Function _collectNeededOps

express/module/PipelineModule.cpp:349–405

}

static std::vector<int> _collectNeededOps(const MNN::Net* net, const std::set<int>& inputIndexes, const std::set<int>& outputIndexes) {
    // 0: not set, 1: output, 2:input
    std::vector<int> tensorMask(net->tensorName()->size());
    ::memset(tensorMask.data(), 0, tensorMask.size() * sizeof(int));

    // 0: use, 1: no use
    std::vector<int> opMask(net->oplists()->size());
    ::memset(opMask.data(), 0, opMask.size() * sizeof(int));

    // Set Initial Status
    for (auto v : outputIndexes) {
        tensorMask[v] = 1;
    }
    for (auto v : inputIndexes) {
        // If both input/output, set as input
        tensorMask[v] = 2;
    }
    bool change = false;
    do {
        change = false;
        for (int i=0; i<opMask.size(); ++i) {
            if (opMask[i] > 0) {
                continue;
            }
            auto op = net->oplists()->GetAs<Op>(i);
            if (nullptr != op->outputIndexes()) {
                for (int j=0; j<op->outputIndexes()->size(); ++j) {
                    auto index = op->outputIndexes()->data()[j];
                    if (tensorMask[index] == 1) {
                        opMask[i] = 1;
                        change = true;
                    }
                }
            }
            if (nullptr != op->inputIndexes() && opMask[i]) {
                for (int j=0; j<op->inputIndexes()->size(); ++j) {
                    auto index = op->inputIndexes()->data()[j];
                    if (tensorMask[index] != 2) {
                        tensorMask[index] = 1;
                    }
                }
            }
        }
    } while (change);

    std::vector<int> ops;
    for (int i=0; i<opMask.size(); ++i) {
        if (opMask[i] > 0) {
            auto op = net->oplists()->GetAs<Op>(i);
            if (needComputeOp(op)) {
                ops.emplace_back(i);
                continue;
            }
        }
    }
    return ops;
}