How to configure OpenVINO™ launcher

OpenVINO™ launcher is one of the supported wrappers for easily launching models within Accuracy Checker tool. This launcher uses OpenVINO™ Inference Engine as inference backend and accepts for executing networks in OpenVINO™ supported formats.

For enabling OpenVINO™ launcher you need to add framework: dlsdk in launchers section of your configuration file and provide following parameters:

  • device - specifies which device will be used for infer. Supported: CPU, GPU, FPGA, MYRIAD, HDDL, Heterogeneous plugin as HETERO:target_device,fallback_device and Multi device plugin as MULTI:target_device1,target_device2.

    If you have several MYRIAD devices in your machine, you are able to provide specific device id in such way: MYRIAD.<DEVICE_ID> (e.g. MYRIAD.1.2-ma2480)

    It is possible to specify one or more devices via -td, --target devices command line argument. Target device will be selected from command line (in case when several devices provided, evaluations will be run one by one with all specified devices).

  • model - path to xml file with model for your topology or compiled executable network.

  • weights - path to bin file with weights for your topology (Optional, the argument can be omitted if bin file stored in the same directory with model xml or if you use compiled blob).

  • adapter - approach how raw output will be converted to representation of dataset problem, some adapters can be specific to framework. You can find detailed instruction how to use adapters here.

Note: You can generate executable blob using compile_tool. Before evaluation executable blob, please make sure that selected device support it.

Additionally you can provide device specific parameters:

  • cpu_extensions (path to extension file with custom layers for cpu). You can also use special key AUTO for automatic search cpu extensions library in the provided as command line argument directory (option -e, --extensions)

  • gpu_extensions (path to extension *.xml file with OpenCL kernel description for gpu).

  • bitstream for running on FPGA.

Launcher understands which batch size will be used from model intermediate representation (IR). If you want to use batch for infer, please, provide model with required batch or convert it using specific parameter in mo_params.

  • allow_reshape_input - parameter, which allows to reshape input layer to data shape (default value is False).

  • reset_memory_state - parameter, which allows resetting internal infer request memory states after inference. State control essential for recurrent networks. (Optional, default is False).

Device config contains device specific options which should be set to Inference Engine. For setting device specific flags, you are able to use -dc or --device_config command line option and provide path to YML file or specify device config directly to Accuracy Checker config file with key device_config in the launchers section. Device config should be represented as dictionary of one of two types:

  1. keys are plugin configuration keys and values are their values respectively. In this way configuration will be applied to current running device.

  2. keys are supported devices and values are plugin configuration for each device. Plugin configuration represented as dictionary where keys are plugin specific configuration keys and values are their values respectively.

Each supported device has own set of supported configuration parameters which can be found on device page in Inference Engine development guide

Note: Since OpenVINO 2020.4 on platforms with native bfloat16 support models will be executed on this precision by default. For disabling this behaviour, you need to use device_config with following configuration:


Device config example can be found here.

Beside that, you can launch model in async_mode, enable this option and optionally provide the number of infer requests (num_requests), which will be used in evaluation process. By default, if num_requests not provided or used value AUTO, automatic number request assignment for specific device will be performed For multi device configuration async mode used always. You can provide number requests for each device as part device specification: MULTI:device_1(num_req_1),device_2(num_req_2) or in num_requests config section (for this case comma-separated list of integer numbers or one value if number requests for all devices equal can be used).

Note: not all models support async execution, in cases when evaluation can not be run in async, the inference will be switched to sync.

Specifying model inputs in config

In case when you model has several inputs you should provide list of input layers in launcher config section using key inputs. Each input description should has following info:

  • name - input layer name in network

  • type - type of input values, it has impact on filling policy. Available options:

    • CONST_INPUT - input will be filled using constant provided in config. It also requires to provide value.

    • IMAGE_INFO - specific key for setting information about input shape to layer (used in Faster RCNN based topologies). You do not need to provide value, because it will be calculated in runtime. Format value is list with N elements of the form [H, W, S], where N is batch size, H - original image height, W - original image width, S - scale of original image (default 1).

    • ORIG_IMAGE_INFO - specific key for setting information about original image size before preprocessing.

    • INPUT - network input for main data stream (e. g. images). If you have several data inputs, you should provide regular expression for identifier as value for specifying which one data should be provided in specific input.

    • LSTM_INPUT - input which should be filled by hidden state from previous iteration. The hidden state layer name should be provided via value parameter.

    • IGNORE_INPUT - input which should be stayed empty during evaluation.

    Optionally you can determine shape of input (by default OpenVINO™ launcher uses info given from network, this option allows override default. It required for running ONNX* models with dynamic input shape on devices, where dynamic shape is not supported), layout in case when your model was trained with non-standard data layout (For OpenVINO™ launcher default layout is NCHW) and precision (Supported precisions: FP32 - float, FP16 - signed shot, U8 - unsigned char, U16 - unsigned short int, I8 - signed char, I16 - short int, I32 - int, I64 - long int).

Launcher configuration in case of conversion from source framework

Launcher may optionally accept model parameters in source framework format which will be converted to Inference Engine IR using Model Optimizer. If you want to use Model Optimizer for model conversion, please view Model Optimizer Developer Guide. You can provide:

  • caffe_model and caffe_weights for Caffe model and weights (*.prototxt and *.caffemodel).

  • tf_model for TensorFlow model (*.pb, *.pb.frozen, *.pbtxt).

  • tf_meta for TensorFlow MetaGraph (*.meta).

  • mxnet_weights for MXNet params (*.params).

  • onnx_model for ONNX model (*.onnx). You also able to pass your ONNX model directly using model option if you do not need Model Optimizer conversion step.

  • kaldi_model for Kaldi model (*.nnet).

In case when you want to determine additional parameters for model conversion (data_type, input_shape and so on), you can use mo_params for arguments with values and mo_flags for positional arguments like legacy_mxnet_model. Full list of supported parameters you can find in Model Optimizer Developer Guide.

Model will be converted before every evaluation. You can provide converted_model_dir for saving converted model in specific folder, otherwise, converted models will be saved in path provided via -C command line argument or source model directory.

Configuration example

OpenVINO™ launcher config example:

  - framework: dlsdk
    device: HETERO:FPGA,CPU
    caffe_model: path_to_model/alexnet.prototxt
    caffe_weights: path_to_weights/alexnet.caffemodel
    adapter: classification
      batch: 4
      - reverse_input_channels