[NEED HELP] migration from tensorrt8 to tensorrt10

[leoxiang66]

Hi guys, I'm automotive engineer. My current work is to migrate a tensorrt 8 project to tensorrt 10.

I have already checked the guide on https://docs.nvidia.com/deeplearning/tensorrt/latest/api/migration-guide.html#c

But seems there are some functions that are not listed on this guide:

1. error: no matching function for call to nvinfer1::IRuntime::deserializeCudaEngine(unsigned char*, std::vector<unsigned char>::size_type, int) (On the guide website, there is only IRuntime::deserializeCudaEngine (void const* blob, std::size_t size, IPluginFactory* pluginFactory) IRuntime::destroy())

Could anyone please help me, thanks!

[leoxiang66]

You can find the source code of the tensorrt 8.5 project: https://github.com/NVIDIA-AI-IOT/Lidar_AI_Solution/blob/0a563c722c75b1b8d968a61ed4b62d2f5a78c06b/CUDA-CenterPoint/src/tensorrt.cpp#L116

[lix19937]

ref deserializeCudaEngine in follow code block

namespace samplesCommon

struct InferDeleter
{
    template <typename T>
    void operator()(T* obj) const
    {
        delete obj;
    }
};
}


bool SampleIOFormats::build(int32_t dataWidth)
{
    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
    if (!builder)
    {
        return false;
    }

    auto network = SampleUniquePtr<nvinfer1::INetworkDefinition>(builder->createNetworkV2(0));
    if (!network)
    {
        return false;
    }

    auto config = SampleUniquePtr<nvinfer1::IBuilderConfig>(builder->createBuilderConfig());
    if (!config)
    {
        return false;
    }

    auto parser
        = SampleUniquePtr<nvonnxparser::IParser>(nvonnxparser::createParser(*network, sample::gLogger.getTRTLogger()));
    if (!parser)
    {
        return false;
    }

    auto constructed = constructNetwork(builder, network, config, parser);
    if (!constructed)
    {
        return false;
    }

    network->getInput(0)->setAllowedFormats(static_cast<TensorFormats>(1 << static_cast<int32_t>(mTensorFormat)));
    network->getOutput(0)->setAllowedFormats(1U << static_cast<int32_t>(TensorFormat::kLINEAR));

    mEngine.reset();

    if (dataWidth == 1)
    {
        config->setFlag(BuilderFlag::kINT8);
        network->getInput(0)->setType(DataType::kINT8);
        network->getOutput(0)->setType(DataType::kINT8);
        samplesCommon::setAllDynamicRanges(network.get(), 127.0F, 127.0F);
    }
    if (dataWidth == 2)
    {
        config->setFlag(BuilderFlag::kFP16);
        network->getInput(0)->setType(DataType::kHALF);
        network->getOutput(0)->setType(DataType::kHALF);
    }

    config->setFlag(BuilderFlag::kGPU_FALLBACK);

    // CUDA stream used for profiling by the builder.
    auto profileStream = samplesCommon::makeCudaStream();
    if (!profileStream)
    {
        return false;
    }
    config->setProfileStream(*profileStream);

    SampleUniquePtr<nvinfer1::ITimingCache> timingCache{};

    // Load timing cache
    if (!mParams.timingCacheFile.empty())
    {
        timingCache = samplesCommon::buildTimingCacheFromFile(
            sample::gLogger.getTRTLogger(), *config, mParams.timingCacheFile, sample::gLogError);
    }

    SampleUniquePtr<IHostMemory> plan{builder->buildSerializedNetwork(*network, *config)};
    if (!plan)
    {
        return false;
    }

    if (timingCache != nullptr && !mParams.timingCacheFile.empty())
    {
        samplesCommon::updateTimingCacheFile(
            sample::gLogger.getTRTLogger(), mParams.timingCacheFile, timingCache.get(), *builder);
    }

    if (!mRuntime)
    {
        mRuntime = SampleUniquePtr<IRuntime>(createInferRuntime(sample::gLogger.getTRTLogger()));
    }

    if (!mRuntime)
    {
        return false;
    }

    mEngine = std::shared_ptr<nvinfer1::ICudaEngine>(
        mRuntime->deserializeCudaEngine(plan->data(), plan->size()), samplesCommon::InferDeleter()); /// here !!!!!!!
    if (!mEngine)
    {
        return false;
    }

    ASSERT(network->getNbInputs() == 1);
    mInputDims = network->getInput(0)->getDimensions();
    ASSERT(mInputDims.nbDims == 4);

    ASSERT(network->getNbOutputs() == 1);
    mOutputDims = network->getOutput(0)->getDimensions();
    ASSERT(mOutputDims.nbDims == 2);

    return true;
}