Configuration File Description

In the instructions below, the Post-Training Optimization Tool directory <INSTALL_DIR>/deployment_tools/tools/post_training_optimization_toolkit is referred to as <POT_DIR>. <INSTALL_DIR> is the directory where Intel® Distribution of OpenVINO™ toolkit is installed.

The toolkit 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) and "simplified", which specify the engine that is used for model inference and validation (if supported):

• Simplified engine. This engine can be used only with DefaultQuantization algorithm to get fully quantized model using a subset of images. It does not use the AccuracyChecker tool and annotation. To measure accuracy, you should implement your own validation pipeline with OpenVINO API.
  
  • To run the simplified mode, define engine section similar to the example mobilenetV2_tf_int8_simple_mode.json file from the <POT_DIR>/configs/examples/quantization/classification/ directory.
• AccuracyChecker engine. It relies on the Deep Learning Accuracy Validation Framework (AccuracyChecker) when inferencing DL models and working with datasets. The benefit of this mode is you can compute accuracy in case you have annotations. It is possible to use accuracy aware algorithms family when this mode is selected. There are two options to define engine parameters in that mode:
  • Refer to the existing AccuracyChecker 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 a path to the AccuracyChecker configuration file.
  • Define all the required AccuracyChecker parameters directly in the JSON file. In this case, POT just passes the corresponding dictionary of parameters to the AccuracyChecker when instantiating it. For more details, refer to the corresponding AccuracyChecker information and examples of configuration files provided with the tool:
    • For the SE-ResNet-50 model:
      <POT_DIR>/configs/examples/quantization/classification/se_resnet50_pytorch_int8.json
    • For the SSD-MobileNet model:
      <POT_DIR>/configs/examples/quantization/object_detection/ssd_mobilenetv1_int8.json

Compression Parameters

This section defines optimization algorithms and their parameters. For more details about parameters of the concrete optimization algorithm, please refer to the corresponding documentation.

Examples of the Configuration File

For a quick start, many examples of configuration files are provided and placed to the <POT_DIR>/configs/examples folder. There you can find ready-to-use configurations for the models from various domains: Computer Vision (Image Classification, Object Detection, Segmentation), Natural Language Processing, Recommendation Systems. We basically put configuration files for the models which require non-default configuration settings in order to get accurate results. For details on how to run the Post-Training Optimization Tool with a sample configuration file, see the instructions.