mirror of https://github.com/alibaba/MNN.git
196 lines
6.9 KiB
C++
196 lines
6.9 KiB
C++
//
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// ResizeFunction.cpp
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// MNN
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//
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// Created by MNN on 2018/07/23.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "backend/cpu/compute/ResizeFunction.h"
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#include <math.h>
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#include "core/AutoStorage.h"
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#include "core/Macro.h"
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#include "math/Vec.hpp"
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using namespace MNN::Math;
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using Vec4 = Vec<float, 4>;
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using Vec16 = Vec<float, 16>;
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using Vec8 = Vec<float, 8>;
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// F = -0.5
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static Vec4 CubicInterpolation(Vec4& A, Vec4& B, Vec4& C, Vec4& D, float t) {
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Vec4 a = (B - C) + (B - A) * 0.5f + (D - C) * 0.5f;
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Vec4 b = C - ((B - A) + (B - C)) - (B + D) * 0.5f;
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Vec4 c = (C - A) * 0.5f;
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Vec4 d = B;
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return ((a * t + b) * t + c) * t + d;
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}
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// F = -0.75
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template<typename T, int pack>
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static Vec<T, pack> CubicInterpolation2(Vec<T, pack>& A, Vec<T, pack>& B, Vec<T, pack>& C, Vec<T, pack>& D, float t) {
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float b0 = 1.0f - 2.25f * t * t + 1.25f * t * t * t;
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float c0 = 1.0f - 2.25f * (1.0f - t) * (1.0f - t) + 1.25 * (1.0f - t) * (1.0f - t) * (1.0f - t);
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auto t_a = 1.0f + t;
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auto t_d = 2.0f - t;
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auto a0 = 3.0f - 6.0f * (t_a) + 5.0f * 0.75 * t_a * t_a - 0.75f * t_a * t_a * t_a;
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auto d0 = 3.0f - 6.0f * (t_d) + 5.0f * 0.75 * t_d * t_d - 0.75f * t_d * t_d * t_d;
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return A * a0 + B * b0 + C * c0 + D * d0;
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}
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void CPUBilinearSampleC4(const float* src, float* dst, const int32_t* position, const float* factor, int8_t* zeroPoint,
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size_t number) {
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int pack = 4;
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for (int i = 0; i < number; ++i) {
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float f = factor[i];
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Vec4 df(f);
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Vec4 sf(1.0f - f);
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Vec4 A = Vec4::load(src + position[2 * i] * pack);
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Vec4 B = Vec4::load(src + position[2 * i + 1] * pack);
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Vec4 Result = B * df + A * sf;
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Vec4::save(dst + pack * i, B * df + A * sf);
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}
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}
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void CPUBilinearLineC4(float* dst, const float* A, const float* B, const float* t, int8_t* zeroPoint, size_t number) {
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int pack = 4;
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Vec4 df(*t);
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Vec4 sf(1.0f - *t);
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for (int i = 0; i < number; ++i) {
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Vec4 value = Vec4::load(A + pack * i) * sf + Vec4::load(B + pack * i) * df;
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Vec4::save(dst + pack * i, value);
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}
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}
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void MNNCubicSampleC4(const float* src, float* dst, int32_t* position, const float* factor, int8_t* zeroPoint, size_t number) {
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for (int i = 0; i < number; ++i) {
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float f = factor[i];
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auto A = Vec4::load(src + 4 * position[4 * i + 0]);
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auto B = Vec4::load(src + 4 * position[4 * i + 1]);
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auto C = Vec4::load(src + 4 * position[4 * i + 2]);
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auto D = Vec4::load(src + 4 * position[4 * i + 3]);
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Vec4::save(dst + 4 * i, CubicInterpolation2(A, B, C, D, f));
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}
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}
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void MNNCubicLineC4(float* dst, const float* A, const float* B, const float* C, const float* D, float* t, int8_t* zeroPoint,
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size_t number, ssize_t minValue, ssize_t maxValue) {
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float f = *t;
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for (int i = 0; i < number; ++i) {
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auto a = Vec4::load(A + 4 * i);
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auto b = Vec4::load(B + 4 * i);
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auto c = Vec4::load(C + 4 * i);
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auto d = Vec4::load(D + 4 * i);
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Vec4::save(dst + 4 * i, CubicInterpolation2<float, 4>(a, b, c, d, f));
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}
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}
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#ifndef MNN_USE_NEON
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void MNNCubicSampleC16(const int8_t* src, float* dst, int32_t* position, const float* factor, int8_t* zeroPoint, size_t number) {
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int pack = 16;
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using Vec16 = Vec<float, 16>;
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#ifdef MNN_USE_SSE
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Vec16 zeroPointV(128 + (*zeroPoint));
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const uint8_t* srcPtr = (uint8_t*)src;
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#else
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Vec16 zeroPointV(*zeroPoint);
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const int8_t* srcPtr = src;
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#endif
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for (int i = 0; i < number; ++i) {
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float f = factor[i];
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auto A = Vec16::load(srcPtr + pack * position[4 * i + 0]) - zeroPointV;
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auto B = Vec16::load(srcPtr + pack * position[4 * i + 1]) - zeroPointV;
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auto C = Vec16::load(srcPtr + pack * position[4 * i + 2]) - zeroPointV;
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auto D = Vec16::load(srcPtr + pack * position[4 * i + 3]) - zeroPointV;
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auto val16 = CubicInterpolation2<float, 16>(A, B, C, D, f);
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Vec16::save(dst + pack * i, CubicInterpolation2<float, 16>(A, B, C, D, f));
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}
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}
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void MNNCubicLineC16(int8_t* dst, const float* A, const float* B, const float* C, const float* D, float* t, int8_t* zeroPoint,
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size_t number, ssize_t minValue, ssize_t maxValue) {
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int pack = 16;
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using Vec16 = Vec<float, 16>;
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#ifdef MNN_USE_SSE
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uint8_t* dstPtr = (uint8_t*)dst;
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int offset = 128 + (*zeroPoint);
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int minVal = 128 + minValue;
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int maxVal = 128 + maxValue;
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#else
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int8_t* dstPtr = dst;
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int offset = *zeroPoint;
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int minVal = (int)minValue;
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int maxVal = (int)maxValue;
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#endif
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float f = *t;
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for (int i = 0; i < number; ++i) {
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auto a = Vec16::load(A + pack * i);
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auto b = Vec16::load(B + pack * i);
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auto c = Vec16::load(C + pack * i);
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auto d = Vec16::load(D + pack * i);
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auto val16 = CubicInterpolation2<float, 16>(a, b, c, d, f);
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for (int j = 0; j < pack; ++j) {
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int val = (int)roundf(val16[j]) + offset;
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if (val > maxVal) {
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val = maxVal;
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}
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if (val < minVal) {
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val = minVal;
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}
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*(dstPtr + pack * i + j) = val;
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}
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}
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}
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void MNNBilinearSampleC8(const int8_t* src, int16_t* dst, const int32_t* position, const float* factor, int8_t* zeroPoint,
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size_t number) {
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#ifdef MNN_USE_SSE
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int offset = 128 + *zeroPoint;
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const uint8_t* srcPtr = (uint8_t*)src;
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#else
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int offset = *zeroPoint;
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const int8_t* srcPtr = src;
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#endif
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int pack = 8;
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for (int i = 0; i < number; ++i) {
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int16_t df = factor[i] * 128;
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int16_t sf = (1 - factor[i]) * 128;
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auto aPtr = srcPtr + position[2 * i] * pack;
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auto bPtr = srcPtr + position[2 * i + 1] * pack;
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for (int j = 0; j < pack; ++j) {
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int a = static_cast<int32_t>(*(aPtr + j) - offset);
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int b = static_cast<int32_t>(*(bPtr + j) - offset);
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int16_t val = static_cast<int16_t>(a * sf + b * df);
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*(dst + pack * i + j) = val;
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}
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}
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}
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void MNNBilinearLineC8(int8_t* dst, const int16_t* A, const int16_t* B, const float* t, int8_t* zeroPoint, size_t number) {
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#ifdef MNN_USE_SSE
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int offset = 128 + (*zeroPoint);
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uint8_t* dstPtr = (uint8_t*)dst;
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#else
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int offset = *zeroPoint;
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int8_t* dstPtr = dst;
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#endif
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int pack = 8;
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int16_t df = (*t) * 128;
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int16_t sf = (1 - *t) * 128;
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for (int i = 0; i < number; ++i) {
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auto aPtr = A + pack * i;
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auto bPtr = B + pack * i;
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for (int j = 0; j < pack; ++j) {
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int32_t val = *(aPtr + j) * sf + *(bPtr + j) * df;
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int8_t valOut = (val + (1<<13)) / (1 << 14);
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if (val < 0) {
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valOut = (val - (1 << 13)) / (1 << 14);
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}
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*(dstPtr+ pack * i + j) = valOut+ offset;
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}
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}
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}
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#endif
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