mirror of https://github.com/alibaba/MNN.git
86 lines
2.8 KiB
C++
86 lines
2.8 KiB
C++
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// CPUQuantizeLinear.cpp
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// MNN
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//
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// Created by MNN on 2018/07/15.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "backend/cpu/CPUBackend.hpp"
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#include "core/Concurrency.h"
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#include "backend/cpu/CPUQuantizeLinear.hpp"
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#include "compute/CommonOptFunction.h"
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#include "core/TensorUtils.hpp"
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namespace MNN {
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CPUQuantizeLinear::CPUQuantizeLinear(Backend *b, int size, int axis) : MNN::Execution(b){
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mSize = size;
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mAxis = axis;
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}
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ErrorCode CPUQuantizeLinear::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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int size = mSize;
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float* scale = inputs[1]->host<float>();
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int8_t* zero = nullptr;
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if (inputs.size() > 2) {
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zero = inputs[2]->host<int8_t>();
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}
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if (mSize == 1) {
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float s = scale[0] == 0?0: 1/ scale[0];
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mQuantScales.resize(4, s);
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if (nullptr != zero) {
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int8_t z = *zero;
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mQuantZeroPoints.resize(4, z);
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} else {
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mQuantZeroPoints.resize(4);
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}
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} else { // TODO scale: (1,D)
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}
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return NO_ERROR;
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}
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ErrorCode CPUQuantizeLinear::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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auto input = inputs[0];
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int N = input->length(0), C = input->length(1), H = input->length(2), W = input->length(3);
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ssize_t size = N * C * H * W;
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auto core = static_cast<CPUBackend*>(backend())->int8Functions();
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int UNIT, SRC_UNIT, DST_XUNIT;
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core->MNNGetGemmUnit(&UNIT, &SRC_UNIT, &DST_XUNIT);
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int maxValue = 127;
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int minValue = -128;
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#ifdef MNN_USE_SSE
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auto dst = outputs[0]->host<uint8_t>();
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int offset = 128;
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#else
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auto dst = outputs[0]->host<int8_t>();
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int offset = 0;
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#endif
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if (mSize == 1) {
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auto src = input->host<float>();
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int sizeDiv = (int)size / UNIT;
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core->MNNFloat2Int8(src, (int8_t*)dst, size / UNIT, mQuantScales.data(), -128, 127, mQuantZeroPoints[0]);
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for (int i = sizeDiv * UNIT; i < size; ++i) {
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int v = (int)roundf(src[i] * mQuantScales[0]) + mQuantZeroPoints[0] + offset;
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v = std::max(minValue + offset, std::min(maxValue + offset, v));
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dst[i] = v;
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}
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} else {
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}
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return NO_ERROR;
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}
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class CPUQuantizeLinearCreator : public CPUBackend::Creator {
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public:
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virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs,
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const MNN::Op *op, Backend *backend) const override {
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int size = op->main_as_QuantizeLinear()->scaleSize();
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int axis = op->main_as_QuantizeLinear()->scaleAxis();
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return new CPUQuantizeLinear(backend, size, axis);
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}
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};
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REGISTER_CPU_OP_CREATOR(CPUQuantizeLinearCreator, OpType_QuantizeLinear);
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} // namespace MNN
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