Comments (1)
Your provided implementation is based on the assumption that dataset
is actually torchvision.datasets.VisionDataset rather than torch.utils.data.Dataset
(especially for Subset
), while this assumption is not always true.
Not every dataset has target_transform
method or targets
attribute.
Especially since pytorch team is gradually deprecating the dataset convention and use the new datapipe style, I don't think it's a good idea to make trojanzoo function rely on such concrete internal method and attribute.
But what you claim is correct, current implementation is too slow for ImageNet.
It'll be perfect if we can find a solution that works for future ImageNet dataset as well. The old ImageNet dataset (ImageFolder style) will be deprecated next year after pytorch 2.0 .
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Related Issues (20)
- BackdoorAttack class has no argument for source_class HOT 1
- Install newest version fail HOT 1
- Using a custom model HOT 4
- RuntimeError: Dataset not found or corrupted. You can use download=True to download it HOT 10
- Clean label attack accuracy is wrong HOT 5
- In new push model path is not working HOT 1
- badnet folder information HOT 1
- [Error] When I test Neural Cleanse i got a error HOT 2
- Is it possible to apply methods to graph? HOT 6
- Input aware dynamic backdoor error HOT 5
- trojanvision.datasets.ImageFolder HOT 1
- Possible bug: target_class not changed when computing ASR for reversed triggers HOT 2
- problem about saving the intermediate results and config problem HOT 6
- strange mark saved HOT 2
- Hyperparameters for training Resnet18 on CIFAR10? HOT 1
- STRIP implementation doesn't match original codebase HOT 1
- Attack saving and loading is not working HOT 2
- Comp version of networks HOT 2
- Unable to Access Triggered Dataset in BadNet Attack HOT 5
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