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0x1306a94/simd_macos_def.h

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TenniS SIMD
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simd_macos_def.h
// source https://github.com/TenniS-Open/TenniS/blob/ef6c8332809a021d0eb5842c0f9d32a7f0b07f96/include/kernels/common/simd_def/simd_base_def.h
#ifndef TENSORSTACK_KERNELS_COMMON_SIMD_DEF_SIMD_MACOS_DEF_H
#define TENSORSTACK_KERNELS_COMMON_SIMD_DEF_SIMD_MACOS_DEF_H
#ifdef TS_USE_MACOS_SIMD
#include <simd/vector.h>
typedef struct _simd_f32x4x3 {
simd_float4 val[3];
} _simd_f32x4x3;
using _simd_f32x4 = simd_float4;
using _simd_f32x4x2 = simd_float8;
using _simd_f32 = simd_float1;
using _simd_int32x4 = simd_int4;
using _simd_int32 = simd_int1;
using _simd_int32x4x2 = simd_int8;
inline _simd_int32x4 _simd_int32x4_load(const _simd_int32 *p) {
// return *((_simd_int32x4 *)p);
return simd_make_int4(p[0], p[1], p[2], p[3]);
}
inline _simd_int32x4 _simd_int32x4_set(_simd_int32 a, _simd_int32 b, _simd_int32 c, _simd_int32 d) {
return simd_make_int4(a, b, c, d);
}
inline void _simd_int32x4_store(_simd_int32 *p, _simd_int32x4 m) {
// *((_simd_int32x4 *)p) = m;
p[0] = m[0];
p[1] = m[1];
p[2] = m[2];
p[3] = m[3];
}
inline _simd_int32x4 _simd_int32x4_add(_simd_int32x4 lhs, _simd_int32x4 rhs) {
return lhs + rhs;
}
inline _simd_int32x4 _simd_int32x4_sub(_simd_int32x4 lhs, _simd_int32x4 rhs) {
return lhs - rhs;
}
inline _simd_int32x4x2 _simd_int32x4x2_load(const _simd_int32 *p) {
_simd_int32x4 lo = simd_make_int4(p[0], p[1], p[2], p[3]);
_simd_int32x4 hi = simd_make_int4(p[4], p[5], p[6], p[7]);
return simd_make_int8(lo, hi);
}
inline _simd_int32x4x2 _simd_int32x4x2_set(_simd_int32 a, _simd_int32 b, _simd_int32 c, _simd_int32 d,
_simd_int32 e, _simd_int32 f, _simd_int32 g, _simd_int32 h) {
_simd_int32x4 lo = simd_make_int4(a, b, c, d);
_simd_int32x4 hi = simd_make_int4(e, f, g, h);
return simd_make_int8(lo, hi);
}
inline void _simd_int32x4x2_store(_simd_int32 *p, _simd_int32x4x2 m) {
// *((_simd_int32x4x2 *)p) = m;
p[0] = m[0];
p[1] = m[1];
p[2] = m[2];
p[3] = m[3];
p[4] = m[4];
p[5] = m[5];
p[6] = m[6];
p[7] = m[7];
}
inline _simd_int32x4x2 _simd_int32x4x2_add(_simd_int32x4x2 lhs, _simd_int32x4x2 rhs) {
return lhs + rhs;
}
inline _simd_int32x4x2 _simd_int32x4x2_sub(_simd_int32x4x2 lhs, _simd_int32x4x2 rhs) {
return lhs - rhs;
}
inline _simd_f32x4 _simd_f32x4_load(const _simd_f32 *p) {
// return *((_simd_f32x4 *)p);
return simd_make_float4(p[0], p[1], p[2], p[3]);
}
inline _simd_f32x4 _simd_f32x4_set(_simd_f32 a, _simd_f32 b, _simd_f32 c, _simd_f32 d) {
return simd_make_float4(a, b, c, d);
}
inline void _simd_f32x4_store(_simd_f32 *p, _simd_f32x4 m) {
// *((_simd_f32x4 *)p) = m;
p[0] = m[0];
p[1] = m[1];
p[2] = m[2];
p[3] = m[3];
}
inline _simd_f32x4 _simd_f32x4_add(_simd_f32x4 lhs, _simd_f32x4 rhs) {
return lhs + rhs;
}
inline _simd_f32x4 _simd_f32x4_sub(_simd_f32x4 lhs, _simd_f32x4 rhs) {
return lhs - rhs;
}
inline _simd_f32x4 _simd_f32x4_mul(_simd_f32x4 lhs, _simd_f32x4 rhs) {
return lhs * rhs;
}
inline _simd_f32x4 _simd_f32x4_div(_simd_f32x4 lhs, _simd_f32x4 rhs) {
return lhs / rhs;
}
inline _simd_f32x4 _simd_f32x4_max(_simd_f32x4 lhs, _simd_f32x4 rhs) {
return simd_max(lhs, rhs);
}
inline _simd_f32x4 _simd_f32x4_min(_simd_f32x4 lhs, _simd_f32x4 rhs) {
return simd_min(lhs, rhs);
}
inline void _simd_f32x4_transpose4x4(_simd_f32x4 &q0, _simd_f32x4 &q1, _simd_f32x4 &q2, _simd_f32x4 &q3) {
_simd_f32 t0[4], t1[4], t2[4], t3[4];
t0[0] = q0[0];
t1[0] = q0[1];
t2[0] = q0[2];
t3[0] = q0[3];
t0[1] = q1[0];
t1[1] = q1[1];
t2[1] = q1[2];
t3[1] = q1[3];
t0[2] = q2[0];
t1[2] = q2[1];
t2[2] = q2[2];
t3[2] = q2[3];
t0[3] = q3[0];
t1[3] = q3[1];
t2[3] = q3[2];
t3[3] = q3[3];
for (int i = 0; i < 4; i++) {
q0[i] = t0[i];
q1[i] = t1[i];
q2[i] = t2[i];
q3[i] = t3[i];
}
}
inline _simd_f32x4 _simd_f32x4_fmadd(const _simd_f32x4 &q0, const _simd_f32x4 &q1, const _simd_f32x4 &q2) {
return q0 * q1 + q2;
}
// Note:Maybe i can find some instruction like vfmaq_laneq_f32 or vfmaq_lane_f32
inline _simd_f32x4 _simd_f32x4_fmadd(const _simd_f32x4 &q0, const _simd_f32x4 &q1, const _simd_f32x4 &q2, const int index) {
_simd_f32 v = *((float *)&q1 + index);
_simd_f32x4 temp = simd_make_float4(v, v, v, v);
return q0 * temp + q2;
}
inline _simd_f32x4 _simd_broadcast2float32x4(const _simd_f32 *src) {
_simd_f32 v = *src;
return simd_make_float4(v, v, v, v);
}
// Note:I can not find some instruction like vextq_f32,sad,- -
inline _simd_f32x4 _simd_f32x4_concat(const _simd_f32x4 &q0, const _simd_f32x4 &q1, const int index) {
if (index == 0)
return q0;
float res[4];
for (int i = index; i < 4; i++) {
res[i - index] = *(((float *)&q0) + i);
}
for (int i = 0; i < index; i++) {
res[i + 4 - index] = *(((float *)&q1) + i);
}
return simd_make_float4(res[0], res[1], res[2], res[3]);
}
inline _simd_f32x4 _simd_f32x4_interval_load(const _simd_f32 *p, const int inc) {
const _simd_f32 *a = p;
const _simd_f32 *b = a + inc;
const _simd_f32 *c = b + inc;
const _simd_f32 *d = c + inc;
return simd_make_float4(*a, *b, *c, *d);
}
inline _simd_f32x4x2 _simd_f32x4x2_load(const _simd_f32 *p) {
_simd_f32x4 lo = simd_make_float4(p[0], p[1], p[2], p[3]);
_simd_f32x4 hi = simd_make_float4(p[4], p[5], p[6], p[7]);
return simd_make_float8(lo, hi);
}
inline _simd_f32x4x2 _simd_f32x4x2_set(_simd_f32 a, _simd_f32 b, _simd_f32 c, _simd_f32 d,
_simd_f32 e, _simd_f32 f, _simd_f32 g, _simd_f32 h) {
_simd_f32x4 lo = simd_make_float4(a, b, c, d);
_simd_f32x4 hi = simd_make_float4(e, f, g, h);
return simd_make_float8(lo, hi);
}
inline void _simd_f32x4x2_store(_simd_f32 *p, _simd_f32x4x2 m) {
// *((_simd_f32x4x2 *)p) = m;
p[0] = m[0];
p[1] = m[1];
p[2] = m[2];
p[3] = m[3];
p[4] = m[4];
p[5] = m[5];
p[6] = m[6];
p[7] = m[7];
}
inline _simd_f32x4x2 _simd_f32x4x2_add(_simd_f32x4x2 lhs, _simd_f32x4x2 rhs) {
return lhs + rhs;
}
inline _simd_f32x4x2 _simd_f32x4x2_sub(_simd_f32x4x2 lhs, _simd_f32x4x2 rhs) {
return lhs - rhs;
}
inline _simd_f32x4x2 _simd_f32x4x2_mul(_simd_f32x4x2 lhs, _simd_f32x4x2 rhs) {
return lhs * rhs;
}
inline _simd_f32x4x2 _simd_f32x4x2_div(_simd_f32x4x2 lhs, _simd_f32x4x2 rhs) {
return lhs / rhs;
}
inline _simd_f32x4 _simd_f32x4x2_index(_simd_f32x4x2 src, const int index) {
switch (index) {
case 0:
return src.lo;
case 1:
return src.hi;
default:
break;
}
return simd_make_float4(0.f, 0.f, 0.f, 0.f);
}
inline _simd_f32x4x2 _simd_f32x4x2_fmadd(_simd_f32x4x2 q0, _simd_f32x4x2 q1, _simd_f32x4x2 q2) {
return q0 * q1 + q2;
}
inline _simd_f32x4x2 _simd_f32x4x2_interval_load(const _simd_f32 *p, const int inc) {
const _simd_f32 *a0 = p, *a1 = p + 1;
const _simd_f32 *b0 = a0 + inc, *b1 = a1 + inc;
const _simd_f32 *c0 = b0 + inc, *c1 = b1 + inc;
const _simd_f32 *d0 = c0 + inc, *d1 = c1 + inc;
_simd_f32x4 lo = simd_make_float4(*a0, *b0, *c0, *d0);
_simd_f32x4 hi = simd_make_float4(*a1, *b1, *c1, *d1);
return simd_make_float8(lo, hi);
}
inline _simd_f32x4x3 _simd_f32x4x3_load(const _simd_f32 *p) {
_simd_f32x4x3 res;
res.val[0] = _simd_f32x4_load(p);
res.val[1] = _simd_f32x4_load(p + 4);
res.val[2] = _simd_f32x4_load(p + 8);
return res;
}
inline _simd_f32x4x3 _simd_f32x4x3_set(_simd_f32 a, _simd_f32 b, _simd_f32 c, _simd_f32 d,
_simd_f32 e, _simd_f32 f, _simd_f32 g, _simd_f32 h,
_simd_f32 i, _simd_f32 j, _simd_f32 k, _simd_f32 l) {
_simd_f32x4x3 res;
res.val[0] = simd_make_float4(a, b, c, d);
res.val[1] = simd_make_float4(e, f, g, h);
res.val[2] = simd_make_float4(i, j, k, l);
return res;
}
inline void _simd_f32x4x3_store(_simd_f32 *p, const _simd_f32x4x3 &m) {
_simd_f32x4_store(p, m.val[0]);
_simd_f32x4_store(p + 4, m.val[1]);
_simd_f32x4_store(p + 8, m.val[2]);
}
inline void _simd_f32x4x3_store(_simd_f32 *p, const _simd_f32x4x3 &m, int index) {
_simd_f32x4_store(p, m.val[index]);
}
// TODO: optimze this fuc,just like vldnq_f32
inline _simd_f32x4x3 _simd_f32x4x3_interval_load(const _simd_f32 *p, int inc) {
_simd_f32x4x3 res;
const _simd_f32 *a0 = p;
const _simd_f32 *b0 = p + 1;
const _simd_f32 *c0 = p + 2;
const _simd_f32 *a1 = a0 + inc;
const _simd_f32 *b1 = b0 + inc;
const _simd_f32 *c1 = c0 + inc;
const _simd_f32 *a2 = a1 + inc;
const _simd_f32 *b2 = b1 + inc;
const _simd_f32 *c2 = c1 + inc;
const _simd_f32 *a3 = a2 + inc;
const _simd_f32 *b3 = b2 + inc;
const _simd_f32 *c3 = c2 + inc;
res.val[0] = simd_make_float4(*a0, *a1, *a2, *a3);
res.val[1] = simd_make_float4(*b0, *b1, *b2, *b3);
res.val[2] = simd_make_float4(*c0, *c1, *c2, *c3);
return res;
}
// TODO: optimize this fuc,just like vstnq_f32
inline void _simd_f32x4x3_interval_save(_simd_f32 *p, const _simd_f32x4x3 &m) {
float *i0 = (float *)&(m.val[0]);
float *i1 = (float *)&(m.val[1]);
float *i2 = (float *)&(m.val[2]);
for (int i = 0; i < 4; ++i) {
*p++ = *i0++;
*p++ = *i1++;
*p++ = *i2++;
}
}
// cast
inline _simd_int32x4x2 _simd_floatx4x2_to_int32x4x2(_simd_f32x4x2 src) {
simd_int8 value = simd_int(src);
return value;
}
inline _simd_f32x4x2 _simd_intx4x2_to_float32x4x2(_simd_int32x4x2 src) {
simd_float8 value = simd_float(src);
return value;
}
inline _simd_f32x4x2 _simd_broadcast2float32x4x2(const _simd_f32 *src) {
_simd_f32 v = *src;
return _simd_f32x4x2_set(v, v, v, v, v, v, v, v);
}
// concat
inline _simd_f32x4x3 _simd_concat(const _simd_f32x4 &q0, const _simd_f32x4 &q1, const _simd_f32x4 &q2) {
_simd_f32x4x3 res;
res.val[0] = q0;
res.val[1] = q1;
res.val[2] = q2;
return res;
}
#endif // TS_USE_MACOS_SIMD
#endif // TENSORSTACK_KERNELS_COMMON_SIMD_DEF_SIMD_MACOS_DEF_H
/*
use simd_base_def.h M1
[2023-06-05 17:41:30.426] [debug] [SeetaFaceBackend.cpp:35] disable pool
[2023-06-05 17:41:30.552] [trace] [SeetaFaceBackend.cpp:84] Create
[2023-06-05 17:41:30.552] [trace] [FaceEngine.cpp:19] Create
[2023-06-05 17:41:30.746] [trace] [SeetaFaceEngine.cpp:98] detect faces size 5 elapsed 183ms
[2023-06-05 17:41:30.774] [trace] [SeetaFaceEngine.cpp:120] mark faces elapsed 28ms
[2023-06-05 17:41:30.804] [trace] [SeetaFaceEngine.cpp:129] extract feature elapsed 58ms
feature:
[0,0.044788,0.0228483,0.0796338,0.00404353,0.0352072,0.0145812,0,0.00733359,0,0,0.0122046,0,0,0.010931,0,0.0651413,0.0219534,0,0.00212701,0.0087284,0.00570349,0,0.0114131,0.0110661,0.0957893,0,0,0,0.00421958,0.0259343,0,0.0209099,0,0.0117085,0.0629094,0,0,0.0108283,0,0.0407078,0.0335438,0.0167985,0.0403636,0.0332712,0,0.0512531,0.025122,0.00686075,0.0470847,0,0,0.081014,0.00200811,0.0646783,0,0.0150455,0,0.0106828,0,0,0.106765,0.0043364,0.0163518,0.0675838,0.0117721,0.00562454,0.0485452,0,0.0798149,0,0.00973853,0,0.0337716,0.0120449,0.0207462,0,0.0106688,0.00987623,0.038413,0,0.0158682,0,0.111059,0.0616533,0.0721328,0,0.0406742,0,0,0,0,0,0.0641162,0.0751595,0.0158749,0.0345114,0.0108376,0,0.0626912,0.0379907,0.0691455,0.0322766,0,0,0.00594014,0,0.0528104,0,0,0,0,0,0.062943,0.0555983,0.00596929,0,0.0366725,0.107605,0.0703458,0.0506294,0,0,0,0,0.00456128,0,0.00579365,0,0.0343695,0.0100304,0.118483,0.0179631,0.0497104,0.0106934,0.0432992,0.0105603,0,0.0207511,0.0545255,0,0.0268505,0.051987,0,0,0.00490103,0,0,0.00763285,0,0,0.00835788,0.0405376,0.0101364,0,0.00968043,0.00876453,0.0177257,0.0431112,0.0211715,0,0,0,0,0.016561,0.0923529,0,0.0113394,0,0,0.00472251,0.0139401,0.100757,0.0662285,0.00440093,0,0.00606682,0,0.0509633,0.00542213,0.0458071,0.0535998,0,0,0.0734471,0,0.044509,0.0346151,0.0223728,0.0615279,0,0,0.0563295,0.0132228,0.113025,0.0533523,0.043985,0.0160827,0,0,0.0070994,0.0825525,0,0.0901195,0,0,0.0431469,0.0148836,0.00204589,0,0,0,0.0111,0.082281,0.0150804,0.00592606,0.00728213,0.0194411,0.0139387,0.0988984,0,0,0.0419028,0,0,0,0,0.0142755,0.0379217,0.0993314,0.00992774,0.0151732,0.0152259,0,0.00783714,0.0138637,0.0244143,0,0,0.113913,0.032105,0.0151533,0,0.0653985,0,0,0.00482401,0,0.0499213,0,0.0427479,0,0,0.00664543,0,0.0662763,0.0372443,0.0122827,0,0.0464496,0.0655576,0.112914,0.014312,0,0.0452334,0.0326202,0.0168287,0,0,0.0081695,0.00258055,0.0127315,0.00317017,0,0.0142722,0.010263,0.0174105,0,0.0971989,0.0941814,0.0183327,0,0.0193854,0.0318506,0,0,0,0.0748133,0.00904842,0.0282608,0.0371174,0,0.00462761,0.061499,0.000769729,0,0.0157614,0.00510099,0.0238474,0,0.00865163,0,0,0,0.0150538,0,0.0166871,0.0124438,0.0499529,0.0437474,0,0.0126297,0,0.0113305,0,0,0.00647451,0.0378259,0,0,0.0284197,0.00991798,0.00526517,0.0513281,0.00316117,0.0129231,0,0.0243211,0.0131223,0,0.0109116,0,0.0641457,0,0.00634081,0,0.00573742,0.0308256,0.0183984,0.00524489,0.010364,0.0208789,0.0212994,0.0233489,0.0236186,0,0,0,0.0526812,0,0,0.0331226,0.00510358,0,0,0,0.0364518,0,0.000507811,0.00871518,0.0240323,0.0553797,0,0.0210174,0,0,0,0.0125252,0.0302098,0,0,0,0.0150309,0,0.0666279,0,0,0,0.00548336,0.00560767,0.0813828,0,0,0,0.00806363,0,0,0.0104724,0,0.00738494,0.0412563,0,0.00373565,0.00431105,0.0103631,0.0132942,0,0,0,0,0.046601,0.0752771,0.00628181,0,0,0,0,0.027827,0.00344195,0,0.0103335,0.0175772,0.0364161,0.0429977,0,0,0,0,0,0,0.0166435,0.0214502,0.00334537,0.00979617,0,0,0.0569941,0.0684493,0.0898466,0,0,0.0150622,0,0.00805806,0,0.0598543,0,0.0162616,0.00760232,0.0911918,0.00534503,0.0295759,0.0693111,0,0.00979055,0,0.0134932,0.00994864,0.0158064,0.027641,0.0421162,0,0,0.0289038,0.0531681,0.012232,0.0345303,0.0489427,0,0,0.0257082,0.00341137,0.0112953,0.0440018,0,0.0142613,0,0.0029878,0.0611848,0,0.00836075,0.0138117,0.0633009,0,0.0132248,0,0.0352318,0,0.0501039,0.00658513,0.00518415,0,0,0.0662234,0.0353705,0,0.0353888,0,0.0519795,0.0120009,0.0946333,0,0,0.0123702,0.00754336,0.00851104,0.00543776,0.0487189,0,0.0390588,0,0.0137241,0,0.0183927,0.0042444,0.00991729,0,0.0868949,0,0,0.0725011,0.015119,0.01007,0,0.0597042,0.0174728,0,0,0.0492092,0.0106963,0.0057988,0,0,0,0,0.00468344,0,0.0152237,0,0.00525697,0,0,0.0139175,0,0,0,0.0200129,0,0.0558477,0.0601801,0,0.0902135,0.0101599,0.013855,0,0.0385156,0.0396009,0.0103714,0.0137314,0.00752487,0.0123283,0.0144253,0,0.0377796,0,0,0,0.0315617,0.0119817,0.0693059,0.0121598,0.0173511,0,0.046589,0.130889,0,0.00874848,0.0104486,0,0,0,0.0270913,0,0,0.00327038,0.0147744,0,0.012693,0,0,0,0,0.00839437,0.0667084,0.0209601,0.0622312,0,0.016688,0,0.036119,0.0131733,0.00112454,0,0.0317723,0.00362632,0.0267761,0,0.0128638,0.0144239,0,0,0.0679223,0.100872,0.0335112,0.0229614,0,0.0239768,0,0.00588423,0.00833443,0.0170438,0,0.0198106,0,0.0213511,0,0.00703136,0.038872,0,0.0114685,0.00824086,0.0301908,0.00715219,0.0154936,0,0.0736086,0,0.0381239,0,0.0259367,0.0859989,0,0,0,0,0,0.00926283,0.0426756,0,0.0116052,0.00598333,0.00817494,0.00581404,0.0119855,0,0,0,0,0,0,0,0.00454197,0,0.00807741,0.0127153,0.0469548,0.0304744,0.00741707,0.0221255,0.0163889,0,0.00451463,0.0276175,0,0.0109082,0.0631083,0,0,0.0311047,0.0353328,0.0151635,0.0173802,0.0326787,0,0.00568726,0,0.0572417,0.131579,0.0117827,0.0437816,0,0.0572269,0.00797378,0.0378605,0.132425,0.00358474,0.0128747,0.02339,0,0.0165948,0,0,0,0.00686529,0.00316893,0.0294033,0,0.0547305,0.0116391,0,0,0.00561211,0.0400627,0,0.120435,0,0.017203,0,0,0,0,0,0.00205381,0,0,0,0.00995872,0,0.0225115,0.0590502,0,0.00898547,0,0.0167789,0,0,0,0.00398962,0,0,0.00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[2023-06-05 17:41:30.805] [trace] [FaceEngine.cpp:23] Destroy
[2023-06-05 17:41:30.805] [trace] [SeetaFaceBackend.cpp:88] Destroy
use simd_macos_def.h M1
[2023-06-05 17:38:56.066] [debug] [SeetaFaceBackend.cpp:35] disable pool
[2023-06-05 17:38:56.187] [trace] [SeetaFaceBackend.cpp:84] Create
[2023-06-05 17:38:56.187] [trace] [FaceEngine.cpp:19] Create
[2023-06-05 17:38:56.374] [trace] [SeetaFaceEngine.cpp:98] detect faces size 5 elapsed 175ms
[2023-06-05 17:38:56.403] [trace] [SeetaFaceEngine.cpp:120] mark faces elapsed 29ms
[2023-06-05 17:38:56.433] [trace] [SeetaFaceEngine.cpp:129] extract feature elapsed 59ms
feature:
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[2023-06-05 17:38:56.434] [trace] [FaceEngine.cpp:23] Destroy
[2023-06-05 17:38:56.434] [trace] [SeetaFaceBackend.cpp:88] Destroy
use simd_base_def.h M2
[2023-06-05 17:52:31.673] [debug] [SeetaFaceBackend.cpp:35] disable pool
[2023-06-05 17:52:31.769] [trace] [SeetaFaceBackend.cpp:84] Create
[2023-06-05 17:52:31.769] [trace] [FaceEngine.cpp:19] Create
[2023-06-05 17:52:31.993] [trace] [SeetaFaceEngine.cpp:98] detect faces size 5 elapsed 217ms
[2023-06-05 17:52:32.019] [trace] [SeetaFaceEngine.cpp:120] mark faces elapsed 25ms
[2023-06-05 17:52:32.043] [trace] [SeetaFaceEngine.cpp:129] extract feature elapsed 49ms
feature:
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[2023-06-05 17:52:32.043] [trace] [FaceEngine.cpp:23] Destroy
[2023-06-05 17:52:32.043] [trace] [SeetaFaceBackend.cpp:88] Destroy
use simd_macos_def.h M2
[2023-06-05 17:50:55.256] [debug] [SeetaFaceBackend.cpp:35] disable pool
[2023-06-05 17:50:55.349] [trace] [SeetaFaceBackend.cpp:84] Create
[2023-06-05 17:50:55.349] [trace] [FaceEngine.cpp:19] Create
[2023-06-05 17:50:55.574] [trace] [SeetaFaceEngine.cpp:98] detect faces size 5 elapsed 218ms
[2023-06-05 17:50:55.601] [trace] [SeetaFaceEngine.cpp:120] mark faces elapsed 27ms
[2023-06-05 17:50:55.625] [trace] [SeetaFaceEngine.cpp:129] extract feature elapsed 51ms
feature:
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[2023-06-05 17:50:55.626] [trace] [FaceEngine.cpp:23] Destroy
[2023-06-05 17:50:55.626] [trace] [SeetaFaceBackend.cpp:88] Destroy
use simd_base_def.h (2.2 GHz 四核Intel Core i7)
[2023-06-05 19:10:42.373] [debug] [SeetaFaceBackend.cpp:35] disable pool
[2023-06-05 19:10:42.819] [trace] [SeetaFaceBackend.cpp:84] Create
[2023-06-05 19:10:42.819] [trace] [FaceEngine.cpp:19] Create
[2023-06-05 19:10:43.005] [trace] [SeetaFaceEngine.cpp:98] detect faces size 5 elapsed 156ms
[2023-06-05 19:10:43.082] [trace] [SeetaFaceEngine.cpp:120] mark faces elapsed 76ms
[2023-06-05 19:10:43.138] [trace] [SeetaFaceEngine.cpp:129] extract feature elapsed 133ms
feature:
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[2023-06-05 19:10:43.140] [trace] [FaceEngine.cpp:23] Destroy
[2023-06-05 19:10:43.140] [trace] [SeetaFaceBackend.cpp:88] Destroy
use simd_macos_def.h (2.2 GHz 四核Intel Core i7)
[2023-06-05 20:11:33.472] [debug] [SeetaFaceBackend.cpp:35] disable pool
[2023-06-05 20:11:33.897] [trace] [SeetaFaceBackend.cpp:84] Create
[2023-06-05 20:11:33.897] [trace] [FaceEngine.cpp:19] Create
[2023-06-05 20:11:34.085] [trace] [SeetaFaceEngine.cpp:98] detect faces size 5 elapsed 162ms
[2023-06-05 20:11:34.152] [trace] [SeetaFaceEngine.cpp:120] mark faces elapsed 66ms
[2023-06-05 20:11:34.231] [trace] [SeetaFaceEngine.cpp:129] extract feature elapsed 145ms
feature:
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[2023-06-05 20:11:34.232] [trace] [FaceEngine.cpp:23] Destroy
[2023-06-05 20:11:34.232] [trace] [SeetaFaceBackend.cpp:88] Destroy
*/
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