Incorrect preprocessing for ImageNet-C evaluation

[imrahulr]

I see that the ImageNet-C evaluation uses the preprocessing: Resize(256)+CenterCrop(224)+ToTensor().

robustbench/robustbench/data.py

Lines 146 to 154 in 61ce9e9

	def load_imagenetc(
	n_examples: Optional[int] = 5000,
	severity: int = 5,
	data_dir: str = './data',
	shuffle: bool = False,
	corruptions: Sequence[str] = CORRUPTIONS,
	prepr: str = 'Res256Crop224'
	) -> Tuple[torch.Tensor, torch.Tensor]:
	transforms_test = PREPROCESSINGS[prepr]

This causes discrepancies with the scores reported in the original papers (DeepAugment, AugMix, Standard RN-50). The ImageNet-C dataset already contains 224x224 images and hence only ToTensor() should be used for consistency.

Fixing prepr='none' in load_imagenetc should solve the issue (assuming all the models are capable of handling 224x224 images as input).

[max-andr]

The suggestion of fixing prepr='none' in load_imagenetc definitely makes sense for the current models! We'll look into this!

[max-andr]

Sorry, took us a long time. Should be fixed with this PR: #85.