mirror of https://github.com/alibaba/MNN.git
88 lines
3.0 KiB
C++
88 lines
3.0 KiB
C++
//
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// CPUConvolution.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 "CPUConvolution.hpp"
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#include <math.h>
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#include "CPUBackend.hpp"
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#include "CommonOptFunction.h"
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#include "Macro.h"
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#include "compute/ConvolutionFloatFactory.h"
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namespace MNN {
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CPUConvolution::CPUConvolution(const Convolution2DCommon *convOp, Backend *b) : MNN::Execution(b), mCommon(convOp) {
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mPostFunction = getPostFunction();
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}
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int CPUConvolution::reorderWeightSize(int depth, int outputCount, int kernelSize, int unit) {
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int unit2 = unit * unit;
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return UP_DIV(outputCount, unit) * UP_DIV(depth, unit) * kernelSize * unit2;
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}
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void CPUConvolution::reorderWeight(float *dest, const float *source, int depth, int outputCount, int kernelSize,
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int unit) {
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int unit2 = unit * unit;
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int alignedWeightSize = UP_DIV(outputCount, unit) * UP_DIV(depth, unit) * kernelSize * unit2;
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int cur = 0;
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int batch_4 = ALIGN_UP4(outputCount) / unit;
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for (int b = 0; b < outputCount; ++b) {
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int b_4 = b / unit;
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float *dst_b = dest + b_4 * (alignedWeightSize / batch_4);
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int mx = b % unit;
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for (int d = 0; d < depth; ++d) {
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int my = d % unit;
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int d_4 = d / unit;
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float *dst_d = dst_b + d_4 * kernelSize * unit2;
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for (int y = 0; y < kernelSize; ++y) {
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float *dst_y = dst_d + y * unit2;
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dst_y[unit * my + mx] = source[cur++];
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}
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}
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}
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}
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ErrorCode CPUConvolution::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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auto input = inputs[0];
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auto output = outputs[0];
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if (mCommon->padMode() == PadMode_SAME) {
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int kernelWidthSize = (mCommon->kernelX() - 1) * mCommon->dilateX() + 1;
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int kernelHeightSize = (mCommon->kernelY() - 1) * mCommon->dilateY() + 1;
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int padNeededWidth = (output->width() - 1) * mCommon->strideX() + kernelWidthSize - input->width();
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int padNeededHeight = (output->height() - 1) * mCommon->strideY() + kernelHeightSize - input->height();
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mPadX = padNeededWidth / 2;
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mPadY = padNeededHeight / 2;
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return NO_ERROR;
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}
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mPadX = mCommon->padX();
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mPadY = mCommon->padY();
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return NO_ERROR;
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}
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CPUConvolution::POSTFUNCTION CPUConvolution::getPostFunction() const {
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if (mCommon->relu()) {
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return MNNAddBiasRelu;
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}
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if (mCommon->relu6()) {
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return MNNAddBiasRelu6;
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}
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return MNNAddBias;
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}
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class ConvolutionFactory : 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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return ConvolutionFloatFactory::create(inputs, outputs, op, backend);
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}
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};
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REGISTER_CPU_OP_CREATOR(ConvolutionFactory, OpType_Convolution);
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} // namespace MNN
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