mirror of https://github.com/alibaba/MNN.git
108 lines
4.2 KiB
C++
108 lines
4.2 KiB
C++
//
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// Arm82InstanceNorm.hpp
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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 "Arm82Backend.hpp"
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#include "Arm82OptFunc.hpp"
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#include "Arm82InstanceNorm.hpp"
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#include "MNN_generated.h"
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#include "core/Concurrency.h"
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#include <MNN/MNNDefine.h>
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#include <cmath>
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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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namespace MNN {
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Arm82InstanceNorm::Arm82InstanceNorm(Backend* backend, const MNN::Op* op) : Execution(backend) {
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auto normParam = op->main_as_BatchNorm();
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const int channels = normParam->channels();
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mEpsilon = normParam->epsilon();
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mScale.reset(ALIGN_UP8(channels));
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mScale.clear();
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if (normParam->slopeData() && normParam->slopeData()->data()) {
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MNNSlowCopy<FLOAT16, float>(mScale.get(), normParam->slopeData()->data(), channels);
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}
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mBias.reset(ALIGN_UP8(channels));
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mBias.clear();
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if (normParam->biasData() && normParam->biasData()->data()) {
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MNNSlowCopy<FLOAT16, float>(mBias.get(), normParam->biasData()->data(), channels);
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}
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}
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ErrorCode Arm82InstanceNorm::onExecute(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs) {
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MNN_ASSERT(3 == inputs.size());
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MNN_ASSERT(1 == outputs.size());
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auto input = inputs[0], mean = inputs[1], variance = inputs[2], output = outputs[0];
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const int batch = input->batch(), imageSize = input->stride(1);
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auto scalePtr = mScale.get(), biasPtr = mBias.get();
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const int threadNum = ((Arm82Backend*)backend())->numberThread();
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const int channelBlock = UP_DIV(input->channel(), 8);
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for (int b = 0; b < batch; ++b) {
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auto inputPtr = input->host<FLOAT16>() + b * ARM82TensorStrideHelper(input, 0);
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auto meanPtr = mean->host<FLOAT16>() + b * ARM82TensorStrideHelper(mean, 0);
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auto variancePtr = variance->host<FLOAT16>() + b * ARM82TensorStrideHelper(variance, 0);
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auto outputPtr = output->host<FLOAT16>() + b * ARM82TensorStrideHelper(output, 0);
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MNN_CONCURRENCY_BEGIN(tId, threadNum) {
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const int step = UP_DIV(channelBlock, threadNum) * 8, start = tId * step, end = ALIMIN(start + step, channelBlock);
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for (int c = start; c < end; c += 8) {
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auto inputPtrZ = inputPtr + c * imageSize;
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auto outputPtrZ = outputPtr + c * imageSize;
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#ifdef MNN_USE_NEON
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float16x8_t meanVec = vld1q_f16(meanPtr + c), varVec = vld1q_f16(variancePtr + c);
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float16x8_t scaleVec = vld1q_f16(scalePtr + c), biasVec = vld1q_f16(biasPtr + c);
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float16x8_t epsVec = vdupq_n_f16(mEpsilon), rsqrtVec = vrsqrteq_f16(varVec + epsVec);
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float16x8_t gamma = vmulq_f16(scaleVec, rsqrtVec);
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float16x8_t beta = vsubq_f16(biasVec, vmulq_f16(meanVec, gamma));
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for (int i = 0; i < imageSize; ++i) {
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float16x8_t in = vld1q_f16(inputPtr + i * 8);
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vst1q_f16(outputPtrZ + i * 8, vaddq_f16(vmulq_f16(in, gamma), beta));
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}
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#else
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FLOAT16 gamma[8], beta[8];
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for (int k = 0; k < 8; ++k) {
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int index = c + k;
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gamma[k] = scalePtr[index] / sqrt(variancePtr[index] + mEpsilon);
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beta[k] = biasPtr[index] - gamma[k] * meanPtr[index];
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}
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for (int i = 0; i < imageSize; ++i) {
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for (int k = 0; k < 8; ++k) {
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outputPtrZ[i * 8 + k] = inputPtrZ[i * 8 + k] * gamma[k] + beta[k];
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}
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}
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#endif
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}
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}
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MNN_CONCURRENCY_END();
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}
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return NO_ERROR;
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}
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class Arm82InstanceNormCreator : 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 Arm82InstanceNorm(backend, op);
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}
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};
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REGISTER_ARM82_OP_CREATOR(OpType_InstanceNorm, Arm82InstanceNormCreator);
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} // namespace MNN
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#endif
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