e25f20f7a3
commit 3f4938648950a7f3bf9a19c320ca9fae7c52de20 Author: sophgo-forum-service <forum_service@sophgo.com> Date: Mon May 13 13:44:23 2024 +0800 [feat] cviruntime opensource for cv18xx soc. - a4b6a3, add cumsum and gatherelements_pt.
96 lines
2.5 KiB
C++
96 lines
2.5 KiB
C++
#ifndef RUNTIME_SIMILARITY_H
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#define RUNTIME_SIMILARITY_H
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#include <stddef.h>
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#include <math.h>
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#include <vector>
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#include <iostream>
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static float u16_to_bf16(uint16_t val) {
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float ret;
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auto *q = reinterpret_cast<uint16_t *>(&ret);
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#if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
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q[0] = val;
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#else
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q[1] = val;
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#endif
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return ret;
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}
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template <typename U, typename V>
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static bool array_convert(U *u, V *v, std::vector<float> &uu, std::vector<float> &vv) {
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size_t equal_cnt = 0;
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for (size_t i = 0; i < uu.size(); i++) {
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uu[i] = (typeid(U) == typeid(uint16_t)) ? u16_to_bf16(u[i]) : static_cast<float>(u[i]);
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vv[i] = (typeid(V) == typeid(uint16_t)) ? u16_to_bf16(v[i]) : static_cast<float>(v[i]);
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if (uu[i] == vv[i])
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equal_cnt++;
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}
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return equal_cnt == uu.size();
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}
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static float array_average(float *u, float *v, size_t size) {
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double average = 0;
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for (size_t i = 0; i < size; i++) {
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average += u[i] * v[i];
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}
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return average / size;
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}
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static float array_average(float *u, size_t size, int power = 1) {
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double average = 0;
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for (size_t i = 0; i < size; i++) {
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if (power != 1) {
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average += pow(u[i], power);
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} else {
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average += u[i];
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}
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}
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return average / size;
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}
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static float euclidean_similiarity(float *u, float *v, size_t size) {
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double distance = 0;
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double root = 0;
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for (size_t i = 0; i < size; i++) {
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distance += pow(u[i] - v[i], 2);
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root += pow((u[i] + v[i]) / 2, 2);
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}
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distance = sqrt(distance);
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root = sqrt(root);
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return (float)(1 - distance / root);
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}
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static float correlation_similarity(float *u, float *v, size_t size, bool centered) {
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if (centered) {
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float umu = array_average(u, size);
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float vmu = array_average(v, size);
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for (size_t i = 0; i < size; i++) {
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u[i] -= umu;
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v[i] -= vmu;
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}
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}
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float uv = array_average(u, v, size);
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float uu = array_average(u, size, 2);
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float vv = array_average(v, size, 2);
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return uv / sqrt(uu * vv);
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}
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template <typename U, typename V>
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static void array_similarity(U *u, V *v, size_t size, float &euclidean, float &cosine,
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float &correlation) {
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std::vector<float> uu(size, 0);
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std::vector<float> vv(size, 0);
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if (array_convert(u, v, uu, vv)) {
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euclidean = 1;
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cosine = 1;
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correlation = 1;
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return;
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}
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euclidean = euclidean_similiarity(uu.data(), vv.data(), uu.size());
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cosine = correlation_similarity(uu.data(), vv.data(), uu.size(), false);
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correlation = correlation_similarity(uu.data(), vv.data(), uu.size(), true);
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}
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#endif |