Semantic Robustness

[dxoigmn]

What does this PR do?

This PR implements a semantic robustness callback for anomalib as described in https://arxiv.org/abs/2405.07969. We make one critical design choice different from the paper: we supply the model with an augmented image and mask for computing metrics. Additionally, anomalib does not implement patching, so all images are square by default. We believe these changes shouldn't significantly alter results since 1) rotating masks is fine so long as we impute pixel mask values as "non-anomalous" or 0 valued.

Type of change

Please check all relevant options.

• [ ] Improvement (non-breaking)
• [ ] Bug fix (non-breaking)
• [ ] New feature (non-breaking)
• [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
• [ ] This change requires a documentation update

Testing

Please describe the tests that you ran to verify your changes. Consider listing any relevant details of your test configuration.

• [ ] pytest
• [ ] CUDA_VISIBLE_DEVICES=0 python -m mart experiment=CIFAR10_CNN_Adv trainer=gpu trainer.precision=16 reports 70% (21 sec/epoch).
• [ ] CUDA_VISIBLE_DEVICES=0,1 python -m mart experiment=CIFAR10_CNN_Adv trainer=ddp trainer.precision=16 trainer.devices=2 model.optimizer.lr=0.2 trainer.max_steps=2925 datamodule.ims_per_batch=256 datamodule.world_size=2 reports 70% (14 sec/epoch).

Before submitting

• [ ] The title is self-explanatory and the description concisely explains the PR
• [ ] My PR does only one thing, instead of bundling different changes together
• [ ] I list all the breaking changes introduced by this pull request
• [ ] I have commented my code
• [ ] I have added tests that prove my fix is effective or that my feature works
• [ ] New and existing unit tests pass locally with my changes
• [ ] I have run pre-commit hooks with pre-commit run -a command without errors

Did you have fun?

Make sure you had fun coding 🙃

[dxoigmn]

@mzweilin @mariusarvinte: This is ready for review.

[dxoigmn]

@mzweilin: Would be nice to get this merged. Really need help on how to package this.

[dxoigmn]

LGTM! Thanks for the README updates!