Quantizing Model


This document describes how to apply model quantization with the Default Quantization method without accuracy control using an unannotated dataset. To use this method, you need to create a Python* script using an API of Post-Training Optimization Tool (POT) and implement data preparation logic and quantization pipeline. In case you are not familiar with Python*, you can try command-line interface of POT which is designed to quantize models from OpenVINO Model Zoo. The figure below shows the common workflow of the quantization script implemented with POT API.


The script should include three basic steps:

  1. Prepare data and dataset interface

  2. Select quantization parameters

  3. Define and run quantization process

Prepare data and dataset interface

In most cases, it is required to implement only openvino.tools.pot.DataLoader interface which allows acquiring data from a dataset and applying model-specific pre-processing providing access by index. Any implementation should override the following methods:

  • __len__(), returns the size of the dataset

  • __getitem__(), provides access to the data by index in range of 0 to len(self). It also can encapsulate the logic of model-specific pre-processing. The method should return data in the following format:

    • (data, annotation)

where data is the input that is passed to the model at inference so that it should be properly preprocessed. data can be either numpy.array object or dictionary, where the key is the name of the model input and value is numpy.array which corresponds to this input. Since annotation is not used by the Default Quantization method this object can be None in this case.

You can wrap framework data loading classes by openvino.tools.pot.DataLoader interface which is usually straightforward. For example, torch.utils.data.Dataset has a similar interface as openvino.tools.pot.DataLoader so that its TorchVision implementations can be easily wrapped by POT API.


Model-specific preprocessing, for example, mean/scale normalization can be embedded into the model at the conversion step using Model Optimizer component. This should be considered during the implementation of the DataLoader interface to avoid “double” normalization which can lead to the loss of accuracy after optimization.

The code example below defines DataLoader for three popular use cases: images, text, and audio.

import os

import numpy as np
import cv2 as cv

from openvino.tools.pot import DataLoader

class ImageLoader(DataLoader):
    """ Loads images from a folder """
    def __init__(self, dataset_path):
        # Use OpenCV to gather image files
        # Collect names of image files
        self._files = []
        all_files_in_dir = os.listdir(dataset_path)
        for name in all_files_in_dir:
            file = os.path.join(dataset_path, name)
            if cv.haveImageReader(file):

        # Define shape of the model
        self._shape = (224,224)

    def __len__(self):
        """ Returns the length of the dataset """
        return len(self._files)

    def __getitem__(self, index):
        """ Returns image data by index in the NCHW layout
        Note: model-specific preprocessing is omitted, consider adding it here
        if index >= len(self):
            raise IndexError("Index out of dataset size")

        image = cv.imread(self._files[index]) # read image with OpenCV
        image = cv.resize(image, self._shape) # resize to a target input size
        image = np.expand_dims(image, 0)  # add batch dimension
        image = image.transpose(0, 3, 1, 2)  # convert to NCHW layout
        return image, None   # annotation is set to None
import os
from pathlib import Path

from datasets import load_dataset      #pip install datasets
from transformers import AutoTokenizer #pip install transformers

from openvino.tools.pot import DataLoader

class TextLoader(DataLoader):
    """ Loads content of .txt files from a folder """
    def __init__(self, dataset_path):
        # HuggingFace dataset API is used to process text files
        # Collect names of text files
        extension = ".txt"
        files = sorted(str(p.stem) for p in
            Path(dataset_path).glob("\*" + extension))
        files = [os.path.join(dataset_path, file + extension) for file in files]
        self._dataset = load_dataset('text', data_files=files)
        # replace with your tokenizer
        self._tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
        self._dataset = self._dataset.map(self._encode, batched=False)
        # replace with names of model inputs
                    columns=['input_ids', 'token_type_ids', 'attention_mask'])

    def _encode(self, examples):
        """ Tokenization of the input text """
        return self._tokenizer(examples['text'], truncation=True, padding='max_length')

    def __len__(self):
        """ Returns the length of the dataset """
        return len(self._dataset['train'])

    def __getitem__(self, index):
        """ Returns data by index as a (dict[str, np.array], None) """
        if index >= len(self):
            raise IndexError("Index out of dataset size")

        data = self._dataset['train'][index]
        return {'input_ids': data['input_ids'],
                'token_type_ids': data['token_type_ids'],
                'attention_mask': data['attention_mask']}, None # annotation is set to None
import os
from pathlib import Path

import torchaudio # pip install torch torchaudio

from openvino.tools.pot import DataLoader

class AudioLoader(DataLoader):
    """ Loads content of .wav files from a folder """
    def __init__(self, dataset_path):
        # Collect names of wav files
        self._extension = ".wav"
        self._dataset_path = dataset_path
        self._files = sorted(str(p.stem) for p in
            Path(self._dataset_path).glob("\*" + self._extension))

    def __len__(self):
        """ Returns the length of the dataset """
        return len(self._files)

    def __getitem__(self, index):
        """ Returns wav data by index
        Note: model-specific preprocessing is omitted, consider adding it here
        if index >= len(self):
            raise IndexError("Index out of dataset size")

        file_name = self._files[index] + self._extension
        file_path = os.path.join(self._dataset_path, file_name)
        waveform, _ = torchaudio.load(file_path) # use a helper from torchaudio to load data
        return waveform.numpy(), None   # annotation is set to None

Select quantization parameters

Default Quantization algorithm has mandatory and optional parameters which are defined as a dictionary:

    "name": "DefaultQuantization",
    "params": {
        "target_device": "ANY",
        "stat_subset_size": 300
  • "target_device" - currently, only two options are available: "ANY" (or "CPU") - to quantize model for CPU, GPU, or VPU, and "GNA" - for inference on GNA.

  • "stat_subset_size" - size of data subset to calculate activations statistics used for quantization. The whole dataset is used if no parameter specified. We recommend using not less than 300 samples.

Full specification of the Default Quantization method is available in this document.

Run quantization

POT API provides its own methods to load and save model objects from OpenVINO Intermediate Representation: load_model and save_model. It also has a concept of Pipeline that sequentially applies specified optimization methods to the model. create_pipeine method is used to instantiate a Pipeline object. A code example below shows a basic quantization workflow:

from openvino.tools.pot import IEEngine
from openvino.tools.pot load_model, save_model
from openvino.tools.pot import compress_model_weights
from openvino.tools.pot import create_pipeline

# Model config specifies the model name and paths to model .xml and .bin file
model_config =
    "model_name": "model",
    "model": path_to_xml,
    "weights": path_to_bin,

# Engine config
engine_config = {"device": "CPU"}

algorithms = [
        "name": "DefaultQuantization",
        "params": {
            "target_device": "ANY",
            "stat_subset_size": 300

# Step 1: Implement and create user's data loader
data_loader = ImageLoader("<path_to_images>")

# Step 2: Load model
model = load_model(model_config=model_config)

# Step 3: Initialize the engine for metric calculation and statistics collection.
engine = IEEngine(config=engine_config, data_loader=data_loader)

# Step 4: Create a pipeline of compression algorithms and run it.
pipeline = create_pipeline(algorithms, engine)
compressed_model = pipeline.run(model=model)

# Step 5 (Optional): Compress model weights to quantized precision
#                     to reduce the size of the final .bin file.

# Step 6: Save the compressed model to the desired path.
# Set save_path to the directory where the model should be saved
compressed_model_paths = save_model(

The output of the script is the quantized model that can be used for inference in the same way as the original full-precision model.

If accuracy degradation after applying the Default Quantization method is high, it is recommended to try tips from Quantization Best Practices document or use Accuracy-aware Quantization method.

Quantizing cascaded models

In some cases, when the optimizing model is a cascaded model, i.e. consists of several submodels, for example, MT-CNN, you will need to implement a complex inference pipeline that can properly handle different submodels and data flow between them. POT API provides an Engine interface for this purpose which allows customization of the inference logic. However, we suggest inheriting from IEEngine helper class that already contains all the logic required to do the inference based on OpenVINO Python API. See the following example.