About Carlini-Wagner Attack

[khairulislam48]

I tried to run the Carlini-Wagner Attack using following code

#!/usr/bin/env python3
"""
A simple example that demonstrates how to run a single attack against
a PyTorch ResNet-18 model for different epsilons and how to then report
the robust accuracy.
"""
import torchvision.models as models
import eagerpy as ep
from foolbox import PyTorchModel, accuracy, samples
from foolbox.attacks import L2CarliniWagnerAttack


def main() -> None:
    # instantiate a model (could also be a TensorFlow or JAX model)
    model = models.resnet18(pretrained=True).eval()
    preprocessing = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], axis=-3)
    fmodel = PyTorchModel(model, bounds=(0, 1), preprocessing=preprocessing)

    # get data and test the model
    # wrapping the tensors with ep.astensors is optional, but it allows
    # us to work with EagerPy tensors in the following
    images, labels = ep.astensors(*samples(fmodel, dataset="imagenet", batchsize=16))
    clean_acc = accuracy(fmodel, images, labels)
    print(f"clean accuracy:  {clean_acc * 100:.1f} %")

    # apply the attack
    #attack = LinfPGD()
    attack = L2CarliniWagnerAttack()

    epsilons = [
        0.0,
        0.0002,
        0.0005,
        0.0008,
        0.001,
        0.0015,
        0.002,
        0.003,
        0.01,
        0.1,
        0.3,
        0.5,
        1.0,
    ]
    raw_advs, clipped_advs, success = attack(fmodel, images, labels, epsilons=epsilons)

    print(attack)

    # calculate and report the robust accuracy (the accuracy of the model when
    # it is attacked)
    robust_accuracy = 1 - success.float32().mean(axis=-1)
    print("robust accuracy for perturbations with")
    for eps, acc in zip(epsilons, robust_accuracy):
        print(f"  Linf norm ≤ {eps:<6}: {acc.item() * 100:4.1f} %")



if __name__ == "__main__":
    main()

This code is running for more than 30 minutes whereas other attacks gave output in 3-4 minutes.
What is wrong in this code?