Method create

source/backend/cpu/compute/ConvolutionFloatFactory.cpp:185–280

}

Execution* ConvolutionFloatFactory::create(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
                                           const MNN::Op* op, Backend* backend) {
    auto conv2d = op->main_as_Convolution2D();
    if (inputs.size() > 1) {
        // Multi Input
        return new ConvolutionTiledExecutorMultiInput(conv2d->common(), backend);
    }
#ifdef MNN_LOW_MEMORY
    bool lowMemory = static_cast<CPUBackend*>(backend)->memoryMode() == BackendConfig::Memory_Low;
    if (lowMemory && static_cast<CPUBackend*>(backend)->functions()->bytes == 2 && static_cast<CPUBackend*>(backend)->int8Functions()->MNNGemmInt8AddBiasScale_Unit_FP16 == nullptr) {
        // Fall back to fp32
        return nullptr;
    }
#else
    bool lowMemory = false;
#endif

    const float* originWeight = nullptr;
    const float* originBias   = nullptr;
    int originWeightSize   = 0;
    int originBiasSize     = 0;
    std::shared_ptr<ConvolutionCommon::Int8Common> quanCommon;
    std::unique_ptr<Tensor> externalWeightTensor, externalBiasTensor;
    bool supportSparse = false;
    auto core = static_cast<CPUBackend*>(backend)->functions();
    int bytes = core->bytes;
#ifdef MNN_USE_SPARSE_COMPUTE
#ifdef MNN_USE_SSE
    const bool onlySSENotAVX = core->pack == 4; // no backend of only sse without avx2 or avx512
#else
    const bool onlySSENotAVX = false;
#endif
    supportSparse = !onlySSENotAVX && bytes == 4;
#endif
    if (nullptr != conv2d->quanParameter()) {
        bool forceFloat = false;
        if (!supportSparse && conv2d->quanParameter()->index() != nullptr) {
            // The weight is storage as float sparse, but the backend don't support sparse compute, expand it
            forceFloat = true;
        }
        quanCommon = ConvolutionCommon::load(op, backend, forceFloat, lowMemory);
        if (nullptr == quanCommon) {
            MNN_ERROR("Memory not Enough, can't extract IDST Convolution: %s \n", op->name()->c_str());
            return nullptr;
        }

        if (conv2d->quanParameter()->has_scaleInt()) {
            if (bytes < 4) {
                // From BF16 / FP16
                return nullptr;
            }
            return ConvolutionIntFactory::create(inputs[0], outputs[0], op, backend, quanCommon.get());
        }
        // Back to float
        originWeight     = quanCommon->weightFloat.get();
        originWeightSize = quanCommon->weightFloat.size();
    } else if (nullptr == conv2d->weight() || nullptr == conv2d->bias()) {
        MNN_ERROR("%s has no weight or bias. The model may be benchmark model, please revert the weight/bias firstly\n", op->name()->c_str());