] = (LaneType)(d[i] * m);
return *this;
}
template <typename T> Data<R> & operator+=(T m)
{
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
d[i] = (LaneType)(d[i] + m);
return *this;
}
void fill(LaneType val, int s, int c = VTraits<R>::vlanes())
{
for (int i = s; i < c; ++i)
d[i] = val;
}
void fill(LaneType val)
{
fill(val, 0);
}
void reverse()
{
for (int i = 0; i < VTraits<R>::vlanes() / 2; ++i)
std::swap(d[i], d[VTraits<R>::vlanes() - i - 1]);
}
const LaneType & operator[](int i) const
{
#if 0 // TODO: strange bug - AVX2 tests are failed with this
CV_CheckGE(i, 0, ""); CV_CheckLT(i, (int)VTraits<R>::vlanes(), "");
#else
CV_Assert(i >= 0 && i < VTraits<R>::max_nlanes);
#endif
return d[i];
}
LaneType & operator[](int i)
{
CV_CheckGE(i, 0, ""); CV_CheckLT(i, (int)VTraits<R>::max_nlanes, "");
return d[i];
}
int_type as_int(int i) const
{
CV_CheckGE(i, 0, ""); CV_CheckLT(i, (int)VTraits<R>::max_nlanes, "");
union
{
LaneType l;
int_type i;
} v;
v.l = d[i];
return v.i;
}
const LaneType * mid() const
{
return d + VTraits<R>::vlanes() / 2;
}
LaneType * mid()
{
return d + VTraits<R>::vlanes() / 2;
}
LaneType sum(int s, int c)
{
LaneType res = (LaneType)0;
for (int i = s; i < s + c; ++i)
res = (LaneType)(res + d[i]);
return res;
}
LaneType sum()
{
return sum(0, VTraits<R>::vlanes());
}
bool operator==(const Data<R> & other) const
{
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
if (d[i] != other.d[i])
return false;
return true;
}
void clear()
{
fill((LaneType)0);
}
bool isZero() const
{
return isValue(0);
}
bool isValue(uchar val) const
{
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
if (d[i] != val)
return false;
return true;
}
LaneType d[VTraits<R>::max_nlanes];
};
template<typename R> struct AlignedData
{
Data<R> CV_DECL_ALIGNED(sizeof(typename VTraits<R>::lane_type)*VTraits<R>::max_nlanes) a; // aligned
char dummy;
Data<R> u; // unaligned
};
template <typename R> std::ostream & operator<<(std::ostream & out, const Data<R> & d)
{
out << "{ ";
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
// out << std::hex << +V_TypeTraits<typename VTraits<R>::lane_type>::reinterpret_int(d.d[i]);
out << +d.d[i];
if (i + 1 < VTraits<R>::vlanes())
out << ", ";
}
out << " }";
return out;
}
template<typename T> static inline void EXPECT_COMPARE_EQ_(const T a, const T b)
{
EXPECT_EQ(a, b);
}
template<> inline void EXPECT_COMPARE_EQ_<float>(const float a, const float b)
{
EXPECT_FLOAT_EQ( a, b );
}
template<> inline void EXPECT_COMPARE_EQ_<double>(const double a, const double b)
{
EXPECT_DOUBLE_EQ( a, b );
}
#if CV_SIMD_FP16
template<> inline void EXPECT_COMPARE_EQ_<hfloat>(const hfloat a, const hfloat b)
{
EXPECT_LT(std::abs(float(a - b)), 0.126);
}
#endif
// pack functions do not do saturation when converting from 64-bit types
template<typename T, typename W>
inline T pack_saturate_cast(W a) { return saturate_cast<T>(a); }
template<>
inline int pack_saturate_cast<int, int64>(int64 a) { return static_cast<int>(a); }
template<>
inline unsigned pack_saturate_cast<unsigned, uint64>(uint64 a) { return static_cast<unsigned>(a); }
template<typename R> struct TheTest
{
typedef typename VTraits<R>::lane_type LaneType;
template <typename T1, typename T2>
static inline void EXPECT_COMPARE_EQ(const T1 a, const T2 b)
{
EXPECT_COMPARE_EQ_<LaneType>((LaneType)a, (LaneType)b);
}
TheTest & test_loadstore()
{
AlignedData<R> data;
AlignedData<R> out;
// check if addresses are aligned and unaligned respectively
EXPECT_EQ((size_t)0, (size_t)&data.a.d % (sizeof(typename VTraits<R>::lane_type) * VTraits<R>::vlanes()));
EXPECT_NE((size_t)0, (size_t)&data.u.d % (sizeof(typename VTraits<R>::lane_type) * VTraits<R>::vlanes()));
EXPECT_EQ((size_t)0, (size_t)&out.a.d % (sizeof(typename VTraits<R>::lane_type) * VTraits<R>::vlanes()));
EXPECT_NE((size_t)0, (size_t)&out.u.d % (sizeof(typename VTraits<R>::lane_type) * VTraits<R>::vlanes()));
// check some initialization methods
R r1 = data.a;
R r2 = vx_load(data.u.d);
R r3 = vx_load_aligned(data.a.d);
R r4(r2);
EXPECT_EQ(data.a[0], v_get0(r1));
EXPECT_EQ(data.u[0], v_get0(r2));
EXPECT_EQ(data.a[0], v_get0(r3));
EXPECT_EQ(data.u[0], v_get0(r4));
R r_low = vx_load_low((LaneType*)data.u.d);
EXPECT_EQ(data.u[0], v_get0(r_low));
v_store(out.u.d, r_low);
for (int i = 0; i < VTraits<R>::vlanes()/2; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ((LaneType)data.u[i], (LaneType)out.u[i]);
}
R r_low_align8byte = vx_load_low((LaneType*)((char*)data.u.d + (sizeof(typename VTraits<R>::lane_type) * VTraits<R>::vlanes() / 2)));
EXPECT_EQ(data.u[VTraits<R>::vlanes()/2], v_get0(r_low_align8byte));
v_store(out.u.d, r_low_align8byte);
for (int i = 0; i < VTraits<R>::vlanes()/2; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ((LaneType)data.u[i + VTraits<R>::vlanes()/2], (LaneType)out.u[i]);
}
// check some store methods
out.u.clear();
out.a.clear();
v_store(out.u.d, r1);
v_store_aligned(out.a.d, r2);
EXPECT_EQ(data.a, out.a);
EXPECT_EQ(data.u, out.u);
// check more store methods
Data<R> d, res(0);
R r5 = d;
v_store_high(res.mid(), r5);
v_store_low(res.d, r5);
EXPECT_EQ(d, res);
// check halves load correctness
res.clear();
R r6 = vx_load_halves(d.d, d.mid());
v_store(res.d, r6);
EXPECT_EQ(d, res);
// zero, all
Data<R> resZ, resV;
resZ.fill((LaneType)0);
resV.fill((LaneType)8);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ((LaneType)0, resZ[i]);
EXPECT_EQ((LaneType)8, resV[i]);
}
// reinterpret_as
v_uint8 vu8 = v_reinterpret_as_u8(r1); out.a.clear(); v_store((uchar*)out.a.d, vu8); EXPECT_EQ(data.a, out.a);
v_int8 vs8 = v_reinterpret_as_s8(r1); out.a.clear(); v_store((schar*)out.a.d, vs8); EXPECT_EQ(data.a, out.a);
v_uint16 vu16 = v_reinterpret_as_u16(r1); out.a.clear(); v_store((ushort*)out.a.d, vu16); EXPECT_EQ(data.a, out.a);
v_int16 vs16 = v_reinterpret_as_s16(r1); out.a.clear(); v_store((short*)out.a.d, vs16); EXPECT_EQ(data.a, out.a);
v_uint32 vu32 = v_reinterpret_as_u32(r1); out.a.clear(); v_store((unsigned*)out.a.d, vu32); EXPECT_EQ(data.a, out.a);
v_int32 vs32 = v_reinterpret_as_s32(r1); out.a.clear(); v_store((int*)out.a.d, vs32); EXPECT_EQ(data.a, out.a);
v_uint64 vu64 = v_reinterpret_as_u64(r1); out.a.clear(); v_store((uint64*)out.a.d, vu64); EXPECT_EQ(data.a, out.a);
v_int64 vs64 = v_reinterpret_as_s64(r1); out.a.clear(); v_store((int64*)out.a.d, vs64); EXPECT_EQ(data.a, out.a);
v_float32 vf32 = v_reinterpret_as_f32(r1); out.a.clear(); v_store((float*)out.a.d, vf32); EXPECT_EQ(data.a, out.a);
#if (CV_SIMD_64F || CV_SIMD_SCALABLE_64F)
v_float64 vf64 = v_reinterpret_as_f64(r1); out.a.clear(); v_store((double*)out.a.d, vf64); EXPECT_EQ(data.a, out.a);
#endif
#if CV_SIMD_WIDTH == 16
R setall_res1 = v_setall((LaneType)5);
R setall_res2 = v_setall<LaneType>(6);
#elif CV_SIMD_WIDTH == 32
R setall_res1 = v256_setall((LaneType)5);
R setall_res2 = v256_setall<LaneType>(6);
#elif CV_SIMD_WIDTH == 64
R setall_res1 = v512_setall((LaneType)5);
R setall_res2 = v512_setall<LaneType>(6);
#elif CV_SIMD_SCALABLE
R setall_res1 = v_setall((LaneType)5);
R setall_res2 = v_setall<LaneType>(6);
#else
#error "Configuration error"
#endif
#if CV_SIMD_WIDTH > 0
Data<R> setall_res1_; v_store(setall_res1_.d, setall_res1);
Data<R> setall_res2_; v_store(setall_res2_.d, setall_res2);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ((LaneType)5, setall_res1_[i]);
EXPECT_EQ((LaneType)6, setall_res2_[i]);
}
#endif
R vx_setall_res1 = vx_setall((LaneType)11);
R vx_setall_res2 = vx_setall<LaneType>(12);
Data<R> vx_setall_res1_; v_store(vx_setall_res1_.d, vx_setall_res1);
Data<R> vx_setall_res2_; v_store(vx_setall_res2_.d, vx_setall_res2);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ((LaneType)11, vx_setall_res1_[i]);
EXPECT_EQ((LaneType)12, vx_setall_res2_[i]);
}
#if CV_SIMD_WIDTH == 16
{
uint64 a = CV_BIG_INT(0x7fffffffffffffff);
uint64 b = (uint64)CV_BIG_INT(0xcfffffffffffffff);
v_uint64x2 uint64_vec(a, b);
EXPECT_EQ(a, v_get0(uint64_vec));
EXPECT_EQ(b, v_extract_n<1>(uint64_vec));
}
{
int64 a = CV_BIG_INT(0x7fffffffffffffff);
int64 b = CV_BIG_INT(-1);
v_int64x2 int64_vec(a, b);
EXPECT_EQ(a, v_get0(int64_vec));
EXPECT_EQ(b, v_extract_n<1>(int64_vec));
}
#endif
return *this;
}
TheTest & test_interleave()
{
Data<R> data1, data2, data3, data4;
data2 += (LaneType)20;
data3 += (LaneType)40;
data4 += (LaneType)60;
R a = data1, b = data2, c = data3;
R d = data1, e = data2, f = data3, g = data4;
LaneType buf3[VTraits<R>::max_nlanes * 3];
LaneType buf4[VTraits<R>::max_nlanes * 4];
v_store_interleave(buf3, a, b, c);
v_store_interleave(buf4, d, e, f, g);
Data<R> z((LaneType)0);
a = b = c = d = e = f = g = z;
v_load_deinterleave(buf3, a, b, c);
v_load_deinterleave(buf4, d, e, f, g);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(data1, Data<R>(a));
EXPECT_EQ(data2, Data<R>(b));
EXPECT_EQ(data3, Data<R>(c));
EXPECT_EQ(data1, Data<R>(d));
EXPECT_EQ(data2, Data<R>(e));
EXPECT_EQ(data3, Data<R>(f));
EXPECT_EQ(data4, Data<R>(g));
}
return *this;
}
TheTest & test_interleave_pq()
{
Data<R> dataA;
R a = dataA;
Data<R> resP = v_interleave_pairs(a);
Data<R> resQ = v_interleave_quads(a);
for (int i = 0; i < VTraits<R>::vlanes()/4; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(resP[4*i], dataA[4*i ]);
EXPECT_EQ(resP[4*i + 1], dataA[4*i+2]);
EXPECT_EQ(resP[4*i + 2], dataA[4*i+1]);
EXPECT_EQ(resP[4*i + 3], dataA[4*i+3]);
}
for (int i = 0; i < VTraits<R>::vlanes()/8; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(resQ[8*i], dataA[8*i ]);
EXPECT_EQ(resQ[8*i + 1], dataA[8*i+4]);
EXPECT_EQ(resQ[8*i + 2], dataA[8*i+1]);
EXPECT_EQ(resQ[8*i + 3], dataA[8*i+5]);
EXPECT_EQ(resQ[8*i + 4], dataA[8*i+2]);
EXPECT_EQ(resQ[8*i + 5], dataA[8*i+6]);
EXPECT_EQ(resQ[8*i + 6], dataA[8*i+3]);
EXPECT_EQ(resQ[8*i + 7], dataA[8*i+7]);
}
return *this;
}
// float32x4 only
TheTest & test_interleave_2channel()
{
Data<R> data1, data2;
data2 += 20;
R a = data1, b = data2;
LaneType buf2[VTraits<R>::max_nlanes * 2];
v_store_interleave(buf2, a, b);
Data<R> z(0);
a = b = z;
v_load_deinterleave(buf2, a, b);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(data1, Data<R>(a));
EXPECT_EQ(data2, Data<R>(b));
}
return *this;
}
// v_expand and v_load_expand
TheTest & test_expand()
{
typedef typename V_RegTraits<R>::w_reg Rx2;
Data<R> dataA;
R a = dataA;
Data<Rx2> resB = vx_load_expand(dataA.d);
Rx2 c, d, e, f;
v_expand(a, c, d);
e = v_expand_low(a);
f = v_expand_high(a);
Data<Rx2> resC = c, resD = d, resE = e, resF = f;
const int n = VTraits<Rx2>::vlanes();
for (int i = 0; i < n; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(dataA[i], resB[i]);
EXPECT_EQ(dataA[i], resC[i]);
EXPECT_EQ(dataA[i + n], resD[i]);
EXPECT_EQ(dataA[i], resE[i]);
EXPECT_EQ(dataA[i + n], resF[i]);
}
return *this;
}
TheTest & test_expand_q()
{
typedef typename V_RegTraits<R>::q_reg Rx4;
Data<R> data;
Data<Rx4> out = vx_load_expand_q(data.d);
const int n = VTraits<Rx4>::vlanes();
for (int i = 0; i < n; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(data[i], out[i]);
}
return *this;
}
TheTest & test_addsub()
{
Data<R> dataA, dataB, dataC;
dataB.reverse();
dataA[1] = static_cast<LaneType>(std::numeric_limits<LaneType>::max());
R a = dataA, b = dataB, c = dataC;
Data<R> resD = v_add(a, b), resE = v_add(a, b, c), resF = v_sub(a, b);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(saturate_cast<LaneType>(dataA[i] + dataB[i]), resD[i]);
EXPECT_EQ(saturate_cast<LaneType>(dataA[i] + dataB[i] + dataC[i]), resE[i]);
EXPECT_EQ(saturate_cast<LaneType>(dataA[i] - dataB[i]), resF[i]);
}
return *this;
}
TheTest & test_arithm_wrap()
{
Data<R> dataA, dataB;
dataB.reverse();
R a = dataA, b = dataB;
Data<R> resC = v_add_wrap(a, b),
resD = v_sub_wrap(a, b),
resE = v_mul_wrap(a, b);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ((LaneType)(dataA[i] + dataB[i]), resC[i]);
EXPECT_EQ((LaneType)(dataA[i] - dataB[i]), resD[i]);
EXPECT_EQ((LaneType)(dataA[i] * dataB[i]), resE[i]);
}
return *this;
}
TheTest & test_mul()
{
Data<R> dataA, dataB, dataC;
dataA[1] = static_cast<LaneType>(std::numeric_limits<LaneType>::max());
dataB.reverse();
R a = dataA, b = dataB, c = dataC;
Data<R> resD = v_mul(a, b);
Data<R> resE = v_mul(a, b, c);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(saturate_cast<LaneType>(dataA[i] * dataB[i]), resD[i]);
EXPECT_EQ(saturate_cast<LaneType>(dataA[i] * dataB[i] * dataC[i]), resE[i]);
}
return *this;
}
TheTest & test_div()
{
Data<R> dataA, dataB;
dataB.reverse();
R a = dataA, b = dataB;
Data<R> resC = v_div(a, b);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(dataA[i] / dataB[i], resC[i]);
}
return *this;
}
// Handle accuracy for fp16
TheTest & test_div_fp16()
{
#if CV_SIMD_FP16
Data<R> dataA, dataB;
dataB.reverse();
R a = dataA, b = dataB;
Data<R> resC = v_div(a, b);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_LT(std::abs(float((dataA[i] / dataB[i]) - resC[i])), 2e-4);
}
#else
std::cout << "SKIP: test_div_fp16, CV_SIMD_FP16 is not available" << std::endl;
#endif
return *this;
}
TheTest & test_mul_expand()
{
typedef typename V_RegTraits<R>::w_reg Rx2;
Data<R> dataA, dataB(2);
R a = dataA, b = dataB;
Rx2 c, d;
v_mul_expand(a, b, c, d);
Data<Rx2> resC = c, resD = d;
const int n = VTraits<R>::vlanes() / 2;
for (int i = 0; i < n; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ((typename VTraits<Rx2>::lane_type)dataA[i] * dataB[i], resC[i]);
EXPECT_EQ((typename VTraits<Rx2>::lane_type)dataA[i + n] * dataB[i + n], resD[i]);
}
return *this;
}
TheTest & test_mul_hi()
{
// typedef typename V_RegTraits<R>::w_reg Rx2;
Data<R> dataA, dataB(32767);
R a = dataA, b = dataB;
R c = v_mul_hi(a, b);
Data<R> resC = c;
const int n = VTraits<R>::vlanes() / 2;
for (int i = 0; i < n; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ((typename VTraits<R>::lane_type)((dataA[i] * dataB[i]) >> 16), resC[i]);
}
return *this;
}
TheTest & test_abs()
{
typedef typename V_RegTraits<R>::u_reg Ru;
typedef typename VTraits<Ru>::lane_type u_type;
typedef typename VTraits<R>::lane_type R_type;
Data<R> dataA, dataB(10);
R a = dataA, b = dataB;
a = v_sub(a, b);
Data<Ru> resC = v_abs(a);
auto R_type_lowest = std::numeric_limits<R_type>::lowest();
for (int i = 0; i < VTraits<Ru>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
R_type ssub = (dataA[i] - dataB[i]) < R_type_lowest ? R_type_lowest : dataA[i] - dataB[i];
EXPECT_EQ((u_type)std::abs(ssub), resC[i]);
}
return *this;
}
TheTest & test_abs_fp16()
{
typedef typename V_RegTraits<R>::u_reg Ru; // v_float16x8
typedef typename VTraits<Ru>::lane_type u_type; // hfloat
typedef typename VTraits<R>::lane_type R_type; // hfloat
Data<R> dataA, dataB((LaneType)10);
R a = dataA, b = dataB;
a = v_sub(a, b);
Data<Ru> resC = v_abs(a);
R_type R_type_lowest = R_type(-65504); // 0 11110 1111111111
for (int i = 0; i < VTraits<Ru>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
R_type ssub = (R_type)((dataA[i] - dataB[i]) < R_type_lowest ? R_type_lowest : dataA[i] - dataB[i]);
EXPECT_EQ((u_type)std::abs(ssub), resC[i]);
}
return *this;
}
template <int s>
TheTest & test_shift()
{
SCOPED_TRACE(s);
Data<R> dataA;
dataA[0] = static_cast<LaneType>(std::numeric_limits<LaneType>::max());
R a = dataA;
Data<R> resB = v_shl<s>(a), resC = v_shl<s>(a), resD = v_shr<s>(a), resE = v_shr<s>(a);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(static_cast<LaneType>(dataA[i] << s), resB[i]);
EXPECT_EQ(static_cast<LaneType>(dataA[i] << s), resC[i]);
EXPECT_EQ(static_cast<LaneType>(dataA[i] >> s), resD[i]);
EXPECT_EQ(static_cast<LaneType>(dataA[i] >> s), resE[i]);
}
return *this;
}
TheTest & test_cmp()
{
Data<R> dataA, dataB;
dataB.reverse();
dataB += 1;
R a = dataA, b = dataB;
Data<R> resC = (v_eq(a, b));
Data<R> resD = (v_ne(a, b));
Data<R> resE = (v_gt(a, b));
Data<R> resF = (v_ge(a, b));
Data<R> resG = (v_lt(a, b));
Data<R> resH = (v_le(a, b));
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(dataA[i] == dataB[i], resC[i] != 0);
EXPECT_EQ(dataA[i] != dataB[i], resD[i] != 0);
EXPECT_EQ(dataA[i] > dataB[i], resE[i] != 0);
EXPECT_EQ(dataA[i] >= dataB[i], resF[i] != 0);
EXPECT_EQ(dataA[i] < dataB[i], resG[i] != 0);
EXPECT_EQ(dataA[i] <= dataB[i], resH[i] != 0);
}
return *this;
}
TheTest & test_dotprod()
{
typedef typename V_RegTraits<R>::w_reg Rx2;
typedef typename VTraits<Rx2>::lane_type w_type;
Data<R> dataA, dataB;
dataA += std::numeric_limits<LaneType>::max() - VTraits<R>::vlanes();
dataB += std::numeric_limits<LaneType>::min() + VTraits<R>::vlanes();
R a = dataA, b = dataB;
Data<Rx2> dataC;
dataC += std::numeric_limits<w_type>::is_signed ?
std::numeric_limits<w_type>::min() :
std::numeric_limits<w_type>::max() - VTraits<R>::vlanes() * (dataB[0] + 1);
Rx2 c = dataC;
Data<Rx2> resD = v_dotprod(a, b),
resE = v_dotprod(a, b, c);
const int n = VTraits<R>::vlanes() / 2;
w_type sumAB = 0, sumABC = 0, tmp_sum;
for (int i = 0; i < n; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
tmp_sum = (w_type)dataA[i*2] * (w_type)dataB[i*2] +
(w_type)dataA[i*2 + 1] * (w_type)dataB[i*2 + 1];
sumAB += tmp_sum;
EXPECT_EQ(tmp_sum, resD[i]);
tmp_sum = tmp_sum + dataC[i];
sumABC += tmp_sum;
EXPECT_EQ(tmp_sum, resE[i]);
}
w_type resF = v_reduce_sum(v_dotprod_fast(a, b)),
resG = v_reduce_sum(v_dotprod_fast(a, b, c));
EXPECT_EQ(sumAB, resF);
EXPECT_EQ(sumABC, resG);
return *this;
}
TheTest & test_dotprod_expand()
{
typedef typename V_RegTraits<R>::q_reg Rx4;
typedef typename VTraits<Rx4>::lane_type l4_type;
Data<R> dataA, dataB;
dataA += std::numeric_limits<LaneType>::max() - VTraits<R>::vlanes();
dataB += std::numeric_limits<LaneType>::min() + VTraits<R>::vlanes();
R a = dataA, b = dataB;
Data<Rx4> dataC;
Rx4 c = dataC;
Data<Rx4> resD = v_dotprod_expand(a, b),
resE = v_dotprod_expand(a, b, c);
l4_type sumAB = 0, sumABC = 0, tmp_sum;
for (int i = 0; i < VTraits<Rx4>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
tmp_sum = (l4_type)dataA[i*4] * (l4_type)dataB[i*4] +
(l4_type)dataA[i*4 + 1] * (l4_type)dataB[i*4 + 1] +
(l4_type)dataA[i*4 + 2] * (l4_type)dataB[i*4 + 2] +
(l4_type)dataA[i*4 + 3] * (l4_type)dataB[i*4 + 3];
sumAB += tmp_sum;
EXPECT_EQ(tmp_sum, resD[i]);
tmp_sum = tmp_sum + dataC[i];
sumABC += tmp_sum;
EXPECT_EQ(tmp_sum, resE[i]);
}
l4_type resF = v_reduce_sum(v_dotprod_expand_fast(a, b)),
resG = v_reduce_sum(v_dotprod_expand_fast(a, b, c));
EXPECT_EQ(sumAB, resF);
EXPECT_EQ(sumABC, resG);
return *this;
}
TheTest & test_dotprod_expand_f64()
{
#if (CV_SIMD_64F || CV_SIMD_SCALABLE_64F)
Data<R> dataA, dataB;
dataA += std::numeric_limits<LaneType>::max() - VTraits<R>::vlanes();
dataB += std::numeric_limits<LaneType>::min();
R a = dataA, b = dataB;
Data<v_float64> dataC;
v_float64 c = dataC;
Data<v_float64> resA = v_dotprod_expand(a, a),
resB = v_dotprod_expand(b, b),
resC = v_dotprod_expand(a, b, c);
const int n = VTraits<R>::vlanes() / 2;
for (int i = 0; i < n; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_COMPARE_EQ((double)dataA[i*2] * (double)dataA[i*2] +
(double)dataA[i*2 + 1] * (double)dataA[i*2 + 1], resA[i]);
EXPECT_COMPARE_EQ((double)dataB[i*2] * (double)dataB[i*2] +
(double)dataB[i*2 + 1] * (double)dataB[i*2 + 1], resB[i]);
EXPECT_COMPARE_EQ((double)dataA[i*2] * (double)dataB[i*2] +
(double)dataA[i*2 + 1] * (double)dataB[i*2 + 1] + dataC[i], resC[i]);
}
#endif
return *this;
}
TheTest & test_logic()
{
Data<R> dataA, dataB(2);
R a = dataA, b = dataB;
Data<R> resC = v_and(a, b), resD = v_or(a, b), resE = v_xor(a, b), resF = v_not(a);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(dataA[i] & dataB[i], resC[i]);
EXPECT_EQ(dataA[i] | dataB[i], resD[i]);
EXPECT_EQ(dataA[i] ^ dataB[i], resE[i]);
EXPECT_EQ((LaneType)~dataA[i], resF[i]);
}
return *this;
}
TheTest & test_sqrt_abs()
{
Data<R> dataA, dataD;
dataD *= -1.0;
R a = dataA, d = dataD;
Data<R> resB = v_sqrt(a), resC = v_invsqrt(a), resE = v_abs(d);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_COMPARE_EQ((float)std::sqrt(dataA[i]), (float)resB[i]);
EXPECT_COMPARE_EQ((float)(1/std::sqrt(dataA[i])), (float)resC[i]);
EXPECT_COMPARE_EQ((float)abs(dataA[i]), (float)resE[i]);
}
return *this;
}
TheTest & test_min_max()
{
Data<R> dataA, dataB;
dataB.reverse();
R a = dataA, b = dataB;
Data<R> resC = v_min(a, b), resD = v_max(a, b);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(std::min(dataA[i], dataB[i]), resC[i]);
EXPECT_EQ(std::max(dataA[i], dataB[i]), resD[i]);
}
return *this;
}
TheTest & test_popcount()
{
typedef typename V_RegTraits<R>::u_reg Ru;
static unsigned popcountTable[] = {
0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, //0x00-0x0f
1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, //0x10-0x1f
1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, //0x20-0x2f
2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, //0x30-0x3f
1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, //0x40-0x4f
2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, //0x50-0x5f
2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, //0x60-0x6f
3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, //0x70-0x7f
1 //0x80
};
Data<R> dataA;
R a = dataA;
Data<Ru> resB = v_popcount(a);
for (int i = 0; i < VTraits<Ru>::vlanes(); ++i)
EXPECT_EQ(popcountTable[i + 1], resB[i]);
return *this;
}
TheTest & test_absdiff()
{
typedef typename V_RegTraits<R>::u_reg Ru;
typedef typename VTraits<Ru>::lane_type u_type;
Data<R> dataA(std::numeric_limits<LaneType>::max()),
dataB(std::numeric_limits<LaneType>::min());
dataA[0] = (LaneType)-1;
dataB[0] = 1;
dataA[1] = 2;
dataB[1] = (LaneType)-2;
R a = dataA, b = dataB;
Data<Ru> resC = v_absdiff(a, b);
const u_type mask = std::numeric_limits<LaneType>::is_signed ? (u_type)(1 << (sizeof(u_type)*8 - 1)) : 0;
for (int i = 0; i < VTraits<Ru>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
u_type uA = dataA[i] ^ mask;
u_type uB = dataB[i] ^ mask;
EXPECT_EQ(uA > uB ? uA - uB : uB - uA, resC[i]);
}
return *this;
}
TheTest & test_float_absdiff()
{
Data<R> dataA(std::numeric_limits<LaneType>::max()),
dataB(std::numeric_limits<LaneType>::min());
dataA[0] = (LaneType)-1;
dataB[0] = (LaneType)1;
dataA[1] = (LaneType)2;
dataB[1] = (LaneType)-2;
R a = dataA, b = dataB;
Data<R> resC = v_absdiff(a, b);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(dataA[i] > dataB[i] ? dataA[i] - dataB[i] : dataB[i] - dataA[i], resC[i]);
}
return *this;
}
TheTest & test_absdiffs()
{
Data<R> dataA(std::numeric_limits<LaneType>::max()),
dataB(std::numeric_limits<LaneType>::min());
dataA[0] = (LaneType)-1;
dataB[0] = 1;
dataA[1] = 2;
dataB[1] = (LaneType)-2;
R a = dataA, b = dataB;
Data<R> resC = v_absdiffs(a, b);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
EXPECT_EQ(saturate_cast<LaneType>(std::abs(dataA[i] - dataB[i])), resC[i]);
}
return *this;
}
TheTest & test_reduce()
{
Data<R> dataA;
LaneType min = (LaneType)VTraits<R>::vlanes(), max = 0;
int sum = 0;
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
min = std::min<LaneType>(min, dataA[i]);
max = std::max<LaneType>(max, dataA[i]);
sum += (int)(dataA[i]); // To prevent a constant overflow with int8
}
R a = dataA;
EXPECT_EQ((LaneType)min, (LaneType)v_reduce_min(a));
EXPECT_EQ((LaneType)max, (LaneType)v_reduce_max(a));
EXPECT_EQ((int)(sum), (int)v_reduce_sum(a));
dataA[0] += (LaneType)VTraits<R>::vlanes();
R an = dataA;
min = (LaneType)VTraits<R>::vlanes();
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
min = std::min<LaneType>(min, dataA[i]);
}
EXPECT_EQ((LaneType)min, (LaneType)v_reduce_min(an));
return *this;
}
TheTest & test_reduce_sad()
{
Data<R> dataA, dataB((LaneType)VTraits<R>::vlanes() /2);
R a = dataA;
R b = dataB;
uint sum = 0;
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
sum += std::abs(int(dataA[i] - dataB[i]));
}
EXPECT_EQ(sum, v_reduce_sad(a, b));
return *this;
}
TheTest & test_mask()
{
typedef typename V_RegTraits<R>::int_reg int_reg;
typedef typename V_RegTraits<int_reg>::u_reg uint_reg;
typedef typename VTraits<int_reg>::lane_type int_type;
typedef typename VTraits<uint_reg>::lane_type uint_type;
Data<R> dataA, dataB((LaneType)0), dataC, dataD((LaneType)1), dataE((LaneType)2);
dataA[0] = (LaneType)std::numeric_limits<int_type>::max();
dataA[1] = (LaneType)(dataA[1] * (LaneType)-1);
union
{
LaneType l;
uint_type ui;
}
all1s;
all1s.ui = (uint_type)-1;
LaneType mask_one = all1s.l;
dataB[VTraits<R>::vlanes() - 1] = mask_one;
R l = dataB;
dataB[1] = mask_one;
dataB[VTraits<R>::vlanes() / 2] = mask_one;
dataC *= (LaneType)-1;
R a = dataA, b = dataB, c = dataC, d = dataD, e = dataE;
dataC[VTraits<R>::vlanes() - 1] = (LaneType)0;
R nl = dataC;
EXPECT_EQ(2, v_signmask(a));
#if (CV_SIMD_WIDTH <= 32) && (!CV_SIMD_SCALABLE)
EXPECT_EQ(2 | (1 << (VTraits<R>::vlanes() / 2)) | (1 << (VTraits<R>::vlanes() - 1)), v_signmask(b));
#endif
EXPECT_EQ(false, v_check_all(a));
EXPECT_EQ(false, v_check_all(b));
EXPECT_EQ(true, v_check_all(c));
EXPECT_EQ(false, v_check_all(nl));
EXPECT_EQ(true, v_check_any(a));
EXPECT_EQ(true, v_check_any(b));
EXPECT_EQ(true, v_check_any(c));
EXPECT_EQ(true, v_check_any(l));
R f = v_select(b, d, e);
Data<R> resF = f;
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
int_type m2 = dataB.as_int(i);
EXPECT_EQ((dataD.as_int(i) & m2) | (dataE.as_int(i) & ~m2), resF.as_int(i));
}
return *this;
}
template <int s>
TheTest & test_pack()
{
SCOPED_TRACE(s);
typedef typename V_RegTraits<R>::w_reg Rx2;
typedef typename VTraits<Rx2>::lane_type w_type;
Data<Rx2> dataA, dataB;
dataA += std::numeric_limits<LaneType>::is_signed ? -10 : 10;
dataB *= 10;
dataB[0] = static_cast<w_type>(std::numeric_limits<LaneType>::max()) + 17; // to check saturation
Rx2 a = dataA, b = dataB;
Data<R> resC = v_pack(a, b);
Data<R> resD = v_rshr_pack<s>(a, b);
Data<R> resE(0);
v_pack_store(resE.d, b);
Data<R> resF(0);
v_rshr_pack_store<s>(resF.d, b);
const int n = VTraits<Rx2>::vlanes();
const w_type add = (w_type)1 << (s - 1);
for (int i = 0; i < n; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(pack_saturate_cast<LaneType>(dataA[i]), resC[i]);
EXPECT_EQ(pack_saturate_cast<LaneType>(dataB[i]), resC[i + n]);
EXPECT_EQ(pack_saturate_cast<LaneType>((dataA[i] + add) >> s), resD[i]);
EXPECT_EQ(pack_saturate_cast<LaneType>((dataB[i] + add) >> s), resD[i + n]);
EXPECT_EQ(pack_saturate_cast<LaneType>(dataB[i]), resE[i]);
EXPECT_EQ((LaneType)0, resE[i + n]);
EXPECT_EQ(pack_saturate_cast<LaneType>((dataB[i] + add) >> s), resF[i]);
EXPECT_EQ((LaneType)0, resF[i + n]);
}
return *this;
}
TheTest & test_pack_triplets()
{
Data<R> dataA;
R a = dataA;
Data<R> res = v_pack_triplets(a);
for (int i = 0; i < VTraits<R>::vlanes()/4; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(dataA[4*i], res[3*i]);
EXPECT_EQ(dataA[4*i+1], res[3*i+1]);
EXPECT_EQ(dataA[4*i+2], res[3*i+2]);
}
return *this;
}
template <int s>
TheTest & test_pack_u()
{
SCOPED_TRACE(s);
//typedef typename V_RegTraits<LaneType>::w_type LaneType_w;
typedef typename V_RegTraits<R>::w_reg R2;
typedef typename V_RegTraits<R2>::int_reg Ri2;
typedef typename VTraits<Ri2>::lane_type w_type;
Data<Ri2> dataA, dataB;
dataA += -10;
dataB *= 10;
dataB[0] = static_cast<w_type>(std::numeric_limits<LaneType>::max()) + 17; // to check saturation
Ri2 a = dataA, b = dataB;
Data<R> resC = v_pack_u(a, b);
Data<R> resD = v_rshr_pack_u<s>(a, b);
Data<R> resE(0);
v_pack_u_store(resE.d, b);
Data<R> resF(0);
v_rshr_pack_u_store<s>(resF.d, b);
const int n = VTraits<Ri2>::vlanes();
const w_type add = (w_type)1 << (s - 1);
for (int i = 0; i < n; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(pack_saturate_cast<LaneType>(dataA[i]), resC[i]);
EXPECT_EQ(pack_saturate_cast<LaneType>(dataB[i]), resC[i + n]);
EXPECT_EQ(pack_saturate_cast<LaneType>((dataA[i] + add) >> s), resD[i]);
EXPECT_EQ(pack_saturate_cast<LaneType>((dataB[i] + add) >> s), resD[i + n]);
EXPECT_EQ(pack_saturate_cast<LaneType>(dataB[i]), resE[i]);
EXPECT_EQ((LaneType)0, resE[i + n]);
EXPECT_EQ(pack_saturate_cast<LaneType>((dataB[i] + add) >> s), resF[i]);
EXPECT_EQ((LaneType)0, resF[i + n]);
}
return *this;
}
// v_uint8 only
TheTest & test_pack_b()
{
// 16-bit
Data<R> dataA, dataB;
dataB.fill(0, VTraits<R>::vlanes() / 2);
R a = dataA, b = dataB;
Data<R> maskA = v_eq(a, b), maskB = v_ne(a, b);
a = maskA; b = maskB;
Data<R> res = v_pack_b(v_reinterpret_as_u16(a), v_reinterpret_as_u16(b));
for (int i = 0; i < VTraits<v_uint16>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(maskA[i * 2], res[i]);
EXPECT_EQ(maskB[i * 2], res[i + VTraits<v_uint16>::vlanes()]);
}
// 32-bit
Data<R> dataC, dataD;
dataD.fill(0, VTraits<R>::vlanes() / 2);
R c = dataC, d = dataD;
Data<R> maskC = v_eq(c, d), maskD = v_ne(c, d);
c = maskC; d = maskD;
res = v_pack_b
(
v_reinterpret_as_u32(a), v_reinterpret_as_u32(b),
v_reinterpret_as_u32(c), v_reinterpret_as_u32(d)
);
for (int i = 0; i < VTraits<v_uint32>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(maskA[i * 4], res[i]);
EXPECT_EQ(maskB[i * 4], res[i + VTraits<v_uint32>::vlanes()]);
EXPECT_EQ(maskC[i * 4], res[i + VTraits<v_uint32>::vlanes() * 2]);
EXPECT_EQ(maskD[i * 4], res[i + VTraits<v_uint32>::vlanes() * 3]);
}
// 64-bit
Data<R> dataE, dataF, dataG(0), dataH(0xFF);
dataF.fill(0, VTraits<R>::vlanes() / 2);
R e = dataE, f = dataF, g = dataG, h = dataH;
Data<R> maskE = v_eq(e, f), maskF = v_ne(e, f);
e = maskE; f = maskF;
res = v_pack_b
(
v_reinterpret_as_u64(a), v_reinterpret_as_u64(b),
v_reinterpret_as_u64(c), v_reinterpret_as_u64(d),
v_reinterpret_as_u64(e), v_reinterpret_as_u64(f),
v_reinterpret_as_u64(g), v_reinterpret_as_u64(h)
);
for (int i = 0; i < VTraits<v_uint64>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(maskA[i * 8], res[i]);
EXPECT_EQ(maskB[i * 8], res[i + VTraits<v_uint64>::vlanes()]);
EXPECT_EQ(maskC[i * 8], res[i + VTraits<v_uint64>::vlanes() * 2]);
EXPECT_EQ(maskD[i * 8], res[i + VTraits<v_uint64>::vlanes() * 3]);
EXPECT_EQ(maskE[i * 8], res[i + VTraits<v_uint64>::vlanes() * 4]);
EXPECT_EQ(maskF[i * 8], res[i + VTraits<v_uint64>::vlanes() * 5]);
EXPECT_EQ(dataG[i * 8], res[i + VTraits<v_uint64>::vlanes() * 6]);
EXPECT_EQ(dataH[i * 8], res[i + VTraits<v_uint64>::vlanes() * 7]);
}
return *this;
}
TheTest & test_unpack()
{
Data<R> dataA, dataB;
dataB *= 10;
R a = dataA, b = dataB;
R c, d, e, f, lo, hi;
v_zip(a, b, c, d);
v_recombine(a, b, e, f);
lo = v_combine_low(a, b);
hi = v_combine_high(a, b);
Data<R> resC = c, resD = d, resE = e, resF = f, resLo = lo, resHi = hi;
const int n = VTraits<R>::vlanes()/2;
for (int i = 0; i < n; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(dataA[i], resC[i*2]);
EXPECT_EQ(dataB[i], resC[i*2+1]);
EXPECT_EQ(dataA[i+n], resD[i*2]);
EXPECT_EQ(dataB[i+n], resD[i*2+1]);
EXPECT_EQ(dataA[i], resE[i]);
EXPECT_EQ(dataB[i], resE[i+n]);
EXPECT_EQ(dataA[i+n], resF[i]);
EXPECT_EQ(dataB[i+n], resF[i+n]);
EXPECT_EQ(dataA[i], resLo[i]);
EXPECT_EQ(dataB[i], resLo[i+n]);
EXPECT_EQ(dataA[i+n], resHi[i]);
EXPECT_EQ(dataB[i+n], resHi[i+n]);
}
return *this;
}
TheTest & test_reverse()
{
Data<R> dataA;
R a = dataA;
Data<R> resB = v_reverse(a);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(dataA[VTraits<R>::vlanes() - i - 1], resB[i]);
}
return *this;
}
template<int s>
TheTest & test_extract()
{
SCOPED_TRACE(s);
Data<R> dataA, dataB;
dataB *= 10;
R a = dataA, b = dataB;
Data<R> resC = v_extract<s>(a, b);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
if (i + s >= VTraits<R>::vlanes())
EXPECT_EQ(dataB[i - VTraits<R>::vlanes() + s], resC[i]);
else
EXPECT_EQ(dataA[i + s], resC[i]);
}
return *this;
}
template<int s>
TheTest & test_rotate()
{
SCOPED_TRACE(s);
Data<R> dataA, dataB;
dataB *= 10;
R a = dataA, b = dataB;
Data<R> resC = v_rotate_right<s>(a);
Data<R> resD = v_rotate_right<s>(a, b);
Data<R> resE = v_rotate_left<s>(a);
Data<R> resF = v_rotate_left<s>(a, b);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
if (i + s >= VTraits<R>::vlanes())
{
EXPECT_EQ((LaneType)0, resC[i]);
EXPECT_EQ(dataB[i - VTraits<R>::vlanes() + s], resD[i]);
EXPECT_EQ((LaneType)0, resE[i - VTraits<R>::vlanes() + s]);
EXPECT_EQ(dataB[i], resF[i - VTraits<R>::vlanes() + s]);
}
else
{
EXPECT_EQ(dataA[i + s], resC[i]);
EXPECT_EQ(dataA[i + s], resD[i]);
EXPECT_EQ(dataA[i], resE[i + s]);
EXPECT_EQ(dataA[i], resF[i + s]);
}
}
return *this;
}
template<int s>
TheTest & test_extract_n()
{
SCOPED_TRACE(s);
Data<R> dataA;
LaneType test_value = (LaneType)(s + 50);
dataA[s] = test_value;
R a = dataA;
LaneType res = v_extract_n<s>(a);
EXPECT_EQ(test_value, res);
return *this;
}
TheTest & test_extract_highest()
{
Data<R> dataA;
LaneType test_value = (LaneType)(VTraits<R>::vlanes()-1 + 50);
dataA[VTraits<R>::vlanes()-1] = test_value;
R a = dataA;
LaneType res = v_extract_highest(a);
EXPECT_EQ(test_value, res);
return *this;
}
template<int s>
TheTest & test_broadcast_element()
{
SCOPED_TRACE(s);
Data<R> dataA;
LaneType test_value = (LaneType)(s + 50);
dataA[s] = test_value;
R a = dataA;
Data<R> res = v_broadcast_element<s>(a);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(i);
EXPECT_EQ(test_value, res[i]);
}
return *this;
}
TheTest & test_broadcast_highest()
{
Data<R> dataA;
LaneType test_value = (LaneType)(VTraits<R>::vlanes()-1 + 50);
dataA[VTraits<R>::vlanes()-1] = test_value;
R a = dataA;
Data<R> res = v_broadcast_highest(a);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(i);
EXPECT_EQ(test_value, res[i]);
}
return *this;
}
TheTest & test_float_math()
{
typedef typename V_RegTraits<R>::round_reg Ri;
Data<R> data1, data1_border, data2, data3;
// See https://github.com/opencv/opencv/issues/24213
data1_border *= 0.5;
data1 *= 1.1;
data2 += 10;
R a1 = data1, a1_border = data1_border, a2 = data2, a3 = data3;
Data<Ri> resB = v_round(a1),
resB_border = v_round(a1_border),
resC = v_trunc(a1),
resD = v_floor(a1),
resE = v_ceil(a1);
Data<R> resF = v_magnitude(a1, a2),
resG = v_sqr_magnitude(a1, a2),
resH = v_muladd(a1, a2, a3);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ(cvRound(data1[i]), resB[i]);
EXPECT_EQ(cvRound(data1_border[i]), resB_border[i]);
EXPECT_EQ((typename VTraits<Ri>::lane_type)data1[i], resC[i]);
EXPECT_EQ(cvFloor(data1[i]), resD[i]);
EXPECT_EQ(cvCeil(data1[i]), resE[i]);
EXPECT_COMPARE_EQ(std::sqrt(data1[i]*data1[i] + data2[i]*data2[i]), resF[i]);
EXPECT_COMPARE_EQ(data1[i]*data1[i] + data2[i]*data2[i], resG[i]);
EXPECT_COMPARE_EQ(data1[i]*data2[i] + data3[i], resH[i]);
}
return *this;
}
#if (CV_SIMD_64F || CV_SIMD_SCALABLE_64F)
TheTest & test_round_pair_f64()
{
typedef typename V_RegTraits<R>::round_reg Ri;
Data<R> data1, data1_border, data2;
// See https://github.com/opencv/opencv/issues/24213
// https://github.com/opencv/opencv/issues/24163
// https://github.com/opencv/opencv/pull/24271
data1_border *= 0.5;
data1 *= 1.1;
data2 += 10;
R a1 = data1, a1_border = data1_border, a2 = data2;
Data<Ri> resA = v_round(a1, a1),
resB = v_round(a1_border, a1_border),
resC = v_round(a2, a2);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
EXPECT_EQ(cvRound(data1[i]), resA[i]);
EXPECT_EQ(cvRound(data1_border[i]), resB[i]);
EXPECT_EQ(cvRound(data2[i]), resC[i]);
}
return *this;
}
#endif
TheTest & test_float_cvt32()
{
typedef v_float32 Rt;
Data<R> dataA;
dataA *= 1.1;
R a = dataA;
Rt b = v_cvt_f32(a);
Data<Rt> resB = b;
int n = std::min<int>(VTraits<Rt>::vlanes(), VTraits<R>::vlanes());
for (int i = 0; i < n; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ((typename VTraits<Rt>::lane_type)dataA[i], resB[i]);
}
return *this;
}
TheTest & test_float_cvt64()
{
#if (CV_SIMD_64F || CV_SIMD_SCALABLE_64F)
typedef v_float64 Rt;
Data<R> dataA;
dataA *= 1.1;
R a = dataA;
Rt b = v_cvt_f64(a);
Rt c = v_cvt_f64_high(a);
Data<Rt> resB = b;
Data<Rt> resC = c;
int n = std::min<int>(VTraits<Rt>::vlanes(), VTraits<R>::vlanes());
for (int i = 0; i < n; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ((typename VTraits<Rt>::lane_type)dataA[i], resB[i]);
}
for (int i = 0; i < n; ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ((typename VTraits<Rt>::lane_type)dataA[i+n], resC[i]);
}
#endif
return *this;
}
TheTest & test_cvt64_double()
{
#if (CV_SIMD_64F || CV_SIMD_SCALABLE_64F)
Data<R> dataA(std::numeric_limits<LaneType>::max()),
dataB(std::numeric_limits<LaneType>::min());
dataB += VTraits<R>::vlanes();
R a = dataA, b = dataB;
v_float64 c = v_cvt_f64(a), d = v_cvt_f64(b);
Data<v_float64> resC = c;
Data<v_float64> resD = d;
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_EQ((double)dataA[i], resC[i]);
EXPECT_EQ((double)dataB[i], resD[i]);
}
#endif
return *this;
}
TheTest & test_matmul()
{
Data<R> dataV, dataA, dataB, dataC, dataD;
dataB.reverse();
dataC += 2;
dataD *= 0.3;
R v = dataV, a = dataA, b = dataB, c = dataC, d = dataD;
Data<R> res = v_matmul(v, a, b, c, d);
// for (int i = 0; i < VTraits<R>::vlanes(); i += 4)
// {
int i = 0;
for (int j = i; j < i + 4; ++j)
{
SCOPED_TRACE(cv::format("i=%d j=%d", i, j));
LaneType val = dataV[i] * dataA[j]
+ dataV[i + 1] * dataB[j]
+ dataV[i + 2] * dataC[j]
+ dataV[i + 3] * dataD[j];
EXPECT_COMPARE_EQ(val, res[j]);
}
// }
Data<R> resAdd = v_matmuladd(v, a, b, c, d);
// for (int i = 0; i < VTraits<R>::vlanes(); i += 4)
// {
i = 0;
for (int j = i; j < i + 4; ++j)
{
SCOPED_TRACE(cv::format("i=%d j=%d", i, j));
LaneType val = dataV[i] * dataA[j]
+ dataV[i + 1] * dataB[j]
+ dataV[i + 2] * dataC[j]
+ dataD[j];
EXPECT_COMPARE_EQ(val, resAdd[j]);
}
// }
return *this;
}
TheTest & test_matmul_fp16()
{
#if CV_SIMD_FP16
Data<R> dataV, data0, data1, data2, data3, data4, data5, data6, data7;
data1.reverse();
data2 += 2;
data3 *= 0.3;
data5.reverse();
data6 += 1;
data7 *= 0.4;
R v = dataV, m0 = data0, m1 = data1, m2 = data2, m3 = data3, m4 = data4, m5 = data5, m6 = data6, m7 = data7;
Data<R> res = v_matmul(v, m0, m1, m2, m3, m4, m5, m6, m7);
int i = 0;
for (int j = i; j < i + 8; ++j) {
SCOPED_TRACE(cv::format("i=%d j=%d", i, j));
LaneType val = (LaneType)(
dataV[i] * data0[j] +
dataV[i + 1] * data1[j] +
dataV[i + 2] * data2[j] +
dataV[i + 3] * data3[j] +
dataV[i + 4] * data4[j] +
dataV[i + 5] * data5[j] +
dataV[i + 6] * data6[j] +
dataV[i + 7] * data7[j]);
EXPECT_COMPARE_EQ(val, res[j]);
}
Data<R> resAdd = v_matmuladd(v, m0, m1, m2, m3, m4, m5, m6, m7);
i = 0;
for (int j = i; j < i + 8; ++j) {
SCOPED_TRACE(cv::format("i=%d j=%d", i, j));
LaneType val = (LaneType)(
dataV[i] * data0[j] +
dataV[i + 1] * data1[j] +
dataV[i + 2] * data2[j] +
dataV[i + 3] * data3[j] +
dataV[i + 4] * data4[j] +
dataV[i + 5] * data5[j] +
dataV[i + 6] * data6[j] +
data7[j]);
EXPECT_COMPARE_EQ(val, resAdd[j]);
}
#else
std::cout << "SKIP: test_matmul_fp16, CV_SIMD_FP16 is not available" << std::endl;
#endif
return *this;
}
TheTest & test_transpose()
{
Data<R> dataA, dataB, dataC, dataD;
dataB *= 5;
dataC *= 10;
dataD *= 15;
R a = dataA, b = dataB, c = dataC, d = dataD;
R e, f, g, h;
v_transpose4x4(a, b, c, d,
e, f, g, h);
// Data<R> res[4] = {e, f, g, h}; // Generates incorrect data in certain RVV case.
Data<R> res0 = e, res1 = f, res2 = g, res3 = h;
EXPECT_EQ(dataA[0], res0[0]);
EXPECT_EQ(dataB[0], res0[1]);
EXPECT_EQ(dataC[0], res0[2]);
EXPECT_EQ(dataD[0], res0[3]);
EXPECT_EQ(dataA[1], res1[0]);
EXPECT_EQ(dataB[1], res1[1]);
EXPECT_EQ(dataC[1], res1[2]);
EXPECT_EQ(dataD[1], res1[3]);
EXPECT_EQ(dataA[2], res2[0]);
EXPECT_EQ(dataB[2], res2[1]);
EXPECT_EQ(dataC[2], res2[2]);
EXPECT_EQ(dataD[2], res2[3]);
EXPECT_EQ(dataA[3], res3[0]);
EXPECT_EQ(dataB[3], res3[1]);
EXPECT_EQ(dataC[3], res3[2]);
EXPECT_EQ(dataD[3], res3[3]);
return *this;
}
TheTest & test_transpose8x8_fp16()
{
#if CV_SIMD_FP16
Data<R> dataA0, dataA1, dataA2, dataA3, dataA4, dataA5, dataA6, dataA7;
dataA1 *= 2;
dataA2 *= 4;
dataA3 *= 6;
dataA4 *= 8;
dataA5 *= 10;
dataA6 *= 12;
dataA7 *= 14;
R a0 = dataA0, a1 = dataA1, a2 = dataA2, a3 = dataA3,
a4 = dataA4, a5 = dataA5, a6 = dataA6, a7 = dataA7;
R b0, b1, b2, b3, b4, b5, b6, b7;
v_transpose8x8(a0, a1, a2, a3, a4, a5, a6, a7,
b0, b1, b2, b3, b4, b5, b6, b7);
Data<R> res0 = b0, res1 = b1, res2 = b2, res3 = b3, res4 = b4, res5 = b5, res6 = b6, res7 = b7;
const Data<R> ref[] = {dataA0, dataA1, dataA2, dataA3, dataA4, dataA5, dataA6, dataA7};
const Data<R> res[] = { res0, res1, res2, res3, res4, res5, res6, res7};
for (int i = 0; i < 8; i++) {
for (int j = 0; j < 8; j++) {
SCOPED_TRACE(cv::format("i=%d j=%d", i, j));
EXPECT_EQ(ref[i][j], res[j][i]);
}
}
#else
std::cout << "SKIP: test_transpose8x8_fp16, CV_SIMD_FP16 is not available" << std::endl;
#endif
return *this;
}
TheTest & test_reduce_sum4()
{
Data<R> dataA, dataB, dataC, dataD;
dataB *= 0.01f;
dataC *= 0.001f;
dataD *= 0.002f;
R a = dataA, b = dataB, c = dataC, d = dataD;
Data<R> res = v_reduce_sum4(a, b, c, d);
for (int i = 0; i < VTraits<R>::vlanes(); i += 4)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_COMPARE_EQ(dataA.sum(i, 4), res[i]);
EXPECT_COMPARE_EQ(dataB.sum(i, 4), res[i + 1]);
EXPECT_COMPARE_EQ(dataC.sum(i, 4), res[i + 2]);
EXPECT_COMPARE_EQ(dataD.sum(i, 4), res[i + 3]);
}
return *this;
}
TheTest & test_reduce_sum8()
{
#if CV_SIMD_FP16
Data<R> dataA, dataB, dataC, dataD, dataW, dataX, dataY, dataZ;
dataB *= 0.01f;
dataC *= 0.001f;
dataD *= 0.002f;
dataW += 0.1f;
dataX *= 0.2f;
dataY += 1;
dataZ *= 2;
R a = dataA, b = dataB, c = dataC, d = dataD,
w = dataW, x = dataX, y = dataY, z = dataZ;
Data<R> res = v_reduce_sum8(a, b, c, d, w, x, y, z);
for (int i = 0; i < VTraits<R>::vlanes(); i += 8)
{
SCOPED_TRACE(cv::format("i=%d", i));
EXPECT_COMPARE_EQ(dataA.sum(i, 8), res[i]);
EXPECT_COMPARE_EQ(dataB.sum(i, 8), res[i + 1]);
EXPECT_COMPARE_EQ(dataC.sum(i, 8), res[i + 2]);
EXPECT_COMPARE_EQ(dataD.sum(i, 8), res[i + 3]);
EXPECT_COMPARE_EQ(dataW.sum(i, 8), res[i + 4]);
EXPECT_COMPARE_EQ(dataX.sum(i, 8), res[i + 5]);
EXPECT_COMPARE_EQ(dataY.sum(i, 8), res[i + 6]);
EXPECT_COMPARE_EQ(dataZ.sum(i, 8), res[i + 7]);
}
#else
std::cout << "SKIP: test_reduce_sum8, CV_SIMD_FP16 is not available" << std::endl;
#endif
return *this;
}
TheTest & test_loadstore_fp16_f32()
{
AlignedData<v_uint16> data; data.a.clear();
data.a.d[0] = 0x3c00; // 1.0
data.a.d[VTraits<R>::vlanes() - 1] = (unsigned short)0xc000; // -2.0
AlignedData<v_float32> data_f32; data_f32.a.clear();
AlignedData<v_uint16> out;
R r1 = vx_load_expand((const cv::hfloat*)data.a.d);
R r2(r1);
EXPECT_EQ(1.0f, v_get0(r1));
v_store(data_f32.a.d, r2);
EXPECT_EQ(-2.0f, data_f32.a.d[VTraits<R>::vlanes() - 1]);
out.a.clear();
v_pack_store((cv::hfloat*)out.a.d, r2);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
EXPECT_EQ(data.a[i], out.a[i]) << "i=" << i;
}
return *this;
}
TheTest & test_loadstore_fp16()
{
#if CV_SIMD_FP16
AlignedData<R> data;
AlignedData<R> out;
// check if addresses are aligned and unaligned respectively
EXPECT_EQ((size_t)0, (size_t)&data.a.d % (sizeof(typename VTraits<R>::lane_type) * VTraits<R>::vlanes()));
EXPECT_NE((size_t)0, (size_t)&data.u.d % (sizeof(typename VTraits<R>::lane_type) * VTraits<R>::vlanes()));
EXPECT_EQ((size_t)0, (size_t)&out.a.d % (sizeof(typename VTraits<R>::lane_type) * VTraits<R>::vlanes()));
EXPECT_NE((size_t)0, (size_t)&out.u.d % (sizeof(typename VTraits<R>::lane_type) * VTraits<R>::vlanes()));
// check some initialization methods
R r1 = data.u;
R r2 = vx_load(data.a.d);
R r3(r2);
EXPECT_EQ(data.u[0], v_get0(r1));
EXPECT_EQ(data.a[0], v_get0(r2));
EXPECT_EQ(data.a[0], v_get0(r3));
// check some store methods
out.a.clear();
v_store(out.a.d, r1);
EXPECT_EQ(data.a, out.a);
#else
std::cout << "SKIP: test_loadstore_fp16, CV_SIMD_FP16 is not available" << std::endl;
#endif
return *this;
}
TheTest & test_float_cvt_fp16()
{
#if CV_SIMD_FP16
AlignedData<v_float32> data;
// check conversion
v_float32 r1 = vx_load(data.a.d);
v_float16 r2 = v_cvt_f16(r1, vx_setzero_f32());
v_float32 r3 = v_cvt_f32(r2);
EXPECT_EQ(1, v_get0(r2));
EXPECT_EQ(v_get0(r3), v_get0(r1));
#else
std::cout << "SKIP: test_float_cvt_fp16, CV_SIMD_FP16 is not available" << std::endl;
#endif
return *this;
}
void do_check_cmp64(const Data<R>& dataA, const Data<R>& dataB)
{
R a = dataA;
R b = dataB;
Data<R> dataEQ = v_eq(a, b);
Data<R> dataNE = v_ne(a, b);
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
SCOPED_TRACE(cv::format("i=%d", i));
if (cvtest::debugLevel > 0) cout << "i=" << i << " ( " << dataA[i] << " vs " << dataB[i] << " ): eq=" << dataEQ[i] << " ne=" << dataNE[i] << endl;
EXPECT_NE((LaneType)dataEQ[i], (LaneType)dataNE[i]);
if (dataA[i] == dataB[i])
EXPECT_EQ((LaneType)-1, (LaneType)dataEQ[i]);
else
EXPECT_EQ((LaneType)0, (LaneType)dataEQ[i]);
if (dataA[i] != dataB[i])
EXPECT_EQ((LaneType)-1, (LaneType)dataNE[i]);
else
EXPECT_EQ((LaneType)0, (LaneType)dataNE[i]);
}
}
TheTest & test_cmp64()
{
Data<R> dataA;
Data<R> dataB;
for (int i = 0; i < VTraits<R>::vlanes(); ++i)
{
dataA[i] = dataB[i];
}
dataA[0]++;
do_check_cmp64(dataA, dataB);
do_check_cmp64(dataB, dataA);
dataA[0] = dataB[0];
dataA[1] += (((LaneType)1) << 32);
do_check_cmp64(dataA, dataB);
do_check_cmp64(dataB, dataA);
dataA[0] = (LaneType)-1;
dataB[0] = (LaneType)-1;
dataA[1] = (LaneType)-1;
dataB[1] = (LaneType)2;
do_check_cmp64(dataA, dataB);
do_check_cmp64(dataB, dataA);
return *this;
}
};
#define DUMP_ENTRY(type) printf("SIMD%d: %s\n", 8*VTraits<v_uint8>::vlanes(), CV__TRACE_FUNCTION);
//============= 8-bit integer =====================================================================
void test_hal_intrin_uint8()
{
DUMP_ENTRY(v_uint8);
TheTest<v_uint8>()
.test_loadstore()
.test_interleave()
.test_interleave_pq()
.test_expand()
.test_expand_q()
.test_addsub()
.test_arithm_wrap()
.test_mul()
.test_mul_expand()
.test_cmp()
.test_logic()
.test_dotprod_expand()
.test_min_max()
.test_absdiff()
.test_reduce()
.test_reduce_sad()
.test_mask()
.test_popcount()
.test_pack<1>().test_pack<2>().test_pack<3>().test_pack<8>()
.test_pack_u<1>().test_pack_u<2>().test_pack_u<3>().test_pack_u<8>()
.test_pack_b()
.test_unpack()
.test_reverse()
.test_extract<0>().test_extract<1>().test_extract<8>().test_extract<15>()
.test_rotate<0>().test_rotate<1>().test_rotate<8>().test_rotate<15>()
.test_extract_n<0>().test_extract_n<1>()
.test_extract_highest()
.test_pack_triplets()
//.test_broadcast_element<0>().test_broadcast_element<1>()
#if CV_SIMD_WIDTH == 32
.test_pack<9>().test_pack<10>().test_pack<13>().test_pack<15>()
.test_pack_u<9>().test_pack_u<10>().test_pack_u<13>().test_pack_u<15>()
.test_extract<16>().test_extract<17>().test_extract<23>().test_extract<31>()
.test_rotate<16>().test_rotate<17>().test_rotate<23>().test_rotate<31>()
#endif
;
}
void test_hal_intrin_int8()
{
DUMP_ENTRY(v_int8);
TheTest<v_int8>()
.test_loadstore()
.test_interleave()
.test_interleave_pq()
.test_expand()
.test_expand_q()
.test_addsub()
.test_arithm_wrap()
.test_mul()
.test_mul_expand()
.test_cmp()
.test_logic()
.test_dotprod_expand()
.test_min_max()
.test_absdiff()
.test_absdiffs()
.test_abs()
.test_reduce()
.test_reduce_sad()
.test_mask()
.test_popcount()
.test_pack<1>().test_pack<2>().test_pack<3>().test_pack<8>()
.test_unpack()
.test_reverse()
.test_extract<0>().test_extract<1>().test_extract<8>().test_extract<15>()
.test_rotate<0>().test_rotate<1>().test_rotate<8>().test_rotate<15>()
.test_extract_n<0>().test_extract_n<1>()
.test_extract_highest()
.test_pack_triplets()
//.test_broadcast_element<0>().test_broadcast_element<1>()
;
}
//============= 16-bit integer =====================================================================
void test_hal_intrin_uint16()
{
DUMP_ENTRY(v_uint16);
TheTest<v_uint16>()
.test_loadstore()
.test_interleave()
.test_interleave_pq()
.test_expand()
.test_addsub()
.test_arithm_wrap()
.test_mul()
.test_mul_expand()
.test_mul_hi()
.test_cmp()
.test_shift<1>()
.test_shift<8>()
.test_dotprod_expand()
.test_logic()
.test_min_max()
.test_absdiff()
.test_reduce()
.test_reduce_sad()
.test_mask()
.test_popcount()
.test_pack<1>().test_pack<2>().test_pack<7>().test_pack<16>()
.test_pack_u<1>().test_pack_u<2>().test_pack_u<7>().test_pack_u<16>()
.test_unpack()
.test_reverse()
.test_extract<0>().test_extract<1>().test_extract<4>().test_extract<7>()
.test_rotate<0>().test_rotate<1>().test_rotate<4>().test_rotate<7>()
.test_extract_n<0>().test_extract_n<1>()
.test_extract_highest()
.test_pack_triplets()
//.test_broadcast_element<0>().test_broadcast_element<1>()
;
}
void test_hal_intrin_int16()
{
DUMP_ENTRY(v_int16);
TheTest<v_int16>()
.test_loadstore()
.test_interleave()
.test_interleave_pq()
.test_expand()
.test_addsub()
.test_arithm_wrap()
.test_mul()
.test_mul_expand()
.test_mul_hi()
.test_cmp()
.test_shift<1>()
.test_shift<8>()
.test_dotprod()
.test_dotprod_expand()
.test_logic()
.test_min_max()
.test_absdiff()
.test_absdiffs()
.test_abs()
.test_reduce()
.test_reduce_sad()
.test_mask()
.test_popcount()
.test_pack<1>().test_pack<2>().test_pack<7>().test_pack<16>()
.test_unpack()
.test_reverse()
.test_extract<0>().test_extract<1>().test_extract<4>().test_extract<7>()
.test_rotate<0>().test_rotate<1>().test_rotate<4>().test_rotate<7>()
.test_extract_n<0>().test_extract_n<1>()
.test_extract_highest()
.test_pack_triplets()
//.test_broadcast_element<0>().test_broadcast_element<1>()
;
}
//============= 32-bit integer =====================================================================
void test_hal_intrin_uint32()
{
DUMP_ENTRY(v_uint32);
TheTest<v_uint32>()
.test_loadstore()
.test_interleave()
// .test_interleave_pq() //not implemented in AVX
.test_expand()
.test_addsub()
.test_mul()
.test_mul_expand()
.test_cmp()
.test_shift<1>()
.test_shift<8>()
.test_logic()
.test_min_max()
.test_absdiff()
.test_reduce()
.test_reduce_sad()
.test_mask()
.test_popcount()
.test_pack<1>().test_pack<2>().test_pack<15>().test_pack<32>()
.test_unpack()
.test_reverse()
.test_extract<0>().test_extract<1>().test_extract<2>().test_extract<3>()
.test_rotate<0>().test_rotate<1>().test_rotate<2>().test_rotate<3>()
.test_extract_n<0>().test_extract_n<1>()
.test_broadcast_element<0>().test_broadcast_element<1>()
.test_extract_highest()
.test_broadcast_highest()
.test_transpose()
.test_pack_triplets()
;
}
void test_hal_intrin_int32()
{
DUMP_ENTRY(v_int32);
TheTest<v_int32>()
.test_loadstore()
.test_interleave()
// .test_interleave_pq() //not implemented in AVX
.test_expand()
.test_addsub()
.test_mul()
.test_abs()
.test_cmp()
.test_popcount()
.test_shift<1>().test_shift<8>()
.test_dotprod()
.test_dotprod_expand_f64()
.test_logic()
.test_min_max()
.test_absdiff()
.test_reduce()
.test_reduce_sad()
.test_mask()
.test_pack<1>().test_pack<2>().test_pack<15>().test_pack<32>()
.test_unpack()
.test_reverse()
.test_extract<0>().test_extract<1>().test_extract<2>().test_extract<3>()
.test_rotate<0>().test_rotate<1>().test_rotate<2>().test_rotate<3>()
.test_extract_n<0>().test_extract_n<1>()
.test_broadcast_element<0>().test_broadcast_element<1>()
.test_float_cvt32()
.test_float_cvt64()
.test_transpose()
.test_extract_highest()
.test_broadcast_highest()
.test_pack_triplets()
;
}
//============= 64-bit integer =====================================================================
void test_hal_intrin_uint64()
{
DUMP_ENTRY(v_uint64);
TheTest<v_uint64>()
.test_loadstore()
.test_addsub()
.test_cmp64()
//.test_cmp() - not declared as supported
.test_shift<1>().test_shift<8>()
.test_logic()
.test_reverse()
.test_extract<0>().test_extract<1>()
.test_rotate<0>().test_rotate<1>()
.test_extract_n<0>().test_extract_n<1>()
.test_extract_highest()
.test_popcount()
//.test_broadcast_element<0>().test_broadcast_element<1>()
;
}
void test_hal_intrin_int64()
{
DUMP_ENTRY(v_int64);
TheTest<v_int64>()
.test_loadstore()
.test_addsub()
.test_cmp64()
//.test_cmp() - not declared as supported
.test_shift<1>().test_shift<8>()
.test_logic()
.test_reverse()
.test_extract<0>().test_extract<1>()
.test_rotate<0>().test_rotate<1>()
.test_extract_n<0>().test_extract_n<1>()
.test_extract_highest()
//.test_broadcast_element<0>().test_broadcast_element<1>()
.test_cvt64_double()
.test_popcount()
;
}
//============= Floating point =====================================================================
void test_hal_intrin_float32()
{
DUMP_ENTRY(v_float32);
TheTest<v_float32>()
.test_loadstore()
.test_interleave()
.test_interleave_2channel()
// .test_interleave_pq() //not implemented in AVX
.test_addsub()
.test_mul()
.test_div()
.test_abs()
.test_cmp()
.test_sqrt_abs()
.test_min_max()
.test_float_absdiff()
.test_reduce()
.test_reduce_sad()
.test_mask()
.test_unpack()
.test_float_math()
.test_float_cvt64()
.test_matmul()
.test_transpose()
.test_reduce_sum4()
.test_reverse()
.test_extract<0>().test_extract<1>().test_extract<2>().test_extract<3>()
.test_rotate<0>().test_rotate<1>().test_rotate<2>().test_rotate<3>()
.test_extract_n<0>().test_extract_n<1>()
.test_broadcast_element<0>().test_broadcast_element<1>()
.test_extract_highest()
.test_broadcast_highest()
.test_pack_triplets()
#if CV_SIMD_WIDTH == 32
.test_extract<4>().test_extract<5>().test_extract<6>().test_extract<7>()
.test_rotate<4>().test_rotate<5>().test_rotate<6>().test_rotate<7>()
#endif
;
}
void test_hal_intrin_float64()
{
DUMP_ENTRY(v_float64);
#if (CV_SIMD_64F || CV_SIMD_SCALABLE_64F)
TheTest<v_float64>()
.test_loadstore()
.test_addsub()
.test_mul()
.test_div()
.test_abs()
.test_cmp()
.test_sqrt_abs()
.test_min_max()
.test_float_absdiff()
.test_mask()
.test_unpack()
.test_float_math()
.test_round_pair_f64()
.test_float_cvt32()
.test_reverse()
.test_extract<0>().test_extract<1>()
.test_rotate<0>().test_rotate<1>()
.test_extract_n<0>().test_extract_n<1>()
.test_extract_highest()
//.test_broadcast_element<0>().test_broadcast_element<1>()
#if CV_SIMD_WIDTH == 32
.test_extract<2>().test_extract<3>()
.test_rotate<2>().test_rotate<3>()
#endif
;
#else
std::cout << "SKIP: CV_SIMD_64F is not available" << std::endl;
#endif
}
void test_hal_intrin_float16()
{
DUMP_ENTRY(v_float16);
#if CV_FP16
TheTest<v_float32>().test_loadstore_fp16_f32();
#if CV_SIMD_FP16
TheTest<v_float16>()
.test_loadstore_fp16()
.test_float_cvt_fp16()
.test_interleave()
.test_addsub()
.test_mul()
.test_div_fp16()
.test_abs_fp16()
.test_cmp()
.test_sqrt_abs()
.test_min_max()
.test_float_absdiff()
.test_mask()
.test_unpack()
.test_float_math()
.test_matmul_fp16()
.test_transpose8x8_fp16()
.test_reduce_sum8()
.test_reverse()
.test_extract<0>().test_extract<1>().test_extract<4>().test_extract<7>()
.test_rotate<0>().test_rotate<1>().test_rotate<4>().test_rotate<7>()
.test_extract_highest()
.test_broadcast_element<0>().test_broadcast_element<1>()
.test_extract_n<0>().test_extract_n<1>()
#endif
;
#else
std::cout << "SKIP: CV_FP16 is not available" << std::endl;
#endif
}
#endif //CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY
//CV_CPU_OPTIMIZATION_NAMESPACE_END
//}}} // namespace
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