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 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:
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) 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):
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. mobilenetV2_tf_int8_simple_mode.json
file from the <POT_DIR>/configs/examples/quantization/classification/
directory."config"
parameter containing a path to the AccuracyChecker configuration file.This section defines optimization algorithms and their parameters. For more details about parameters of the concrete optimization algorithm, please refer to the corresponding documentation.
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.