mirror of https://github.com/alibaba/MNN.git
70 lines
2.2 KiB
C++
70 lines
2.2 KiB
C++
|
//
|
||
|
|
// CPUSigmoid.cpp
|
||
|
|
// MNN
|
||
|
|
//
|
||
|
|
// Created by MNN on 2018/08/09.
|
||
|
|
// Copyright © 2018, Alibaba Group Holding Limited
|
||
|
|
//
|
||
|
|
|
||
|
|
#include "CPUSigmoid.hpp"
|
||
|
|
#include <math.h>
|
||
|
|
#include "CPUBackend.hpp"
|
||
|
|
#include "CommonOptFunction.h"
|
||
|
|
#include "Macro.h"
|
||
|
|
|
||
|
|
namespace MNN {
|
||
|
|
ErrorCode CPUSigmoid::onExecute(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
|
||
|
|
MNN_ASSERT(1 == inputs.size());
|
||
|
|
MNN_ASSERT(1 == outputs.size());
|
||
|
|
auto inputData = inputs[0]->host<float>();
|
||
|
|
auto outputData = outputs[0]->host<float>();
|
||
|
|
|
||
|
|
const int dataSize = outputs[0]->elementSize();
|
||
|
|
int countC8 = dataSize / 8;
|
||
|
|
if (countC8 > 0) {
|
||
|
|
// Align to eight so asm is easier to write
|
||
|
|
static float parameters[] = {
|
||
|
|
(float)log(2.0f), 1.0f / (float)log(2.0f), 1.0f, 1.0f, 0.5f, 1.0f / 6.0f, 1.0f / 24.0f, 1.0f / 120.0f};
|
||
|
|
MNNExpC8(outputData, inputData, parameters, countC8);
|
||
|
|
int cc8 = countC8 * 8;
|
||
|
|
for (int i = 0; i < cc8; ++i) {
|
||
|
|
outputData[i] = 1.0f / (1.0f + outputData[i]);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
int remain = countC8 * 8;
|
||
|
|
auto param = log(2.0f);
|
||
|
|
for (int i = remain; i < dataSize; i++) {
|
||
|
|
/*Origin Function*/
|
||
|
|
// outputData[i] = 1.0f/(1.0f+exp(-inputData[i]));
|
||
|
|
|
||
|
|
/*Approciate Function*/
|
||
|
|
auto x = -inputData[i];
|
||
|
|
int div = (x / param);
|
||
|
|
auto xReamin = x - div * param;
|
||
|
|
div = std::min(div, 24);
|
||
|
|
div = std::max(div, -24);
|
||
|
|
float expBasic = 1.0;
|
||
|
|
if (div < 0) {
|
||
|
|
expBasic = 1.0f / (1 << (-div));
|
||
|
|
} else {
|
||
|
|
expBasic = (float)(1 << div);
|
||
|
|
}
|
||
|
|
auto t = xReamin;
|
||
|
|
auto expRemain = ((((1.0f / 120 * t + 1.0f / 24) * t + 1.0f / 6) * t + 0.5f) * t + 1.0f) * t + 1.0f;
|
||
|
|
outputData[i] = 1.0f / (1.0f + expBasic * expRemain);
|
||
|
|
}
|
||
|
|
|
||
|
|
return NO_ERROR;
|
||
|
|
}
|
||
|
|
|
||
|
|
class CPUSigmoidCreator : public CPUBackend::Creator {
|
||
|
|
public:
|
||
|
|
virtual Execution* onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
|
||
|
|
const MNN::Op* op, Backend* backend) const {
|
||
|
|
return new CPUSigmoid(backend);
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
REGISTER_CPU_OP_CREATOR(CPUSigmoidCreator, OpType_Sigmoid);
|
||
|
|
} // namespace MNN
|