Sync Benchmark C++ Sample

@sphinxdirective

This sample demonstrates how to estimate performance of a model using Synchronous Inference Request API. It makes sense to use synchronous inference only in latency oriented scenarios. Models with static input shapes are supported. Unlike :doc:demos <omz_demos> this sample doesn't have other configurable command line arguments. Feel free to modify sample's source code to try out different options.

The following C++ API is used in the application:

+--------------------------+----------------------------------------------+----------------------------------------------+ | Feature | API | Description | +==========================+==============================================+==============================================+ | OpenVINO Runtime Version | ov::get_openvino_version | Get Openvino API version. | +--------------------------+----------------------------------------------+----------------------------------------------+ | Basic Infer Flow | ov::Core, ov::Core::compile_model, | Common API to do inference: compile a model, | | | ov::CompiledModel::create_infer_request, | create an infer request, | | | ov::InferRequest::get_tensor | configure input tensors. | +--------------------------+----------------------------------------------+----------------------------------------------+ | Synchronous Infer | ov::InferRequest::infer, | Do synchronous inference. | +--------------------------+----------------------------------------------+----------------------------------------------+ | Model Operations | ov::CompiledModel::inputs | Get inputs of a model. | +--------------------------+----------------------------------------------+----------------------------------------------+ | Tensor Operations | ov::Tensor::get_shape, | Get a tensor shape and its data. | | | ov::Tensor::data | | +--------------------------+----------------------------------------------+----------------------------------------------+

+--------------------------------+------------------------------------------------------------------------------------------------+ | Options | Values | +================================+================================================================================================+ | Validated Models | :doc:alexnet <omz_models_model_alexnet>, | | | :doc:googlenet-v1 <omz_models_model_googlenet_v1>, | | | :doc:yolo-v3-tf <omz_models_model_yolo_v3_tf>, | | | :doc:face-detection-0200 <omz_models_model_face_detection_0200> | +--------------------------------+------------------------------------------------------------------------------------------------+ | Model Format | OpenVINO™ toolkit Intermediate Representation | | | (*.xml + *.bin), ONNX (*.onnx) | +--------------------------------+------------------------------------------------------------------------------------------------+ | Supported devices | :doc:All <openvino_docs_OV_UG_supported_plugins_Supported_Devices> | +--------------------------------+------------------------------------------------------------------------------------------------+ | Other language realization | :doc:Python <openvino_inference_engine_ie_bridges_python_sample_sync_benchmark_README> | +--------------------------------+------------------------------------------------------------------------------------------------+

How It Works ####################

The sample compiles a model for a given device, randomly generates input data, performs synchronous inference multiple times for a given number of seconds. Then processes and reports performance results.

You can see the explicit description of each sample step at :doc:Integration Steps <openvino_docs_OV_UG_Integrate_OV_with_your_application> section of "Integrate OpenVINO™ Runtime with Your Application" guide.

Building ####################

To build the sample, please use instructions available at :doc:Build the Sample Applications <openvino_docs_OV_UG_Samples_Overview> section in OpenVINO™ Toolkit Samples guide.

Running ####################

.. code-block:: sh

sync_benchmark <path_to_model>

To run the sample, you need to specify a model:

You can use :doc:public <omz_models_group_public> or :doc:Intel's <omz_models_group_intel> pre-trained models from the Open Model Zoo. The models can be downloaded using the :doc:Model Downloader <omz_tools_downloader>.

.. note::

Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (*.xml + *.bin) using the :doc:Model Optimizer tool <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>.

The sample accepts models in ONNX format (.onnx) that do not require preprocessing.

Example ++++++++++++++++++++

Install the openvino-dev Python package to use Open Model Zoo Tools:

.. code-block:: sh

python -m pip install openvino-dev[caffe]
Download a pre-trained model using:

.. code-block:: sh

omz_downloader --name googlenet-v1
If a model is not in the IR or ONNX format, it must be converted. You can do this using the model converter:

.. code-block:: sh

omz_converter --name googlenet-v1
Perform benchmarking using the googlenet-v1 model on a CPU:

.. code-block:: sh

sync_benchmark googlenet-v1.xml

Sample Output ####################

The application outputs performance results.

.. code-block:: sh

[ INFO ] OpenVINO: [ INFO ] Build ................................. [ INFO ] Count: 992 iterations [ INFO ] Duration: 15009.8 ms [ INFO ] Latency: [ INFO ] Median: 14.00 ms [ INFO ] Average: 15.13 ms [ INFO ] Min: 9.33 ms [ INFO ] Max: 53.60 ms [ INFO ] Throughput: 66.09 FPS