Method build

samples/sampleDynamicReshape/sampleDynamicReshape.cpp:115–151

\brief Builds the two engines required for inference. \details This function creates one TensorRT engine for resizing inputs to the correct sizes, then creates a TensorRT network by parsing the ONNX model and builds an engine that will be used to run inference (mPredictionEngine). \return false if error in build preprocessor or predict engine.

//! \return false if error in build preprocessor or predict engine.
//!
bool SampleDynamicReshape::build()
{
    auto builder = makeUnique(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
    if (!builder)
    {
        sample::gLogError << "Create inference builder failed." << std::endl;
        return false;
    }

    auto runtime = makeUnique(nvinfer1::createInferRuntime(sample::gLogger.getTRTLogger()));
    if (!runtime)
    {
        sample::gLogError << "Runtime object creation failed." << std::endl;
        return false;
    }

    // This function will also set mPredictionInputDims and mPredictionOutputDims,
    // so it needs to be called before building the preprocessor.
    try
    {
        // CUDA stream used for profiling by the builder.
        auto profileStream = samplesCommon::makeCudaStream();
        if (!profileStream)
        {
            return false;
        }

        bool result = buildPredictionEngine(builder, runtime, *profileStream)
            && buildPreprocessorEngine(builder, runtime, *profileStream);
        return result;
    }
    catch (std::runtime_error& e)
    {
        sample::gLogError << e.what()  << std::endl;
        return false;
    }
}

//!
//! \brief Builds an engine for preprocessing (mPreprocessorEngine).