mirror of https://github.com/alibaba/MNN.git
69 lines
1.7 KiB
C++
69 lines
1.7 KiB
C++
//
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// ParameterOptimizer.cpp
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// MNN
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//
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// Created by MNN on 2019/11/22.
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// Copyright © 2018, Alibaba Group Holding Limited
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//
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#include "ParameterOptimizer.hpp"
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#include "SGD.hpp"
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#include "ADAM.hpp"
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namespace MNN {
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namespace Train {
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ParameterOptimizer* ParameterOptimizer::createSGD(float lr, float momentum) {
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auto sgd = new SGD;
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sgd->setLearningRate(lr);
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sgd->setMomentum(momentum);
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sgd->setWeightDecay(0.0005f);
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return sgd;
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}
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ParameterOptimizer* ParameterOptimizer::createADAM(float lr, float momentum, float momentum2) {
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auto opt = new ADAM;
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opt->setMomentum(momentum);
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opt->setLearningRate(lr);
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opt->setMomentum2(momentum2);
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return opt;
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}
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bool ParameterOptimizer::step(Express::VARP loss) {
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mStep++;
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auto res = this->onGetNextParameter(loss);
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for (auto iter : res) {
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iter.second.fix(Express::VARP::TRAINABLE);
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}
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for (auto iter : res) {
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iter.first->input(iter.second);
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}
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return !res.empty();
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}
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int ParameterOptimizer::currentStep() {
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return mStep;
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}
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void ParameterOptimizer::setCurrentStep(int step) {
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mStep = step;
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}
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void ParameterOptimizer::append(const std::vector<Express::VARP>& parameters) {
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for (auto p : parameters) {
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if (p->expr().first->inputType() == Express::VARP::TRAINABLE) {
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mParameters.insert(p);
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this->onAppend(p);
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}
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}
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}
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void ParameterOptimizer::remove(const std::vector<Express::VARP>& parameters) {
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for (auto p : parameters) {
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mParameters.erase(p);
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this->onRemove(p);
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}
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}
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const std::set<Express::VARP>& ParameterOptimizer::parameters() const {
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return mParameters;
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}
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} // namespace Train
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} // namespace MNN
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