Vitis AI provides a C++ DpuRunner class with the following interfaces:
std::pair<uint32_t, int> execute_async( const std::vector<TensorBuffer*>& input, const std::vector<TensorBuffer*>& output);Note: For some historical reasons, this function is actually a blocking
function, not an asynchronous non-blocking function.
- Submit input tensors for execution, and output tensors to store
results. The host pointer is passed via the TensorBuffer object. This function
returns a job ID and the status of the function
call.
int wait(int jobid, int timeout);The job ID returned by execute_async is passed to
wait()to block until the job is complete and the results are ready.TensorFormat get_tensor_format() - Query the DpuRunner for the tensor format it expects.
Returns DpuRunner::TensorFormat::NCHW or DpuRunner::TensorFormat::NHWC
std::vector<Tensor*> get_input_tensors() - Query the DpuRunner for the shape and name of the output tensors it expects
for its loaded AI
model.
std::vector<Tensor*> get_output_tensors() - To create a DpuRunner object call the
following:
create_runner(const xir::Subgraph* subgraph, const std::string& mode = "")It returns the following:
std::unique_ptr<Runner>
The input to create_runner is a XIR Subgraph generated by the AI compiler.