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dxoigmn
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May 25, 2023
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| on_run_end() | ||
| """ | ||
| class Adversary(pl.LightningModule): |
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Ideally this would inherit from LitModular. Then we can create pre-defined sequences for training.
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I tried to run adversarial training on 2 GPUs but failed. Then hiding perturber parameters accidentally resolved the issue. python -m mart \
experiment=CIFAR10_CNN_Adv \
trainer=ddp \
trainer.devices=2 \
model.optimizer.lr=0.2 \
trainer.max_steps=2925 \
datamodule.ims_per_batch=256 File "/home/weilinxu/coder/MART/.venv/lib/python3.9/site-packages/torch/nn/parallel/distributed.py", line 807, in <listcomp>
for param_name, param in module.named_parameters(recurse=False)
File "/home/weilinxu/coder/MART/mart/attack/perturber.py", line 83, in named_parameters
raise MisconfigurationException("You need to call configure_perturbation before fit.")
pytorch_lightning.utilities.exceptions.MisconfigurationException: You need to call configure_perturbation before fit. |
mzweilin
reviewed
Jun 1, 2023
mzweilin
reviewed
Jun 1, 2023
mzweilin
reviewed
Jun 2, 2023
| @@ -0,0 +1,2 @@ | |||
| attack_in_eval_mode: | |||
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Shall we make an accurate name for this?
* Fix progress bar display. * Enable progress bar for adversary. * Switch on/off progress bar for adversary in the callback config. * Make a default progress bar for adversary that can be turned off in FGSM. * Display progress bars of adversary in multi-rank. * Display gain instead of loss. * Make process_position configurable and avoid touching the internal variable. * Make renaming metrics configurable.
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What does this PR do?
Right now we treat adversaries as special things with their own loops and callbacks, when really we should just treat them like LightningModules. Doing so means that we can just use a Trainer to fit its parameters. This PR attempts to make that so.
As of dcf7599, there is a bug in adversarial training.Depends upon #146 and #147.
Type of change
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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.
pytestpython -m mart experiment=CIFAR10_CNN_Adv trainer=gpuachieves 71% accuracy.python -m mart experiment=CIFAR10_CNN_Adv trainer=ddp datamodule.world_size=2 trainer.devices=2achieves 71% accuracy.Before submitting
pre-commit run -acommand without errorsDid you have fun?
Make sure you had fun coding 🙃