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ADV_Regression.py
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ADV_Regression.py
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"""
Created on Sun Oct 25 2018
@author: Kimin Lee
"""
from __future__ import print_function
import numpy as np
import os
import lib_regression
import argparse
from sklearn.linear_model import LogisticRegressionCV
parser = argparse.ArgumentParser(description='PyTorch code: Mahalanobis detector')
parser.add_argument('--net_type', required=True, help='resnet | densenet')
args = parser.parse_args()
print(args)
def main():
# initial setup
dataset_list = ['cifar10', 'cifar100', 'svhn']
adv_test_list = ['FGSM', 'BIM', 'DeepFool', 'CWL2']
print('evaluate the LID estimator')
score_list = ['LID_10', 'LID_20', 'LID_30', 'LID_40', 'LID_50', 'LID_60', 'LID_70', 'LID_80', 'LID_90']
list_best_results, list_best_results_index = [], []
for dataset in dataset_list:
print('load train data: ', dataset)
outf = './adv_output/' + args.net_type + '_' + dataset + '/'
list_best_results_out, list_best_results_index_out = [], []
for out in adv_test_list:
best_auroc, best_result, best_index = 0, 0, 0
for score in score_list:
print('load train data: ', out, ' of ', score)
total_X, total_Y = lib_regression.load_characteristics(score, dataset, out, outf)
X_val, Y_val, X_test, Y_test = lib_regression.block_split_adv(total_X, total_Y)
pivot = int(X_val.shape[0] / 6)
X_train = np.concatenate((X_val[:pivot], X_val[2*pivot:3*pivot], X_val[4*pivot:5*pivot]))
Y_train = np.concatenate((Y_val[:pivot], Y_val[2*pivot:3*pivot], Y_val[4*pivot:5*pivot]))
X_val_for_test = np.concatenate((X_val[pivot:2*pivot], X_val[3*pivot:4*pivot], X_val[5*pivot:]))
Y_val_for_test = np.concatenate((Y_val[pivot:2*pivot], Y_val[3*pivot:4*pivot], Y_val[5*pivot:]))
lr = LogisticRegressionCV(n_jobs=-1).fit(X_train, Y_train)
y_pred = lr.predict_proba(X_train)[:, 1]
#print('training mse: {:.4f}'.format(np.mean(y_pred - Y_train)))
y_pred = lr.predict_proba(X_val_for_test)[:, 1]
#print('test mse: {:.4f}'.format(np.mean(y_pred - Y_val_for_test)))
results = lib_regression.detection_performance(lr, X_val_for_test, Y_val_for_test, outf)
if best_auroc < results['TMP']['AUROC']:
best_auroc = results['TMP']['AUROC']
best_index = score
best_result = lib_regression.detection_performance(lr, X_test, Y_test, outf)
list_best_results_out.append(best_result)
list_best_results_index_out.append(best_index)
list_best_results.append(list_best_results_out)
list_best_results_index.append(list_best_results_index_out)
print('evaluate the Mahalanobis estimator')
score_list = ['Mahalanobis_0.0', 'Mahalanobis_0.01', 'Mahalanobis_0.005', \
'Mahalanobis_0.002', 'Mahalanobis_0.0014', 'Mahalanobis_0.001', 'Mahalanobis_0.0005']
list_best_results_ours, list_best_results_index_ours = [], []
for dataset in dataset_list:
print('load train data: ', dataset)
outf = './adv_output/' + args.net_type + '_' + dataset + '/'
list_best_results_out, list_best_results_index_out = [], []
for out in adv_test_list:
best_auroc, best_result, best_index = 0, 0, 0
for score in score_list:
print('load train data: ', out, ' of ', score)
total_X, total_Y = lib_regression.load_characteristics(score, dataset, out, outf)
X_val, Y_val, X_test, Y_test = lib_regression.block_split_adv(total_X, total_Y)
pivot = int(X_val.shape[0] / 6)
X_train = np.concatenate((X_val[:pivot], X_val[2*pivot:3*pivot], X_val[4*pivot:5*pivot]))
Y_train = np.concatenate((Y_val[:pivot], Y_val[2*pivot:3*pivot], Y_val[4*pivot:5*pivot]))
X_val_for_test = np.concatenate((X_val[pivot:2*pivot], X_val[3*pivot:4*pivot], X_val[5*pivot:]))
Y_val_for_test = np.concatenate((Y_val[pivot:2*pivot], Y_val[3*pivot:4*pivot], Y_val[5*pivot:]))
lr = LogisticRegressionCV(n_jobs=-1).fit(X_train, Y_train)
y_pred = lr.predict_proba(X_train)[:, 1]
#print('training mse: {:.4f}'.format(np.mean(y_pred - Y_train)))
y_pred = lr.predict_proba(X_val_for_test)[:, 1]
#print('test mse: {:.4f}'.format(np.mean(y_pred - Y_val_for_test)))
results = lib_regression.detection_performance(lr, X_val_for_test, Y_val_for_test, outf)
if best_auroc < results['TMP']['AUROC']:
best_auroc = results['TMP']['AUROC']
best_index = score
best_result = lib_regression.detection_performance(lr, X_test, Y_test, outf)
list_best_results_out.append(best_result)
list_best_results_index_out.append(best_index)
list_best_results_ours.append(list_best_results_out)
list_best_results_index_ours.append(list_best_results_index_out)
count_in = 0
mtypes = ['TNR', 'AUROC', 'DTACC', 'AUIN', 'AUOUT']
print("results of LID")
for in_list in list_best_results:
print('in_distribution: ' + dataset_list[count_in] + '==========')
count_out = 0
for results in in_list:
print('out_distribution: '+ adv_test_list[count_out])
for mtype in mtypes:
print(' {mtype:6s}'.format(mtype=mtype), end='')
print('\n{val:6.2f}'.format(val=100.*results['TMP']['TNR']), end='')
print(' {val:6.2f}'.format(val=100.*results['TMP']['AUROC']), end='')
print(' {val:6.2f}'.format(val=100.*results['TMP']['DTACC']), end='')
print(' {val:6.2f}'.format(val=100.*results['TMP']['AUIN']), end='')
print(' {val:6.2f}\n'.format(val=100.*results['TMP']['AUOUT']), end='')
print('Input noise: ' + list_best_results_index[count_in][count_out])
print('')
count_out += 1
count_in += 1
count_in = 0
print("results of Mahalanobis")
for in_list in list_best_results_ours:
print('in_distribution: ' + dataset_list[count_in] + '==========')
count_out = 0
for results in in_list:
print('out_distribution: '+ adv_test_list[count_out])
for mtype in mtypes:
print(' {mtype:6s}'.format(mtype=mtype), end='')
print('\n{val:6.2f}'.format(val=100.*results['TMP']['TNR']), end='')
print(' {val:6.2f}'.format(val=100.*results['TMP']['AUROC']), end='')
print(' {val:6.2f}'.format(val=100.*results['TMP']['DTACC']), end='')
print(' {val:6.2f}'.format(val=100.*results['TMP']['AUIN']), end='')
print(' {val:6.2f}\n'.format(val=100.*results['TMP']['AUOUT']), end='')
print('Input noise: ' + list_best_results_index_ours[count_in][count_out])
print('')
count_out += 1
count_in += 1
if __name__ == '__main__':
main()