openvino-xai 1.0.0rc1

OpenVINO™ Explainable AI (XAI) Toolkit: Visual Explanation for OpenVINO Models

OpenVINO™ Explainable AI Toolkit - OpenVINO XAI

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Features • Install • Quick Start • License • Documentation

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OpenVINO™ Explainable AI (XAI) Toolkit provides a suite of XAI algorithms for visual explanation of OpenVINO™ Intermediate Representation (IR) models.

Given OpenVINO models and input images, OpenVINO XAI generates saliency maps which highlights regions of the interest in the inputs from the models' perspective to help users understand the reason why the complex AI models output such responses.

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Features

What's new in v1.0.0

• Support generation of classification and detection per-class and per-image saliency maps
• Enable White-Box (ReciproCAM) and Black-Box (RISE) eXplainable AI algorithms
• Support CNNs and Transformer-based architectures (validation on diverse set of timm models)
• Enable Explainer (stateful object) as the main interface for XAI algorithms
• Support AUTO mode by default to detect the best XAI method for given models
• Expose insert_xai functional API to support XAI head insertion for OpenVINO IR models

Please refer to the change logs for the full release history.

Supported XAI methods

At the moment, Image Classification and Object Detection tasks are supported for the Computer Vision domain. Black-Box (model agnostic but slow) methods and White-Box (model specific but fast) methods are supported:

Domain	Task	Type	Algorithm	Links
Computer Vision	Image Classification	White-Box	ReciproCAM	arxiv / src
			VITReciproCAM	arxiv / src
			ActivationMap	experimental / src
		Black-Box	RISE	arxiv / src
	Object Detection	White-Box	ClassProbabilityMap	experimental / src

Supported explainable models

Most of CNNs and Transformer models from Pytorch Image Models (timm) are supported and validated.

Please refer to the following known issues for unsupported models and reasons.

• OpenVINO IR branch insertion not working for models converted directly from torch models with OVC (#26)
• Runtime error from ONNX / OpenVINO IR models while conversion or inference for XAI (#29)
• Models not supported by white box XAI methods (#30)

NOTE: GenAI / LLMs would be also supported incrementally in the upcoming releases.

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Installation

NOTE: OpenVINO XAI works on Python 3.10 or higher

Set up environment

# Create virtual env.
python3.10 -m venv .ovxai

# Activate virtual env.
source .ovxai/bin/activate

Install from PyPI package

# Base package (for normal use):
pip install openvino_xai

# Dev package (for development):
pip install openvino_xai[dev]

Install from source

# Clone the source repository
git clone https://github.com/openvinotoolkit/openvino_xai.git
cd openvino_xai

# Editable mode (for development):
pip install -e .[dev]

Verify installation

# Run tests
pytest -v -s ./tests/unit

# Run code quality checks
pre-commit run --all-files

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Quick Start

Hello, OpenVINO XAI

Let's imagine the case that our OpenVINO IR model is up and running on a inference pipeline. While watching the outputs, we may want to analyze the model's behavior for debugging or understanding purposes.

By using the OpenVINO XAI Explainer, we can visualize why the model gives such responses. In this example, we are trying to know the reason why the model outputs a cheetah label for the given input image.

import cv2
import numpy as np
import openvino.runtime as ov
import openvino_xai as xai

# Load the model
ov_model: ov.Model = ov.Core().read_model("mobilenet_v3.xml")

# Load the image to be analyzed
image: np.ndarray = cv2.imread("tests/assets/cheetah_person.jpg")
image = cv2.resize(image, dsize=(224, 224))
image = np.expand_dims(image, 0)

# Create the Explainer object
explainer = xai.Explainer(
    model=ov_model,
    task=xai.Task.CLASSIFICATION,
)

# Generate saliency map for the label of interest
explanation: xai.Explanation = explainer(
    data=image,
    targets=293,  # (cheetah), accepts label indices or actual label names if label_names provided
    overlay=True,  # saliency map overlay over the input image, defaults to False
)

# Save saliency maps to output directory
explanation.save(dir_path="./output")

Original image	Explained image

We can see that model is focusing on the body or skin area of the animals to tell if this image contains actual cheetahs.

More advanced use-cases

Users could tweak the basic use-case according to their purpose, which include but not limited to:

• Select XAI mode (White-Box or Black-Box) or even specific method which are automatically decided by default
• Provide custom model pre/post processing functions like resize and normalizations which the model expects
• Customize output image visualization options
• Explain multiple class targets, passing them as label indices or as actual label names
• Call explainer multiple times to explain multiple images or to use different targets
• Using insert_xai API, insert XAI head to your OpenVINO IR model and get additional saliency map output in the same inference pipeline

Please find more options and scenarios in the following links:

• OpenVINO XAI User Guide
• (TBD) OpenVINO Notebook - XAI Basic
• (TBD) OpenVINO Notebook - XAI Deep Dive

Playing with the examples

Please look around the runnable example scripts and play with them to get used to the Explainer APIs.

# Prepare models by running tests (need "pip install openvino_xai[dev]" extra option)
# Models are downloaded and stored in .data/otx_models
pytest tests/test_classification.py

# Run a bunch of classification examples
# All outputs will be stored in the corresponding output directory
python examples/run_classification.py .data/otx_models/mlc_mobilenetv3_large_voc.xml \
tests/assets/cheetah_person.jpg --output output

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Contributing

For those who would like to contribute to the library, please refer to the contribution guide for details.

Please let us know via the Issues tab if you have any issues, feature requests, or questions.

Thank you! We appreciate your support!

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License

OpenVINO™ Toolkit is licensed under Apache License Version 2.0. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.

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Disclaimer

Intel is committed to respecting human rights and avoiding complicity in human rights abuses. See Intel's Global Human Rights Principles. Intel's products and software are intended only to be used in applications that do not cause or contribute to a violation of an internationally recognized human right.

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