Make the Adversary Trainer accept loggers.

[mzweilin]

What does this PR do?

Make Adversary's Trainer accept loggers, so that we can log gradients of perturbation, etc.

Example

CUDA_VISIBLE_DEVICES=0 \
python -m mart \
experiment=CIFAR10_CNN_Adv \
fit=false \
trainer=gpu \
+trainer.limit_test_batches=1 \
+callbacks@model.modules.input_adv_test.callbacks=gradient_monitor \
+model.modules.input_adv_test.callbacks.gradient_monitor.frequency=1 \
+logger@model.modules.input_adv_test.logger=[tensorboard]

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.

• Test A
• Test B

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

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[dxoigmn]

LGTM! This will be useful when debugging adversarial training.

The base branch was changed.