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lib_generation.py
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lib_generation.py
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from __future__ import print_function
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from scipy.spatial.distance import pdist, cdist, squareform
# lid of a batch of query points X
def mle_batch(data, batch, k):
'''
commpute lid score using data & batch with k-neighbors
return: a: computed LID score
'''
data = np.asarray(data, dtype=np.float32)
batch = np.asarray(batch, dtype=np.float32)
k = min(k, len(data)-1)
f = lambda v: - k / np.sum(np.log(v/v[-1]))
a = cdist(batch, data)
a = np.apply_along_axis(np.sort, axis=1, arr=a)[:,1:k+1]
a = np.apply_along_axis(f, axis=1, arr=a)
return a
# this function is from https://github.com/xingjunm/lid_adversarial_subspace_detection
def merge_and_generate_labels(X_pos, X_neg):
"""
merge positve and nagative artifact and generate labels
return: X: merged samples, 2D ndarray
y: generated labels (0/1): 2D ndarray same size as X
"""
X_pos = np.asarray(X_pos, dtype=np.float32)
X_pos = X_pos.reshape((X_pos.shape[0], -1))
X_neg = np.asarray(X_neg, dtype=np.float32)
X_neg = X_neg.reshape((X_neg.shape[0], -1))
X = np.concatenate((X_pos, X_neg))
y = np.concatenate((np.ones(X_pos.shape[0]), np.zeros(X_neg.shape[0])))
y = y.reshape((X.shape[0], 1))
return X, y
def sample_estimator(model, num_classes, feature_list, train_loader):
"""
compute sample mean and precision (inverse of covariance)
return: sample_class_mean: list of class mean
precision: list of precisions
"""
import sklearn.covariance
model.eval()
group_lasso = sklearn.covariance.EmpiricalCovariance(assume_centered=False)
correct, total = 0, 0
num_output = len(feature_list)
num_sample_per_class = np.empty(num_classes)
num_sample_per_class.fill(0)
list_features = []
for i in range(num_output):
temp_list = []
for j in range(num_classes):
temp_list.append(0)
list_features.append(temp_list)
for data, target in train_loader:
total += data.size(0)
data = data.cuda()
data = Variable(data, volatile=True)
output, out_features = model.feature_list(data)
# get hidden features
for i in range(num_output):
out_features[i] = out_features[i].view(out_features[i].size(0), out_features[i].size(1), -1)
out_features[i] = torch.mean(out_features[i].data, 2)
# compute the accuracy
pred = output.data.max(1)[1]
equal_flag = pred.eq(target.cuda()).cpu()
correct += equal_flag.sum()
# construct the sample matrix
for i in range(data.size(0)):
label = target[i]
if num_sample_per_class[label] == 0:
out_count = 0
for out in out_features:
list_features[out_count][label] = out[i].view(1, -1)
out_count += 1
else:
out_count = 0
for out in out_features:
list_features[out_count][label] \
= torch.cat((list_features[out_count][label], out[i].view(1, -1)), 0)
out_count += 1
num_sample_per_class[label] += 1
sample_class_mean = []
out_count = 0
for num_feature in feature_list:
temp_list = torch.Tensor(num_classes, int(num_feature)).cuda()
for j in range(num_classes):
temp_list[j] = torch.mean(list_features[out_count][j], 0)
sample_class_mean.append(temp_list)
out_count += 1
precision = []
for k in range(num_output):
X = 0
for i in range(num_classes):
if i == 0:
X = list_features[k][i] - sample_class_mean[k][i]
else:
X = torch.cat((X, list_features[k][i] - sample_class_mean[k][i]), 0)
# find inverse
group_lasso.fit(X.cpu().numpy())
temp_precision = group_lasso.precision_
temp_precision = torch.from_numpy(temp_precision).float().cuda()
precision.append(temp_precision)
print('\n Training Accuracy:({:.2f}%)\n'.format(100. * correct / total))
return sample_class_mean, precision
def get_Mahalanobis_score(model, test_loader, num_classes, outf, out_flag, net_type, sample_mean, precision, layer_index, magnitude):
'''
Compute the proposed Mahalanobis confidence score on input dataset
return: Mahalanobis score from layer_index
'''
model.eval()
Mahalanobis = []
if out_flag == True:
temp_file_name = '%s/confidence_Ga%s_In.txt'%(outf, str(layer_index))
else:
temp_file_name = '%s/confidence_Ga%s_Out.txt'%(outf, str(layer_index))
g = open(temp_file_name, 'w')
for data, target in test_loader:
data, target = data.cuda(), target.cuda()
data, target = Variable(data, requires_grad = True), Variable(target)
out_features = model.intermediate_forward(data, layer_index)
out_features = out_features.view(out_features.size(0), out_features.size(1), -1)
out_features = torch.mean(out_features, 2)
# compute Mahalanobis score
gaussian_score = 0
for i in range(num_classes):
batch_sample_mean = sample_mean[layer_index][i]
zero_f = out_features.data - batch_sample_mean
term_gau = -0.5*torch.mm(torch.mm(zero_f, precision[layer_index]), zero_f.t()).diag()
if i == 0:
gaussian_score = term_gau.view(-1,1)
else:
gaussian_score = torch.cat((gaussian_score, term_gau.view(-1,1)), 1)
# Input_processing
sample_pred = gaussian_score.max(1)[1]
batch_sample_mean = sample_mean[layer_index].index_select(0, sample_pred)
zero_f = out_features - Variable(batch_sample_mean)
pure_gau = -0.5*torch.mm(torch.mm(zero_f, Variable(precision[layer_index])), zero_f.t()).diag()
loss = torch.mean(-pure_gau)
loss.backward()
gradient = torch.ge(data.grad.data, 0)
gradient = (gradient.float() - 0.5) * 2
if net_type == 'densenet':
gradient.index_copy_(1, torch.LongTensor([0]).cuda(), gradient.index_select(1, torch.LongTensor([0]).cuda()) / (63.0/255.0))
gradient.index_copy_(1, torch.LongTensor([1]).cuda(), gradient.index_select(1, torch.LongTensor([1]).cuda()) / (62.1/255.0))
gradient.index_copy_(1, torch.LongTensor([2]).cuda(), gradient.index_select(1, torch.LongTensor([2]).cuda()) / (66.7/255.0))
elif net_type == 'resnet':
gradient.index_copy_(1, torch.LongTensor([0]).cuda(), gradient.index_select(1, torch.LongTensor([0]).cuda()) / (0.2023))
gradient.index_copy_(1, torch.LongTensor([1]).cuda(), gradient.index_select(1, torch.LongTensor([1]).cuda()) / (0.1994))
gradient.index_copy_(1, torch.LongTensor([2]).cuda(), gradient.index_select(1, torch.LongTensor([2]).cuda()) / (0.2010))
tempInputs = torch.add(data.data, -magnitude, gradient)
noise_out_features = model.intermediate_forward(Variable(tempInputs, volatile=True), layer_index)
noise_out_features = noise_out_features.view(noise_out_features.size(0), noise_out_features.size(1), -1)
noise_out_features = torch.mean(noise_out_features, 2)
noise_gaussian_score = 0
for i in range(num_classes):
batch_sample_mean = sample_mean[layer_index][i]
zero_f = noise_out_features.data - batch_sample_mean
term_gau = -0.5*torch.mm(torch.mm(zero_f, precision[layer_index]), zero_f.t()).diag()
if i == 0:
noise_gaussian_score = term_gau.view(-1,1)
else:
noise_gaussian_score = torch.cat((noise_gaussian_score, term_gau.view(-1,1)), 1)
noise_gaussian_score, _ = torch.max(noise_gaussian_score, dim=1)
Mahalanobis.extend(noise_gaussian_score.cpu().numpy())
for i in range(data.size(0)):
g.write("{}\n".format(noise_gaussian_score[i]))
g.close()
return Mahalanobis
def get_posterior(model, net_type, test_loader, magnitude, temperature, outf, out_flag):
'''
Compute the maximum value of (processed) posterior distribution - ODIN
return: null
'''
criterion = nn.CrossEntropyLoss()
model.eval()
total = 0
if out_flag == True:
temp_file_name_val = '%s/confidence_PoV_In.txt'%(outf)
temp_file_name_test = '%s/confidence_PoT_In.txt'%(outf)
else:
temp_file_name_val = '%s/confidence_PoV_Out.txt'%(outf)
temp_file_name_test = '%s/confidence_PoT_Out.txt'%(outf)
g = open(temp_file_name_val, 'w')
f = open(temp_file_name_test, 'w')
for data, _ in test_loader:
total += data.size(0)
data = data.cuda()
data = Variable(data, requires_grad = True)
batch_output = model(data)
# temperature scaling
outputs = batch_output / temperature
labels = outputs.data.max(1)[1]
labels = Variable(labels)
loss = criterion(outputs, labels)
loss.backward()
# Normalizing the gradient to binary in {0, 1}
gradient = torch.ge(data.grad.data, 0)
gradient = (gradient.float() - 0.5) * 2
if net_type == 'densenet':
gradient.index_copy_(1, torch.LongTensor([0]).cuda(), gradient.index_select(1, torch.LongTensor([0]).cuda()) / (63.0/255.0))
gradient.index_copy_(1, torch.LongTensor([1]).cuda(), gradient.index_select(1, torch.LongTensor([1]).cuda()) / (62.1/255.0))
gradient.index_copy_(1, torch.LongTensor([2]).cuda(), gradient.index_select(1, torch.LongTensor([2]).cuda()) / (66.7/255.0))
elif net_type == 'resnet':
gradient.index_copy_(1, torch.LongTensor([0]).cuda(), gradient.index_select(1, torch.LongTensor([0]).cuda()) / (0.2023))
gradient.index_copy_(1, torch.LongTensor([1]).cuda(), gradient.index_select(1, torch.LongTensor([1]).cuda()) / (0.1994))
gradient.index_copy_(1, torch.LongTensor([2]).cuda(), gradient.index_select(1, torch.LongTensor([2]).cuda()) / (0.2010))
tempInputs = torch.add(data.data, -magnitude, gradient)
outputs = model(Variable(tempInputs, volatile=True))
outputs = outputs / temperature
soft_out = F.softmax(outputs, dim=1)
soft_out, _ = torch.max(soft_out.data, dim=1)
for i in range(data.size(0)):
if total <= 1000:
g.write("{}\n".format(soft_out[i]))
else:
f.write("{}\n".format(soft_out[i]))
f.close()
g.close()
def get_Mahalanobis_score_adv(model, test_data, test_label, num_classes, outf, net_type, sample_mean, precision, layer_index, magnitude):
'''
Compute the proposed Mahalanobis confidence score on adversarial samples
return: Mahalanobis score from layer_index
'''
model.eval()
Mahalanobis = []
batch_size = 100
total = 0
for data_index in range(int(np.floor(test_data.size(0)/batch_size))):
target = test_label[total : total + batch_size].cuda()
data = test_data[total : total + batch_size].cuda()
total += batch_size
data, target = Variable(data, requires_grad = True), Variable(target)
out_features = model.intermediate_forward(data, layer_index)
out_features = out_features.view(out_features.size(0), out_features.size(1), -1)
out_features = torch.mean(out_features, 2)
gaussian_score = 0
for i in range(num_classes):
batch_sample_mean = sample_mean[layer_index][i]
zero_f = out_features.data - batch_sample_mean
term_gau = -0.5*torch.mm(torch.mm(zero_f, precision[layer_index]), zero_f.t()).diag()
if i == 0:
gaussian_score = term_gau.view(-1,1)
else:
gaussian_score = torch.cat((gaussian_score, term_gau.view(-1,1)), 1)
# Input_processing
sample_pred = gaussian_score.max(1)[1]
batch_sample_mean = sample_mean[layer_index].index_select(0, sample_pred)
zero_f = out_features - Variable(batch_sample_mean)
pure_gau = -0.5*torch.mm(torch.mm(zero_f, Variable(precision[layer_index])), zero_f.t()).diag()
loss = torch.mean(-pure_gau)
loss.backward()
gradient = torch.ge(data.grad.data, 0)
gradient = (gradient.float() - 0.5) * 2
if net_type == 'densenet':
gradient.index_copy_(1, torch.LongTensor([0]).cuda(), gradient.index_select(1, torch.LongTensor([0]).cuda()) / (63.0/255.0))
gradient.index_copy_(1, torch.LongTensor([1]).cuda(), gradient.index_select(1, torch.LongTensor([1]).cuda()) / (62.1/255.0))
gradient.index_copy_(1, torch.LongTensor([2]).cuda(), gradient.index_select(1, torch.LongTensor([2]).cuda()) / (66.7/255.0))
elif net_type == 'resnet':
gradient.index_copy_(1, torch.LongTensor([0]).cuda(), gradient.index_select(1, torch.LongTensor([0]).cuda()) / (0.2023))
gradient.index_copy_(1, torch.LongTensor([1]).cuda(), gradient.index_select(1, torch.LongTensor([1]).cuda()) / (0.1994))
gradient.index_copy_(1, torch.LongTensor([2]).cuda(), gradient.index_select(1, torch.LongTensor([2]).cuda()) / (0.2010))
tempInputs = torch.add(data.data, -magnitude, gradient)
noise_out_features = model.intermediate_forward(Variable(tempInputs, volatile=True), layer_index)
noise_out_features = noise_out_features.view(noise_out_features.size(0), noise_out_features.size(1), -1)
noise_out_features = torch.mean(noise_out_features, 2)
noise_gaussian_score = 0
for i in range(num_classes):
batch_sample_mean = sample_mean[layer_index][i]
zero_f = noise_out_features.data - batch_sample_mean
term_gau = -0.5*torch.mm(torch.mm(zero_f, precision[layer_index]), zero_f.t()).diag()
if i == 0:
noise_gaussian_score = term_gau.view(-1,1)
else:
noise_gaussian_score = torch.cat((noise_gaussian_score, term_gau.view(-1,1)), 1)
noise_gaussian_score, _ = torch.max(noise_gaussian_score, dim=1)
Mahalanobis.extend(noise_gaussian_score.cpu().numpy())
return Mahalanobis
def get_LID(model, test_clean_data, test_adv_data, test_noisy_data, test_label, num_output):
'''
Compute LID score on adversarial samples
return: LID score
'''
model.eval()
total = 0
batch_size = 100
LID, LID_adv, LID_noisy = [], [], []
overlap_list = [10, 20, 30, 40, 50, 60, 70, 80, 90]
for i in overlap_list:
LID.append([])
LID_adv.append([])
LID_noisy.append([])
for data_index in range(int(np.floor(test_clean_data.size(0)/batch_size))):
data = test_clean_data[total : total + batch_size].cuda()
adv_data = test_adv_data[total : total + batch_size].cuda()
noisy_data = test_noisy_data[total : total + batch_size].cuda()
target = test_label[total : total + batch_size].cuda()
total += batch_size
data, target = Variable(data, volatile=True), Variable(target)
output, out_features = model.feature_list(data)
X_act = []
for i in range(num_output):
out_features[i] = out_features[i].view(out_features[i].size(0), out_features[i].size(1), -1)
out_features[i] = torch.mean(out_features[i].data, 2)
X_act.append(np.asarray(out_features[i], dtype=np.float32).reshape((out_features[i].size(0), -1)))
output, out_features = model.feature_list(Variable(adv_data, volatile=True))
X_act_adv = []
for i in range(num_output):
out_features[i] = out_features[i].view(out_features[i].size(0), out_features[i].size(1), -1)
out_features[i] = torch.mean(out_features[i].data, 2)
X_act_adv.append(np.asarray(out_features[i], dtype=np.float32).reshape((out_features[i].size(0), -1)))
output, out_features = model.feature_list(Variable(noisy_data, volatile=True))
X_act_noisy = []
for i in range(num_output):
out_features[i] = out_features[i].view(out_features[i].size(0), out_features[i].size(1), -1)
out_features[i] = torch.mean(out_features[i].data, 2)
X_act_noisy.append(np.asarray(out_features[i], dtype=np.float32).reshape((out_features[i].size(0), -1)))
# LID
list_counter = 0
for overlap in overlap_list:
LID_list = []
LID_adv_list = []
LID_noisy_list = []
for j in range(num_output):
lid_score = mle_batch(X_act[j], X_act[j], k = overlap)
lid_score = lid_score.reshape((lid_score.shape[0], -1))
lid_adv_score = mle_batch(X_act[j], X_act_adv[j], k = overlap)
lid_adv_score = lid_adv_score.reshape((lid_adv_score.shape[0], -1))
lid_noisy_score = mle_batch(X_act[j], X_act_noisy[j], k = overlap)
lid_noisy_score = lid_noisy_score.reshape((lid_noisy_score.shape[0], -1))
LID_list.append(lid_score)
LID_adv_list.append(lid_adv_score)
LID_noisy_list.append(lid_noisy_score)
LID_concat = LID_list[0]
LID_adv_concat = LID_adv_list[0]
LID_noisy_concat = LID_noisy_list[0]
for i in range(1, num_output):
LID_concat = np.concatenate((LID_concat, LID_list[i]), axis=1)
LID_adv_concat = np.concatenate((LID_adv_concat, LID_adv_list[i]), axis=1)
LID_noisy_concat = np.concatenate((LID_noisy_concat, LID_noisy_list[i]), axis=1)
LID[list_counter].extend(LID_concat)
LID_adv[list_counter].extend(LID_adv_concat)
LID_noisy[list_counter].extend(LID_noisy_concat)
list_counter += 1
return LID, LID_adv, LID_noisy