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IDC

This repository contains the official implementation of the IDC Framework proposed in our paper "Input Distribution Coverage: Measuring Feature Interaction Adequacy in Neural Network Testing".

Datasets

Components

  1. Out-of-distribution data detection
  2. Variational Autoencoder
  3. Combinatorial Coverage Measurement

Out-of-distribution (OOD) data detection

We used the Likelihood-Regret[1] based out-of-distribution (OOD) filter in the framework. Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder has the code and pretrained models available.

The included code is for measuring the total t-way coverage of the test datasets of MNIST, Fashion-MNIST and CIFAR10. Since the test datasets are assumed to be in-distribution data, we have not included the Likelihood Regret code base in this repository. When using this framework for other test sets, please follow the instructions from the Likelihood-Regret repository for filtering out the OOD inputs from the test sets.

Variational Autoencoder (VAE)

FactoVAE[2] and $\beta$-TCVAE[3] models from https://github.com/YannDubs/disentangling-vae repository is used in this work.

Combinatorial Coverage Measurement

Combinatorial Coverage Measurement (CCM) Command Line Tool from https://github.com/usnistgov/combinatorial-testing-tools is used for measuring the total t-way coverage of a test set.

Copy ccmcl.jar from CCM Command Line Tool directory of the repository to the current project directory. Running this tool requires Java installed on the machine.

Usage

  • Create virtual environment

    python -m venv idc

  • Activate virtual environment

    source idc/bin/activate

  • Install the required packages using the requirements.txt file.

    pip install -r requirements.txt

  • Run measure_coverage.py to measure the total t-way coverage of the test datasets of MNIST, Fashion-MNIST and CIFAR10. Refer to results directory for supported VAE models.

  python measure_coverage.py [vae] --dataset [mnist/fmnist/cifar10] --no_bins [intervals] --ways [ways] --density [target density: range[0,1]]

  E.g. : python measure_coverage.py btcvae_mnist_6 --dataset mnist --no_bins 20 --ways 3 --density 0.9999

Measuring test coverage of custom test sets

Test coverage of custom test sets can be measured by setting the file path argument of measure_coverage.py. Use Likelihood Regret to filter out the OOD test inputs, and convert the in-distribution test inputs into numpy format. Run the below command.

python measure_coverage.py [vae] --dataset [mnist/fmnist/cifar10] --no_bins [intervals] --ways [ways] --density [target density: range[0,1]] --path [numpy file path]

E.g. python measure_coverage.py btcvae_mnist_6 --dataset mnist --no_bins 20 --ways 3 --density 0.9999 --path ./custom_testset.npy

References

[1] Xiao, Zhisheng, Qing Yan, and Yali Amit. "Likelihood regret: An out-of-distribution detection score for variational auto-encoder." Advances in neural information processing systems 33 (2020): 20685-20696.

[2] Kim, Hyunjik, and Andriy Mnih. "Disentangling by factorising." International Conference on Machine Learning. PMLR, 2018.

[3] Chen, Ricky TQ, et al. "Isolating sources of disentanglement in variational autoencoders." Advances in neural information processing systems 31 (2018).