vlm_eval/attacks/utils.py

import torch
import torch.nn.functional as F


# some parts of this code are adapted from
# https://github.com/M4xim4l/InNOutRobustness/blob/main/utils/adversarial_attacks/utils.py

def project_perturbation(perturbation, eps, norm):
    if norm in ['inf', 'linf', 'Linf']:
        pert_normalized = torch.clamp(perturbation, -eps, eps)
        return pert_normalized
    elif norm in [2, 2.0, 'l2', 'L2', '2']:
        pert_normalized = torch.renorm(perturbation, p=2, dim=0, maxnorm=eps)
        return pert_normalized
    else:
        raise NotImplementedError(f'Norm {norm} not supported')


def normalize_grad(grad, p):
    if p in ['inf', 'linf', 'Linf']:
        return grad.sign()
    elif p in [2, 2.0, 'l2', 'L2', '2']:
        bs = grad.shape[0]
        grad_flat = grad.view(bs, -1)
        grad_normalized = F.normalize(grad_flat, p=2, dim=1)
        return grad_normalized.view_as(grad)