DriveWorks SDK Reference
4.0.0 Release
For Test and Development only

DNN Workflow

This code snippet demonstrates the how the DNN module is typically used. Note that error handling is left out for clarity.

Initialize network from file.

If the model has been generated on DLA using --useDLA option with tensorrt_optimization tool, the processor type should be either DW_PROCESSOR_TYPE_DLA_0 or DW_PROCESSOR_TYPE_DLA_1 depending on which DLA engine the inference should take place. Otherwise, the processor type should always be DW_PROCESSOR_TYPE_GPU.

contextHandle is assumed to be a previously initialized dwContextHandle_t.

// Load the DNN from a file. Note that the DNN model has to be generated with the tensorrt_optimization tool.
dwDNNHandle_t dnn = nullptr;
dwDNN_initializeTensorRTFromFile(&dnn, "network.fp32", nullptr, DW_PROCESSOR_TYPE_GPU, contextHandle);

Check that the loaded network has the expected number of inputs and outputs.

// Find out the number of input and output blobs in the netowrk
uint32_t numInputs = 0;
uint32_t numOutputs = 0;
dwDNN_getInputBlobCount(&numInputs, dnn);
dwDNN_getOutputBlobCount(&numOutputs, dnn);
if (numInputs != 1) {
std::cerr << "Expected a DNN with one input blob." << std::endl;
return -1;
if (numOutputs != 2) {
std::cerr << "Expected a DNN with two output blobs." << std::endl;
return -1;

Ask the DNN about the order of the input and output blobs. The network is assumed to contain the input blob "data_in" and output blobs "data_out1" and "data_out2".

uint32_t inputIndex = 0;
uint32_t output1Index = 0;
uint32_t output2Index = 0;
// Find indices of blobs by their name.
dwDNN_getInputIndex(&inputIndex, "data_in", dnn);
dwDNN_getOutputIndex(&output1Index, "data_out1", dnn);
dwDNN_getOutputIndex(&output2Index, "data_out2", dnn);

Initialize host and device memory to hold the inputs and outputs of the network.

std::vector<float32_t*> dnnInputs(numInputs, nullptr);
std::vector<float32_t*> dnnOutputs(numOutputs, nullptr);
std::vector<float32_t> dnnInputHost;
std::vector<std::vector<float32_t>> dnnOutputHost(numOutputs);
// Allocate device memory for DNN input.
dwBlobSize sizeInput;
dwDNN_getInputSize(&sizeInput, inputIndex, dnn);
size_t numInputElements = sizeInput.batchsize * sizeInput.channels * sizeInput.height * sizeInput.width;
cudaMalloc(&dnnInputs[inputIndex], sizeof(float32_t) * numInputElements);
// Allocate device and host memory for DNN outputs
dwBlobSize size1, size2;
dwDNN_getOutputSize(&size1, output1Index, dnn);
dwDNN_getOutputSize(&size2, output2Index, dnn);
size_t numElements1 = size1.batchsize * size1.channels * size1.height * size1.width;
size_t numElements2 = size2.batchsize * size2.channels * size2.height * size2.width;
cudaMalloc(&dnnOutputs[output1Index], sizeof(float32_t) * numElements1);
cudaMalloc(&dnnOutputs[output2Index], sizeof(float32_t) * numElements2);
// Fill dnnInputHost with application data.

Copy DNN input from host buffers to device, then perform DNN inference and copy results back. All operations are performed asynchronously with the host code.

// Enqueue asynchronous copy of network input data from host to device memory.
cudaMemcpyAsync(dnnInputs[inputIndex],, sizeof(float32_t) * numInputElements, cudaMemcpyHostToDevice);
// Begin DNN inference in the currently selected CUDA stream.
dwDNN_infer(,, dnn);
// Enqueue asynchronous copy of the inference results to host memory
cudaMemcpyAsync(dnnOutputHost[output1Index].data(), dnnOutputs[output1Index], sizeof(float32_t) * numElements1, cudaMemcpyDeviceToHost);
cudaMemcpyAsync(dnnOutputHost[output2Index].data(), dnnOutputs[output2Index], sizeof(float32_t) * numElements2, cudaMemcpyDeviceToHost);
// Do something while inference results are being calculated.
// Wait until all pending operations on the CUDA device have finished.
// Inference and memory copies are done. Read results from dnnOutputHost[output1Index] and dnnOutputHost[output2Index].

Finally, free previously allocated memory.

// Free resources.

For more information see: