Update GradientModifier to be an in-place operation#123
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mzweilin
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Mar 31, 2023
| def __call__(self, grad: torch.Tensor) -> torch.Tensor: | ||
| return grad.sign() | ||
| def __call__(self, parameters: torch.Tensor | Iterable[torch.Tensor]) -> None: | ||
| if isinstance(parameters, torch.Tensor): |
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We wouldn't need a batch-aware GradientModifier if we accept the shared modality-iterable-dispatch mechanism in #115
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@mzweilin: I think it's better to merge this before the generic dispatch mechanism because it updates the tests. |
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What does this PR do?
This PR updates GradientModifier to be an in-place operation like torch.nn.utils.clip_grad_norm_. This also adds a test to make sure Adversary is properly modifying gradients.
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Testing
Please describe the tests that you ran to verify your changes. Consider listing any relevant details of your test configuration.
make testCIFAR10_CNN_Advachieves ~70% accuracyBefore submitting
pre-commit run -acommand without errorsDid you have fun?
Make sure you had fun coding 🙃