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Fixed evaluation of models with random defenses #105

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Fixed evaluation of models with random defenses #105

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3a03281
rand version
LYM-fire Dec 18, 2023
2b6d247
Update autoattack.py
LYMDLUT Jan 19, 2024
d790e04
Update autoattack.py
LYMDLUT Jan 19, 2024
d74761b
Update autoattack.py
LYMDLUT Jan 19, 2024
7971b00
separate eval_iter from apgd_eot and choose its value by evaluation v…
Buntender Jan 19, 2024
4ce0edb
minor fix
Buntender Jan 19, 2024
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64 changes: 47 additions & 17 deletions autoattack/autoattack.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@

class AutoAttack():
def __init__(self, model, norm='Linf', eps=.3, seed=None, verbose=True,
attacks_to_run=[], version='standard', is_tf_model=False,
attacks_to_run=[], version='standard', eval_iter = None, is_tf_model=False,
device='cuda', log_path=None):
self.model = model
self.norm = norm
Expand All @@ -21,6 +21,7 @@ def __init__(self, model, norm='Linf', eps=.3, seed=None, verbose=True,
self.verbose = verbose
self.attacks_to_run = attacks_to_run
self.version = version
self.eval_iter = eval_iter
self.is_tf_model = is_tf_model
self.device = device
self.logger = Logger(log_path)
Expand Down Expand Up @@ -108,9 +109,19 @@ def run_standard_evaluation(self,
self.logger.log('{} was/were already run.'.format(', '.join(state.run_attacks)))

# checks on type of defense
is_randomized_defense = True
if self.version != 'rand':
checks.check_randomized(self.get_logits, x_orig[:bs].to(self.device),
is_randomized_defense = checks.is_randomized(self.get_logits, x_orig[:bs].to(self.device),
y_orig[:bs].to(self.device), bs=bs, logger=self.logger)

if self.eval_iter is None:
if is_randomized_defense:
self.logger.log("random defense, using default eval_iter 20")
self.eval_iter = 20
else:
self.logger.log("non-random defense, using default eval_iter 1")
self.eval_iter = 1

n_cls = checks.check_range_output(self.get_logits, x_orig[:bs].to(self.device),
logger=self.logger)
checks.check_dynamic(self.model, x_orig[:bs].to(self.device), self.is_tf_model,
Expand All @@ -122,19 +133,23 @@ def run_standard_evaluation(self,
# calculate accuracy
n_batches = int(np.ceil(x_orig.shape[0] / bs))
if state.robust_flags is None:
robust_flags = torch.zeros(x_orig.shape[0], dtype=torch.bool, device=x_orig.device)
#robust_flags = torch.zeros(x_orig.shape[0], dtype=torch.bool, device=x_orig.device)
robust_flags = torch.zeros(x_orig.shape[0], device=x_orig.device)
y_adv = torch.empty_like(y_orig)
for batch_idx in range(n_batches):
start_idx = batch_idx * bs
end_idx = min( (batch_idx + 1) * bs, x_orig.shape[0])
end_idx = min((batch_idx + 1) * bs, x_orig.shape[0])

x = x_orig[start_idx:end_idx, :].clone().to(self.device)
y = y_orig[start_idx:end_idx].clone().to(self.device)
output = self.get_logits(x).max(dim=1)[1]
y_adv[start_idx: end_idx] = output
correct_batch = y.eq(output)
robust_flags[start_idx:end_idx] = correct_batch.detach().to(robust_flags.device)

for _ in range(self.eval_iter):
output = self.get_logits(x).max(dim=1)[1]
y_adv[start_idx: end_idx] = output
correct_batch = y.eq(output)
robust_flags[start_idx:end_idx] += correct_batch.detach().to(robust_flags.device)

robust_flags /= self.eval_iter
state.robust_flags = robust_flags
robust_accuracy = torch.sum(robust_flags).item() / x_orig.shape[0]
robust_accuracy_dict = {'clean': robust_accuracy}
Expand All @@ -154,7 +169,8 @@ def run_standard_evaluation(self,
startt = time.time()
for attack in attacks_to_run:
# item() is super important as pytorch int division uses floor rounding
num_robust = torch.sum(robust_flags).item()
#num_robust = torch.sum(robust_flags).item()
num_robust = torch.sum(robust_flags != 0).item()

if num_robust == 0:
break
Expand Down Expand Up @@ -218,17 +234,31 @@ def run_standard_evaluation(self,
else:
raise ValueError('Attack not supported')

output = self.get_logits(adv_curr).max(dim=1)[1]
false_batch = ~y.eq(output).to(robust_flags.device)
non_robust_lin_idcs = batch_datapoint_idcs[false_batch]
robust_flags[non_robust_lin_idcs] = False
state.robust_flags = robust_flags
# output = self.get_logits(adv_curr).max(dim=1)[1]
# false_batch = ~y.eq(output).to(robust_flags.device)
# non_robust_lin_idcs = batch_datapoint_idcs[false_batch]
# robust_flags[non_robust_lin_idcs] = False
# state.robust_flags = robust_flags

x_adv[non_robust_lin_idcs] = adv_curr[false_batch].detach().to(x_adv.device)
y_adv[non_robust_lin_idcs] = output[false_batch].detach().to(x_adv.device)
# x_adv[non_robust_lin_idcs] = adv_curr[false_batch].detach().to(x_adv.device)
# y_adv[non_robust_lin_idcs] = output[false_batch].detach().to(x_adv.device)

correct_batch = torch.zeros_like(y)
for _ in range(self.eval_iter):
output = self.get_logits(adv_curr).max(dim=1)[1]
correct_batch += y.eq(output).to(robust_flags.device)

correct_batch = correct_batch / self.eval_iter

smaller_indices = correct_batch < robust_flags[batch_datapoint_idcs]
robust_flags[batch_datapoint_idcs[smaller_indices]] = correct_batch[smaller_indices]
x_adv[batch_datapoint_idcs[smaller_indices]] = adv_curr[smaller_indices].detach().to(x_adv.device)
y_adv[batch_datapoint_idcs[smaller_indices]] = output[smaller_indices].detach().to(x_adv.device)


if self.verbose:
num_non_robust_batch = torch.sum(false_batch)
#num_non_robust_batch = torch.sum(false_batch)
num_non_robust_batch = torch.sum(1 - correct_batch)
self.logger.log('{} - {}/{} - {} out of {} successfully perturbed'.format(
attack, batch_idx + 1, n_batches, num_non_robust_batch, x.shape[0]))

Expand Down
4 changes: 3 additions & 1 deletion autoattack/checks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@
checks_doc_path = 'flags_doc.md'


def check_randomized(model, x, y, bs=250, n=5, alpha=1e-4, logger=None):
def is_randomized(model, x, y, bs=250, n=5, alpha=1e-4, logger=None):
acc = []
corrcl = []
outputs = []
Expand All @@ -39,6 +39,8 @@ def check_randomized(model, x, y, bs=250, n=5, alpha=1e-4, logger=None):
warnings.warn(Warning(msg))
else:
logger.log(f'Warning: {msg}')
return True
return False


def check_range_output(model, x, alpha=1e-5, logger=None):
Expand Down