Metadata-Version: 2.1
Name: metaNNvis
Version: 0.0.2
Summary: A meta-tool for cross-framework access to PyTorch and Tensorflow visualisation methods
Project-URL: Homepage, https://github.com/sfluegel05/metaNNvis
Project-URL: Bug Tracker, https://github.com/sfluegel05/metaNNvis/issues
Author-email: Simon Flügel <sfluegel@ovgu.de>
License: MIT License
        
        Copyright (c) 2022 Simon Flügel
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
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License-File: LICENSE
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Requires-Dist: captum>=0.5.0
Requires-Dist: matplotlib>=3.3.3
Requires-Dist: numpy>=1.22.3
Requires-Dist: onnx2keras>=0.1
Requires-Dist: onnx2torch>=1.3.0
Requires-Dist: onnx>=1.11.0
Requires-Dist: seaborn>=0.11.2
Requires-Dist: tensorflow>=2.8.0
Requires-Dist: tf-keras-vis>=0.8.1
Requires-Dist: torch>=1.11.0
Requires-Dist: torchvision>=0.12.0
Description-Content-Type: text/markdown

# Cross-Framework Introspection

Cross-Framework Introspection is a tool for accessing introspection methods for neural networks regardless of the
framework in which the neural network has been built. It is easily extendable and currently supports models from
TensorFlow 2.0 and PyTorch in combination with methods from [Captum](captum.ai) and
[tf-keras-vis](https://github.com/keisen/tf-keras-vis). For more details, see the
[project report](https://github.com/sfluegel05/metaNNvis/blob/main/results/report.md).

## Installation

The latest version of metaNNvis can be installed via pip:

`pip install metaNNvis`

## Usage

For instructions on how to use cross-framework introspection and how to extend it by new methods, see
the [user guide](https://github.com/sfluegel05/metaNNvis/blob/main/results/user_guide.ipynb).

## Available methods

Cross-Framework Introspection currently supports most methods from Captum and all methods from tf-keras-vis. The
supported methods are:

| Method                        | Category |
|-------------------------------| --- |
| **Captum**                    ||
| Integrated Gradients          | primary, layer, neuron  |
| Saliency                      | primary |
| DeepLift                      | primary, layer, neuron  |
| GradientShap                  | primary, layer, neuron  |
| Input X Gradient              | primary |
| Gradient X Activation         | layer |
| Deconvolution                 | primary, neuron |
| Feature Ablation              | primary, layer, neuron  |
| Feature Permutation           | primary |
| Conductance                   | layer, neuron |
| Layer Activation              | layer |
| GradCAM                       | layer |
| Neuron Gradient               | neuron  |
| **tf-keras-vis**              ||
| Activation Maximization       | feature visualization |
| Vanilla Saliency / SmoothGrad | attribution |
| GradCAM                       | attribution |
| GradCAM++                     | attribution |
| ScoreCAM                      | attribution |
| LayerCAM                      | attribution |

Currently not supported are:

| Method                        | Category |
|-------------------------------| --- |
| **Captum** ||
| DeepLiftShap | primary, layer, neuron  |
| Guided Backpropagation | primary, neuron |
| Guided GradCAM | primary |
| Occlusion | primary  |
| Shapley Value Sampling | primary |
| Lime | primary |
| KernelShap | primary |
| Layer Relevance Propagation | primary, layer  |
| Internal Influence | layer |



