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evaluation_tinyimagenet.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 21 17:19:50 2019
@author: eric
"""
# EXTERNAL LIBRARY IMPORTS
import prebuilt_loss_functions as plf
import adversarial_training as advtrain
import adversarial_evaluation as adveval
import adversarial_perturbations as ap
import adversarial_attacks as aa
import loss_functions as lf
import argparse
import re
import torch
import os
import torch.nn as nn
from adv_defence.models import Classifier
import torchvision.datasets as datasets
import torch.utils.data as data
import torchvision.transforms as transforms
import utils.pytorch_utils as utils
transform_list = [transforms.ToTensor()]
transform_chain = transforms.Compose(transform_list)
image_dataset = datasets.ImageFolder('./datasets/tinyimagenet/val', transform_chain)
test_dataloader = data.DataLoader(image_dataset, batch_size=100, shuffle=False, num_workers=2)
def main(config):
model = Classifier(200, classifier_name='resnet18', dataset="tinyimagenet", pretrained=False)
# format matching
data_classifier_state = torch.load(os.path.join(config.path, 'Classifier.pth'), map_location=None)
if 'state_dict' in data_classifier_state:
data_classifier_state = data_classifier_state['state_dict']
bad_classifier_state = {}
for k, v in data_classifier_state.items():
if k.startswith('1.'):
bad_classifier_state[k[2:]] = v
else:
bad_classifier_state[k] = v
starts_with_module = False
for key in bad_classifier_state.keys():
if key.startswith('module.'):
starts_with_module = True
break
if starts_with_module:
correct_classifier_state = {k[7:]: v for k, v in
bad_classifier_state.items()}
else:
correct_classifier_state = bad_classifier_state
starts_with_feature_extractor = False
for k in correct_classifier_state.keys():
if k.startswith('feature_extractor.'):
starts_with_feature_extractor = True
break
if not starts_with_feature_extractor:
correct_classifier_state = {'feature_extractor.'+k: v for k, v in
correct_classifier_state.items()}
# fit into our model
model.load_state_dict( correct_classifier_state )
normalizer = utils.IdentityNormalize()
# Put this into the AdversarialEvaluation object
adv_eval_object = adveval.AdversarialEvaluation(model, normalizer)
surrogate = model
normalizer_surr = normalizer
# First let's build the attack parameters for each.
# we'll reuse the loss function:
attack_loss = plf.VanillaXentropy(surrogate, normalizer_surr)
linf_8_threat = ap.ThreatModel(ap.DeltaAddition, {'lp_style': 'inf',
'lp_bound': 8.0 / 255.0})
#------ FGSM Block
fgsm_attack = aa.FGSM(surrogate, normalizer_surr, linf_8_threat, attack_loss)
fgsm_attack_kwargs = {'step_size': 8.0 / 255.0,
'verbose': False}
fgsm_attack_params = advtrain.AdversarialAttackParameters(fgsm_attack,
attack_specific_params=
{'attack_kwargs': fgsm_attack_kwargs})
# ------ pgd10 Block
pgd10_attack = aa.PGD(surrogate, normalizer_surr, linf_8_threat, attack_loss)
pgd10_attack_kwargs = {'step_size': 8.0/255.0/4.0,
'num_iterations': 10,
'keep_best': True,
'random_init': True,
'verbose': False}
pgd10_attack_params = advtrain.AdversarialAttackParameters(pgd10_attack,
attack_specific_params=
{'attack_kwargs': pgd10_attack_kwargs})
# ------ pgd100 Block
pgd100_attack = aa.PGD(surrogate, normalizer_surr, linf_8_threat, attack_loss)
pgd100_attack_kwargs = {'step_size': 8.0/255.0/12.0,
'num_iterations': 100,
'keep_best': True,
'random_init': True,
'verbose': False}
pgd100_attack_params = advtrain.AdversarialAttackParameters(pgd100_attack,
attack_specific_params=
{'attack_kwargs': pgd100_attack_kwargs})
# ------ CarliniWagner100 Block
cwloss6 = lf.CWLossF6
distance_fxn = lf.SoftLInfRegularization
cw100_attack = aa.CarliniWagner(surrogate, normalizer_surr, linf_8_threat, distance_fxn, cwloss6)
cw100_attack_kwargs = {'num_optim_steps': 100,
'verbose': False}
cw100_attack_params = advtrain.AdversarialAttackParameters(cw100_attack,
attack_specific_params=
{'attack_kwargs': cw100_attack_kwargs})
# ------ CarliniWagner1000 Block
cwloss6 = lf.CWLossF6
distance_fxn = lf.SoftLInfRegularization
cw1000_attack = aa.CarliniWagner(surrogate, normalizer_surr, linf_8_threat, distance_fxn, cwloss6)
cw1000_attack_kwargs = {'num_optim_steps': 1000,
'verbose': False}
cw1000_attack_params = advtrain.AdversarialAttackParameters(cw1000_attack,
attack_specific_params=
{'attack_kwargs': cw1000_attack_kwargs})
to_eval_dict = {'top1': 'top1',
'avg_loss_value': 'avg_loss_value',
'avg_successful_ssim': 'avg_successful_ssim'}
fgsm_eval = adveval.EvaluationResult(fgsm_attack_params,
to_eval=to_eval_dict)
pgd10_eval = adveval.EvaluationResult(pgd10_attack_params,
to_eval=to_eval_dict)
pgd100_eval = adveval.EvaluationResult(pgd100_attack_params,
to_eval=to_eval_dict)
cw100_eval = adveval.EvaluationResult(cw100_attack_params,
to_eval=to_eval_dict)
cw1000_eval = adveval.EvaluationResult(cw1000_attack_params,
to_eval=to_eval_dict)
attack_ensemble = {'fgsm': fgsm_eval,
'pgd10' : pgd10_eval,
'pgd100' : pgd100_eval,
'cw100' : cw100_eval,
'cw1000' : cw1000_eval}
ensemble_out = adv_eval_object.evaluate_ensemble(test_dataloader, attack_ensemble,
verbose=True,
num_minibatches=None)
sort_order = {'ground': 1, 'fgsm': 2, 'pgd10': 3, 'pgd100': 4, 'cw100': 5, 'cw1000': 6}
# sort_order = {'ground': 1, 'pgd10': 2, 'pgd100': 3}
def pretty_printer(fd, eval_ensemble, result_type):
print('~' * 10, result_type, '~' * 10)
fd.write('~' * 10+ result_type+ '~' * 10+"\n")
for key in sorted(list(eval_ensemble.keys()), key=lambda k: sort_order[k]):
eval_result = eval_ensemble[key]
pad = 6 - len(key)
if result_type not in eval_result.results:
continue
avg_result = eval_result.results[result_type].avg
print(key, pad* ' ', ': ', avg_result)
fd.write(key + pad* ' '+ ': '+ str(avg_result)+"\n")
with open(os.path.join(config.path, 'base_eval_result.txt'), "w") as fd:
fd.write('Result for {}'.format(config.path)+"\n")
fd.write("\n")
pretty_printer(fd, ensemble_out, 'top1')
# We can examine the loss (noting that we seek to 'maximize' loss in the adversarial example domain)
pretty_printer(fd, ensemble_out, 'avg_loss_value')
# This is actually 1-SSIM, which can serve as a makeshift 'similarity index',
# which essentially gives a meterstick for how similar the perturbed images are to the originals
pretty_printer(fd, ensemble_out, 'avg_successful_ssim')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--path', type=str, default='tinynet_resnet18_ours_x_x',
help='classifier path')
parser.add_argument('--architecture', type=str, default='resnet18',
help='architecture of the model')
config, _ = parser.parse_known_args()
main(config)