A big missing feature of MART is the ability to generate universal perturbations. This PR tries to change that.
The way universal perturbations should work is that we "train" a perturbation on a dataset and frozen model. Right now we can only generate perturbations in "test" mode, however. This is because it is unclear how to "freeze" a model in MART.
python -m mart experiment=COCO_TorchvisionFasterRCNN_ShapeShifter trainer=gpu
My sense is that Perturber should really just be a LitModular. That will greatly unify everything and make it easier to implement universal perturbations.
This PR depends upon the following PRs:
[ ] #141
[ ] #154
[x] #174
[x] #175
[x] #176
[x] #184
Type of change
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[x] Improvement (non-breaking)
[ ] Bug fix (non-breaking)
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[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] This change requires a documentation update
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What does this PR do?
A big missing feature of MART is the ability to generate universal perturbations. This PR tries to change that.
The way universal perturbations should work is that we "train" a perturbation on a dataset and frozen model. Right now we can only generate perturbations in "test" mode, however. This is because it is unclear how to "freeze" a model in MART.
My sense is that
Perturbershould really just be aLitModular. That will greatly unify everything and make it easier to implement universal perturbations.This PR depends upon the following PRs:
Type of change
Please check all relevant options.
Testing
Please describe the tests that you ran to verify your changes. Consider listing any relevant details of your test configuration.
Before submitting
pre-commit run -acommand without errorsDid you have fun?
Make sure you had fun coding 🙃