Method onGetNextParameter

tools/train/source/optimizer/SGD.cpp:79–118

}

std::map<Express::VARP, Express::VARP> SGD::onGetNextParameter(Express::VARP loss) {
    auto grad = OpGrad::grad(loss, trainable(), mGradBlockExprName);
    auto parameters = module()->parameters();
    std::vector<VARP> prepareCompute;
    for (auto iter : parameters) {
        if (iter->expr().first->get() != nullptr) {
            prepareCompute.emplace_back(iter);
        }
    }
    for (auto& iter : grad) {
        prepareCompute.emplace_back(iter.second);
    }
    Variable::prepareCompute(prepareCompute);
    std::vector<VARP> replaceOp(prepareCompute.size());
    for (int i=0; i<prepareCompute.size(); ++i) {
        auto info = prepareCompute[i]->getInfo();
        auto ptr = prepareCompute[i]->readMap<void>();
        if (nullptr == ptr) {
            MNN_ERROR("Compute error in SGD\n");
            return {};
        }
        auto newVar = _Const(ptr, info->dim, info->order, info->type);
        replaceOp[i]= newVar;
    }
    for (int i=0; i<prepareCompute.size(); ++i) {
        Variable::replace(prepareCompute[i], replaceOp[i]);
    }

    for (auto& iter : grad) {
        // apply regularization
        auto addWeightDecayGrad = regularizeParameters(iter.first, iter.second);
        addWeightDecayGrad.fix(Express::VARP::CONSTANT);
        // apply momentum, etc.
        auto updateValue = this->onComputeUpdateValue(iter.first, addWeightDecayGrad);
        // apply update
        auto newParameter = iter.first - updateValue;
        iter.second       = newParameter;
    }
    return grad;
}

std::pair<std::vector<Express::VARP>, std::vector<Express::VARP>>  SGD::onMakeParameterUpdateGraphByGrad(const std::vector<ParameterOptGrad>& parameterGrads) {
    std::map<MNN::Express::VARP, MNN::Express::VARP> varUpdateMap;