ShapeShifter for YOLO #182
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This reverts commit 01a2066.
dxoigmn
commented
Jun 23, 2023
dxoigmn
commented
Jun 23, 2023
dxoigmn
commented
Jun 23, 2023
| # We can omit the key of _call_with_args_ if it is the only config. | ||
| module_cfg = {"_call_with_args_": module_cfg} | ||
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| # Add support for calling different functions using dot-syntax |
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Move _call_ functionality to new PR.
dxoigmn
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Jun 23, 2023
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| # Turn tuple of dicts into dict of tuples | ||
| new_targets = {k: tuple(t[k] for t in targets) for k in targets[0].keys()} | ||
| new_targets["list_of_targets"] = targets |
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Would be nice to get rid of this...
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Basically, we either need to decide to accept a List[Tensor] for input and List[Dict[str, Tensor]] for target or Tensor for input and Dict[str, Tensor] for target. The former interface at least allows images with different shapes and sizes even if the underlying network does not.
This was referenced Jun 23, 2023
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dxoigmn
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Jun 28, 2023
| return super().forward(*values, weights=weights) | ||
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| def _total_variation(self, image): | ||
| return torch.mean( |
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Mean doesn't do anything and we should probably normalize this by the size of the patch (e.g., C*H*W).
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What does this PR do?
This obseletes #135 and #177. However, we should wait to merge this until we can remove the detection code and directly use torchvision.
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.
pytestCUDA_VISIBLE_DEVICES=0 python -m mart experiment=CIFAR10_CNN_Adv trainer=gpu trainer.precision=16reports 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=2reports 70% (14 sec/epoch).Before submitting
pre-commit run -acommand without errorsDid you have fun?
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