[Deprecated] Configuration File Description


Post-training Optimization Tool is deprecated since OpenVINO 2023.0. Neural Network Compression Framework (NNCF) is recommended for the post-training quantization instead.

The tool is designed to work with the configuration file where all the parameters required for the optimization are specified. These parameters are organized as a dictionary and stored in a JSON file. JSON file allows using comments that are supported by the jstyleson Python package. Logically all parameters are divided into three groups:

  • Model parameters that are related to the model definition (e.g. model name, model path, etc.)

  • Engine parameters that define parameters of the engine which is responsible for the model inference and data preparation used for optimization and evaluation (e.g. preprocessing parameters, dataset path, etc.)

  • Compression parameters that are related to the optimization algorithm (e.g. algorithm name and specific parameters)

Model Parameters

"model": {
        "model_name": "model_name",
        "model": "<MODEL_PATH>",
        "weights": "<PATH_TO_WEIGHTS>"

This section contains only three parameters:

  • "model_name" - string parameter that defines a model name, e.g. "MobileNetV2"

  • "model" - string parameter that defines the path to an input model topology (.xml)

  • "weights" - string parameter that defines the path to an input model weights (.bin)

Engine Parameters

"engine": {
        "type": "accuracy_checker",
        "config": "./configs/examples/accuracy_checker/mobilenet_v2.yaml"

The main parameter is "type" which can take two possible options: "accuracy_checher" (default) or "simplified". It specifies the engine used for model inference and validation (if supported):

  • Simplified mode engines. These engines can be used only with the DefaultQuantization algorithm to get a fully quantized model. They do not use the Accuracy Checker tool and annotation. In this case, the following parameters are applicable:

    • "data_source" specifies the path to the directory​ where the calibration data is stored.

    • "layout" - (Optional) Layout of input data. Supported values: ["NCHW", "NHWC", "CHW", "CWH"]​.

  • Accuracy Checker engine. It relies on the Deep Learning Accuracy Validation Framework (Accuracy Checker) when inferencing DL models and working with datasets.

If you have annotations, you can benefit from this mode by measuring accuracy. When this mode is selected, you can use the accuracy-aware algorithms family. There are two options to define engine parameters in this mode:

  • Refer to the existing Accuracy Checker configuration file which is represented by the YAML file. It can be a file used for full-precision model validation. In this case, you should define only the "config" parameter containing the path to the AccuracyChecker configuration file.

  • Define all the required Accuracy Checker parameters directly in the JSON file. In this case, POT just passes the corresponding dictionary of parameters to the Accuracy Checker when instantiating it. For more details, refer to the corresponding Accuracy Checker information and examples of configuration files provided with the tool: 8-bit quantization of SSD-MobileNet model

Compression Parameters

For more details on the parameters of a particular optimization algorithm, see descriptions of Default Quantization and Accuracy-aware Quantizatoin methods.

Examples of the Configuration File

For a quick start, many examples of configuration files are provided here. There, you can find ready-to-use configurations for the models from various domains: Computer Vision (Image Classification, Object Detection, Segmentation), Natural Language Processing, and Recommendation Systems. We put configuration files for the models which require non-default configuration settings to get accurate results.

For details on how to run the Post-Training Optimization Tool with a sample configuration file, see the example.