Make reusable attack config components

[mzweilin]

What does this PR do?

Currently, it requires full understanding of the config structure to compose an attack algorithm. This PR makes it easier to compose attack algorithms by introducing

• Reusable components, e.g. FGM, PGD and Linf.
• Variable interpolation for lr, max_iters, eps, so we only define once at fixed paths.
• A customized OmegaConf resolver negate to get -eps in config.

Existing attack configurations are updated to demonstrate the simplicity.

Depends on #212

Type of change

Please check all relevant options.

• [x] 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.

• [x] pytest
• [x] 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

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

Did you have fun?

Make sure you had fun coding 🙃

[mzweilin]

I think the examples of composing FGSM and PGD help people understand how MART works. In addition, the reusable components of FGM and PGD make it easier to do a sanity check on other components (e.g. composer, gain function) when composing novel attacks against new models.