eps 8./255. works fine 4./255. does not work fine. about auto-attack HOT 12 CLOSED

Warning: it seems that more target classes (9) than possible (3) are used in APGD-T! Also, it seems that too many target classes (9) are used in FAB-T (3 possible)!

using standard version including apgd-ce, apgd-t, fab-t, square
initial accuracy: 90.00%
apgd-ce - 1/1 - 445 out of 450 successfully perturbed
robust accuracy after APGD-CE: 1.00% (total time 226.9 s)
Traceback (most recent call last):
  File "/home/user/adversialml/src/src/attacks.py", line 104, in <module>
    adv_complete, max_nr = adversary.run_standard_evaluation(x_test, y_test, bs=args.batch_size)
  File "/home/user/adversialml/src/src/submodules/autoattack/autoattack/autoattack.py", line 172, in run_standard_evaluation
    adv_curr = self.apgd_targeted.perturb(x, y) #cheap=True
  File "/home/user/adversialml/src/src/submodules/autoattack/autoattack/autopgd_base.py", line 679, in perturb
    self.y_target = output.sort(dim=1)[1][:, -target_class]
IndexError: index -5 is out of bounds for dimension 1 with size 4
Load modules...

        if version == 'standard':
            self.attacks_to_run = ['apgd-ce', 'apgd-t', 'fab-t', 'square']
            if self.norm in ['Linf', 'L2']:
                self.apgd.n_restarts = 1
                self.apgd_targeted.n_target_classes = 3 # 9
            elif self.norm in ['L1']:
                self.apgd.use_largereps = True
                self.apgd_targeted.use_largereps = True
                self.apgd.n_restarts = 5
                self.apgd_targeted.n_target_classes = 3 # 5
            self.fab.n_restarts = 1
            self.apgd_targeted.n_restarts = 1
            self.fab.n_target_classes = 3 # 9
            #self.apgd_targeted.n_target_classes = 9
            self.square.n_queries = 5000

using standard version including apgd-ce, apgd-t, fab-t, square
initial accuracy: 91.60%
apgd-ce - 1/1 - 453 out of 458 successfully perturbed
robust accuracy after APGD-CE: 1.00% (total time 110.8 s)
apgd-t - 1/1 - 2 out of 5 successfully perturbed
robust accuracy after APGD-T: 0.60% (total time 121.9 s)
fab-t - 1/1 - 0 out of 3 successfully perturbed
robust accuracy after FAB-T: 0.60% (total time 140.2 s)
square - 1/1 - 0 out of 3 successfully perturbed
robust accuracy after SQUARE: 0.60% (total time 212.3 s)
max Linf perturbation: 0.01569, nan in tensor: 0, max: 1.00000, min: 0.00000
robust accuracy: 0.60%