Post-training Optimization Toolkit (POT) is designed to accelerate the inference of deep learning models by applying special methods without model retraining or fine-tuning, like post-training quantization. Therefore, the tool does not require a training dataset or a pipeline. To apply post-training algorithms from the POT, you need:
The tool is aimed to fully automate the model transformation process without changing the model structure. The POT is available only in the Intel® distribution of OpenVINO™ toolkit and is not opensourced. For details about the low-precision flow in OpenVINO™, see the Low Precision Optimization Guide.
Post-training Optimization Toolkit includes a standalone command-line tool and a Python* API that provide the following key features:
TIP: You also can work with the Post-training Optimization Toolkit inside the OpenVINO™ Deep Learning Workbench (DL Workbench). DL Workbench is a platform built upon OpenVINO™ and provides a web-based graphical environment that enables you to optimize, fine-tune, analyze, visualize, and compare performance of deep learning models on various Intel® architecture configurations. In the DL Workbench, you can use most of OpenVINO™ toolkit components.
Proceed to an easy installation from Docker Hub to get started.
For benchmarking results collected for the models optimized with POT tool, see INT8 vs FP32 Comparison on Select Networks and Platforms.
Further documentation presumes that you are familiar with the basic deep learning concepts, such as model inference, dataset preparation, model optimization, as well as with the OpenVINO™ toolkit and its components such as Model Optimizer and Accuracy Checker Tool.
Before running the POT, convert your pretrained model into the OpenVINO™ IR format with the Model Optimizer. In addition, it is highly recommended to use the Accuracy Checker Tool to make sure that the model can be successfully inferred and achieves similar accuracy numbers as the reference model from the original framework.
To run the command-line Post-training Optimization Tool:
configsfolder. To simplify this step, use the Accuracy Checker configuration file for the floating-point model and refer to it when necessary. See Post-Training Optimization Best Practices.
For all available usage options, use the `-h`, `--help` arguments or refer to the Command-Line Arguments below.
resultsfolder that is created in the same directory where the tool is run from. Use the
-eoption to evaluate the accuracy directly from the tool.
See the How to Run Examples tutorial about how to run a particular example of 8-bit quantization with the POT.
The following command-line options are available to run the tool:
|Optional. Show help message and exit.|
|Path to a config file with task- or model-specific parameters.|
|Optional. Evaluate model on the whole dataset after optimization.|
|Optional. A directory where results are saved. Default: |
|Optional. Save the original full-precision model.|
|Optional. Save results directly to output directory without additional subfolders.|
|Optional. Log level to print. Default: INFO.|
|Optional. Disable CL logging and enable progress bar.|
|Optional. Switch model quantization progress display to a multiline mode. Use with third-party components.|
|Optional. Keep Convolution, Deconvolution and FullyConnected weights uncompressed. Use with third-party components.|