mirror of https://github.com/alibaba/MNN.git
121 lines
4.5 KiB
C++
121 lines
4.5 KiB
C++
//
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// Arm82Moments.cpp
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// MNN
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//
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// Created by MNN on 2019/02/28.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#if defined(__ANDROID__) || defined(__aarch64__)
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#include "Arm82Moments.hpp"
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#include "Arm82Backend.hpp"
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#include "Arm82Vec.hpp"
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#include "core/Concurrency.h"
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#include <MNN/MNNDefine.h>
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#include "core/Macro.h"
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#include "core/TensorUtils.hpp"
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#ifdef MNN_USE_NEON
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#include <arm_neon.h>
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#endif
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using Vec = MNN::Math::Vec<FLOAT16, 8>;
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namespace MNN {
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Arm82Moments::Arm82Moments(Backend *backend, const MNN::Op *op) : Execution(backend) {
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auto momentsParam = op->main_as_MomentsParam();
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if (momentsParam->dim()) {
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for (int i = 0; i < momentsParam->dim()->size(); ++i) {
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mAxis.push_back(momentsParam->dim()->data()[i]);
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}
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}
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mKeepDims = momentsParam->keepDims();
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MNN_ASSERT(DataType_DT_FLOAT == momentsParam->dType());
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}
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ErrorCode Arm82Moments::onResize(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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return NO_ERROR;
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}
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void Arm82Moments::calculateMean(const FLOAT16 *src, FLOAT16 *mean, int channelBlock, int planeNumber) {
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const int numberThread = ((Arm82Backend*)backend())->numberThread();
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MNN_CONCURRENCY_BEGIN(tId, numberThread) {
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int step = UP_DIV(channelBlock, numberThread), start = tId * step, end = ALIMIN(start + step, channelBlock);
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for (int z = start; z < end; ++z) {
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const FLOAT16* srcZ = src + z * planeNumber * 8;
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FLOAT16* meanZ = mean + z * 8;
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Vec sum(0);
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for (int i = 0; i < planeNumber; ++i) {
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sum = sum + Vec::load(srcZ + i * 8);
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}
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Vec result = sum / (float)planeNumber;
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Vec::save(meanZ, result);
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}
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} MNN_CONCURRENCY_END();
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}
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void Arm82Moments::calculateVariance(const FLOAT16 *src, const FLOAT16 *mean, FLOAT16* var, int channelBlock, int planeNumber) {
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const int numberThread = ((Arm82Backend*)backend())->numberThread();
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MNN_CONCURRENCY_BEGIN(tId, numberThread) {
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int step = UP_DIV(channelBlock, numberThread), start = tId * step, end = ALIMIN(start + step, channelBlock);
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for (int z = start; z < end; ++z) {
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const FLOAT16* srcZ = src + z * planeNumber * 8, *meanZ = mean + z * 8;
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FLOAT16* varZ = var + z * 8;
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Vec sum(0), meanVal = Vec::load(meanZ);
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for (int i = 0; i < planeNumber; ++i) {
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Vec diff = Vec::load(srcZ + i * 8) - meanVal;
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sum = sum + diff * diff;
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}
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Vec result = sum / (float)planeNumber;
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Vec::save(varZ, result);
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}
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} MNN_CONCURRENCY_END();
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}
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ErrorCode Arm82Moments::onExecute(const std::vector<Tensor *> &inputs, const std::vector<Tensor *> &outputs) {
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MNN_ASSERT(1 == inputs.size());
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MNN_ASSERT(2 == outputs.size());
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auto input = inputs[0], mean = outputs[0], variance = outputs[1];
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// the layout of Moments is NC4HW4, now only support for calculating Moments along height and width
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MNN_ASSERT(MNN_DATA_FORMAT_NC4HW4 == TensorUtils::getDescribe(input)->dimensionFormat);
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MNN_ASSERT(mKeepDims);
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MNN_ASSERT(mAxis.size() == 2 && mAxis[0] == 2 && mAxis[1] == 3);
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const int batch = input->batch(), channelBlock = UP_DIV(mean->channel(), 8);
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const int inBatchStride = ARM82TensorStrideHelper(input, 0), outBatchStride = ARM82TensorStrideHelper(mean, 0);
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const int planeNumber = ARM82TensorStrideHelper(input, 1);
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// mean
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for (int b = 0; b < batch; ++b) {
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const FLOAT16* srcPtr = input->host<FLOAT16>() + b * inBatchStride;
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FLOAT16* meanPtr = mean->host<FLOAT16>() + b * outBatchStride;
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calculateMean(srcPtr, meanPtr, channelBlock, planeNumber);
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}
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// variance
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for (int b = 0; b < batch; ++b) {
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const FLOAT16* srcPtr = input->host<FLOAT16>() + b * inBatchStride;
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const FLOAT16* meanPtr = mean->host<FLOAT16>() + b * outBatchStride;
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FLOAT16* variancePtr = variance->host<FLOAT16>() + b * outBatchStride;
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calculateVariance(srcPtr, meanPtr, variancePtr, channelBlock, planeNumber);
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}
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return NO_ERROR;
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}
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class Arm82MomentsCreator : public Arm82Backend::Arm82Creator {
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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 new Arm82Moments(backend, op);
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}
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};
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REGISTER_ARM82_OP_CREATOR(OpType_Moments, Arm82MomentsCreator);
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} // namespace MNN
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#endif
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