Hide parameters of Adversary's Perturber from DDP

[mzweilin]

What does this PR do?

• Hide uninitialized parameters from DDP so that we can run adversarial training with DDP.
• Add test examples of adversarial training.

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%.
• 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%.

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]

LGTM!

[dxoigmn]

Why not just CUDA_VISIBLE_DEVICES=0,1 python -m mart experiment=CIFAR10_CNN_Adv trainer=ddp trainer.precision=16 trainer.devices=2 datamodule.world_size=2 ?

[mzweilin]

We don't get speed-up from two-GPUs if we keep the original learning rate and effective batch size.

[mzweilin]

I have added the estimated time per epoch for the two experiments.