def main():
# set the path to pre-trained model and output
pre_trained_net = './pre_trained/' + args.net_type + '_' + args.dataset + '.pth'
args.outf = args.outf + args.net_type + '_' + args.dataset + '/'
if os.path.isdir(args.outf) == False:
os.mkdir(args.outf)
torch.cuda.manual_seed(0)
torch.cuda.set_device(args.gpu)
# check the in-distribution dataset
if args.dataset == 'cifar100':
args.num_classes = 100
# load networks
if args.net_type == 'densenet':
if args.dataset == 'svhn':
model = models.DenseNet3(100, int(args.num_classes))
model.load_state_dict(
torch.load(pre_trained_net,
map_location="cuda:" + str(args.gpu)))
else:
model = torch.load(pre_trained_net,
map_location="cuda:" + str(args.gpu))
for i, (name, module) in enumerate(model._modules.items()):
module = recursion_change_bn(model)
for m in model.modules():
if 'Conv' in str(type(m)):
setattr(m, 'padding_mode', 'zeros')
in_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((125.3 / 255, 123.0 / 255, 113.9 / 255),
(63.0 / 255, 62.1 / 255.0, 66.7 / 255.0)),
])
elif args.net_type == 'resnet':
model = models.ResNet34(num_c=args.num_classes)
model.load_state_dict(
torch.load(pre_trained_net, map_location="cuda:" + str(args.gpu)))
in_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
model.cuda()
print('load model: ' + args.net_type)
# load dataset
print('load target data: ', args.dataset)
train_loader, _ = data_loader.getTargetDataSet(args.dataset,
args.batch_size,
in_transform, args.dataroot)
test_clean_data = torch.load(args.outf + 'clean_data_%s_%s_%s.pth' %
(args.net_type, args.dataset, 'FGSM'))
test_adv_data = torch.load(args.outf + 'adv_data_%s_%s_%s.pth' %
(args.net_type, args.dataset, 'FGSM'))
test_noisy_data = torch.load(args.outf + 'noisy_data_%s_%s_%s.pth' %
(args.net_type, args.dataset, 'FGSM'))
test_label = torch.load(args.outf + 'label_%s_%s_%s.pth' %
(args.net_type, args.dataset, 'FGSM'))
# set information about feature extaction
model.eval()
temp_x = torch.rand(2, 3, 32, 32).cuda()
temp_x = Variable(temp_x)
temp_list = model.feature_list(temp_x)[1]
num_output = len(temp_list)
feature_list = np.empty(num_output)
count = 0
for out in temp_list:
feature_list[count] = out.size(1)
count += 1
print('get sample mean and covariance')
sample_mean, precision = lib_generation.sample_estimator(
model, args.num_classes, feature_list, train_loader)
print('get Mahalanobis scores')
m_list = [0.0, 0.01, 0.005, 0.002, 0.0014, 0.001, 0.0005]
for magnitude in m_list:
print('\nNoise: ' + str(magnitude))
for i in range(num_output):
M_in \
= lib_generation.get_Mahalanobis_score_adv(model, test_clean_data, test_label, \
args.num_classes, args.outf, args.net_type, \
sample_mean, precision, i, magnitude)
M_in = np.asarray(M_in, dtype=np.float32)
if i == 0:
Mahalanobis_in = M_in.reshape((M_in.shape[0], -1))
else:
Mahalanobis_in = np.concatenate(
(Mahalanobis_in, M_in.reshape((M_in.shape[0], -1))),
axis=1)
for i in range(num_output):
M_out \
= lib_generation.get_Mahalanobis_score_adv(model, test_adv_data, test_label, \
args.num_classes, args.outf, args.net_type, \
sample_mean, precision, i, magnitude)
M_out = np.asarray(M_out, dtype=np.float32)
if i == 0:
Mahalanobis_out = M_out.reshape((M_out.shape[0], -1))
else:
Mahalanobis_out = np.concatenate(
(Mahalanobis_out, M_out.reshape((M_out.shape[0], -1))),
axis=1)
for i in range(num_output):
M_noisy \
= lib_generation.get_Mahalanobis_score_adv(model, test_noisy_data, test_label, \
args.num_classes, args.outf, args.net_type, \
sample_mean, precision, i, magnitude)
M_noisy = np.asarray(M_noisy, dtype=np.float32)
if i == 0:
Mahalanobis_noisy = M_noisy.reshape((M_noisy.shape[0], -1))
else:
Mahalanobis_noisy = np.concatenate(
(Mahalanobis_noisy, M_noisy.reshape(
(M_noisy.shape[0], -1))),
axis=1)
Mahalanobis_in = np.asarray(Mahalanobis_in, dtype=np.float32)
Mahalanobis_out = np.asarray(Mahalanobis_out, dtype=np.float32)
Mahalanobis_noisy = np.asarray(Mahalanobis_noisy, dtype=np.float32)
Mahalanobis_pos = np.concatenate((Mahalanobis_in, Mahalanobis_noisy))
Mahalanobis_data, Mahalanobis_labels = lib_generation.merge_and_generate_labels(
Mahalanobis_out, Mahalanobis_pos)
file_name = os.path.join(
args.outf, 'Mahalanobis_%s_%s_%s.npy' %
(str(magnitude), args.dataset, 'FGSM'))
Mahalanobis_data = np.concatenate(
(Mahalanobis_data, Mahalanobis_labels), axis=1)
np.save(file_name, Mahalanobis_data)
def main():
# set the path to pre-trained model and output
pre_trained_net = './pre_trained/' + args.net_type + '_' + args.dataset + '.pth'
args.outf = args.outf + args.net_type + '_' + args.dataset + '/'
if os.path.isdir(args.outf) == False:
os.mkdir(args.outf)
torch.cuda.manual_seed(0)
torch.cuda.set_device(args.gpu)
# check the in-distribution dataset
if args.dataset == 'cifar100':
args.num_classes = 100
if args.dataset == 'svhn':
out_dist_list = ['cifar10', 'imagenet_resize', 'lsun_resize']
else:
out_dist_list = ['svhn', 'imagenet_resize', 'lsun_resize']
# load networks
if args.net_type == 'densenet':
if args.dataset == 'svhn':
model = models.DenseNet3(100, int(args.num_classes))
model.load_state_dict(
torch.load(pre_trained_net,
map_location="cuda:" + str(args.gpu)))
else:
model = torch.load(pre_trained_net,
map_location="cuda:" + str(args.gpu))
in_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((125.3 / 255, 123.0 / 255, 113.9 / 255),
(63.0 / 255, 62.1 / 255.0, 66.7 / 255.0)),
])
elif args.net_type == 'resnet':
model = models.ResNet34(num_c=args.num_classes)
model.load_state_dict(
torch.load(pre_trained_net, map_location="cuda:" + str(args.gpu)))
in_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
model.cuda()
print('load model: ' + args.net_type)
# load dataset
print('load target data: ', args.dataset)
train_loader, test_loader = data_loader.getTargetDataSet(
args.dataset, args.batch_size, in_transform, args.dataroot)
# set information about feature extaction
model.eval()
temp_x = torch.rand(2, 3, 32, 32).cuda()
temp_x = Variable(temp_x)
temp_list = model.feature_list(temp_x)[1]
num_output = len(temp_list)
feature_list = np.empty(num_output)
count = 0
for out in temp_list:
feature_list[count] = out.size(1)
count += 1
################################ edits
print("Calculate SVD before getting sample mean and variance")
# svd_result= lib_generation.get_pca(model, args.num_classes, feature_list, train_loader)
# lib_generation.get_pca_incremental(model, args.num_classes, feature_list, train_loader,args)
svd_result = None
print('get sample mean and covariance')
sample_mean, precision = lib_generation.sample_estimator(
model, args.num_classes, feature_list, train_loader, svd_result, args)
################################ edits_end_sample_generator
print('get Mahalanobis scores')
m_list = [0.0, 0.01, 0.005, 0.002, 0.0014, 0.001, 0.0005]
for magnitude in m_list:
print('Noise: ' + str(magnitude))
for i in range(num_output):
M_in = lib_generation.get_Mahalanobis_score(model, test_loader, args.num_classes, args.outf, \
True, args.net_type, sample_mean, precision, i, magnitude,svd_result,args)
M_in = np.asarray(M_in, dtype=np.float32)
if i == 0:
Mahalanobis_in = M_in.reshape((M_in.shape[0], -1))
else:
Mahalanobis_in = np.concatenate(
(Mahalanobis_in, M_in.reshape((M_in.shape[0], -1))),
axis=1)
for out_dist in out_dist_list:
out_test_loader = data_loader.getNonTargetDataSet(
out_dist, args.batch_size, in_transform, args.dataroot)
print('Out-distribution: ' + out_dist)
for i in range(num_output):
M_out = lib_generation.get_Mahalanobis_score(model, out_test_loader, args.num_classes, args.outf, \
False, args.net_type, sample_mean, precision, i, magnitude,svd_result,args)
M_out = np.asarray(M_out, dtype=np.float32)
if i == 0:
Mahalanobis_out = M_out.reshape((M_out.shape[0], -1))
else:
Mahalanobis_out = np.concatenate(
(Mahalanobis_out, M_out.reshape((M_out.shape[0], -1))),
axis=1)
Mahalanobis_in = np.asarray(Mahalanobis_in, dtype=np.float32)
Mahalanobis_out = np.asarray(Mahalanobis_out, dtype=np.float32)
Mahalanobis_data, Mahalanobis_labels = lib_generation.merge_and_generate_labels(
Mahalanobis_out, Mahalanobis_in)
file_name = os.path.join(
args.outf, 'Mahalanobis_%s_%s_%s.npy' %
(str(magnitude), args.dataset, out_dist))
Mahalanobis_data = np.concatenate(
(Mahalanobis_data, Mahalanobis_labels), axis=1)
np.save(file_name, Mahalanobis_data)
def main():
# set the path to pre-trained model and output
pre_trained_net = './pre_trained/' + args.net_type + '_' + args.dataset + '.pth'
args.outf = args.outf + args.net_type + '_' + args.dataset + '/'
if os.path.isdir(args.outf) == False:
os.mkdir(args.outf)
torch.cuda.manual_seed(0)
torch.cuda.set_device(args.gpu)
# check the in-distribution dataset
if args.dataset == 'malaria':
args.num_classes = 2
if args.dataset == 'cifar100':
args.num_classes = 100
# load networks
if args.net_type == 'densenet':
if args.dataset == 'svhn':
model = models.DenseNet3(100, int(args.num_classes))
model.load_state_dict(
torch.load(pre_trained_net,
map_location="cuda:" + str(args.gpu)))
else:
model = torch.load(pre_trained_net,
map_location="cuda:" + str(args.gpu))
in_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((125.3 / 255, 123.0 / 255, 113.9 / 255),
(63.0 / 255, 62.1 / 255.0, 66.7 / 255.0)),
])
elif args.net_type == 'resnet':
model = models.ResNet34(num_c=args.num_classes)
model.load_state_dict(
torch.load(pre_trained_net, map_location="cuda:" + str(args.gpu)))
in_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
elif args.net_type == 'resnet18':
model = models.get_model(args.net_type)
model.load_state_dict(
torch.load(pre_trained_net, map_location="cuda:" + str(args.gpu)))
model.cuda()
print('load model: ' + args.net_type)
# load dataset
# selected_list = torch.LongTensor(selected_list)
# test_clean_data = torch.index_select(test_clean_data, 0, selected_list)
# test_adv_data = torch.index_select(test_adv_data, 0, selected_list)
# test_noisy_data = torch.index_select(test_noisy_data, 0, selected_list)
# test_label = torch.index_select(test_label, 0, selected_list)
save_path = '%s/%s_%s/' % (args.outf, args.net_type, args.dataset)
os.makedirs(save_path, exist_ok=True)
# torch.save(test_clean_data, '%s/clean_data_%s_%s_%s.pth' % (save_path, args.model, args.dataset, attack))
# torch.save(test_adv_data, '%s/adv_data_%s_%s_%s.pth' % (save_path, args.model, args.dataset, attack))
# torch.save(test_noisy_data, '%s/noisy_data_%s_%s_%s.pth' % (save_path, args.model, args.dataset, attack))
# torch.save(test_label, '%s/label_%s_%s_%s.pth' % (save_path, args.model, args.dataset, attack))
print('load target data: ', args.dataset)
train_loader, _ = data_loader.getTargetDataSet(args.dataset,
args.batch_size, None,
args.dataroot)
test_clean_data = torch.load(args.outf + 'clean_data_%s_%s_%s.pth' %
(args.net_type, args.dataset, args.adv_type))
test_adv_data = torch.load(args.outf + 'adv_data_%s_%s_%s.pth' %
(args.net_type, args.dataset, args.adv_type))
test_noisy_data = torch.load(args.outf + 'noisy_data_%s_%s_%s.pth' %
(args.net_type, args.dataset, args.adv_type))
test_label = torch.load(args.outf + 'label_%s_%s_%s.pth' %
(args.net_type, args.dataset, args.adv_type))
# set information about feature extaction
model.eval()
temp_x = torch.rand(2, 3, 32, 32).cuda()
temp_x = Variable(temp_x)
temp_list = model.feature_list(temp_x)[1]
num_output = len(temp_list)
feature_list = np.empty(num_output)
count = 0
for out in temp_list:
feature_list[count] = out.size(1)
count += 1
print('get sample mean and covariance')
sample_mean, precision = lib_generation.sample_estimator(
model, args.num_classes, feature_list, train_loader)
print('get LID scores')
LID, LID_adv, LID_noisy \
= lib_generation.get_LID(model, test_clean_data, test_adv_data, test_noisy_data, test_label, num_output)
overlap_list = [10, 20, 30, 40, 50, 60, 70, 80, 90]
list_counter = 0
for overlap in overlap_list:
Save_LID = np.asarray(LID[list_counter], dtype=np.float32)
Save_LID_adv = np.asarray(LID_adv[list_counter], dtype=np.float32)
Save_LID_noisy = np.asarray(LID_noisy[list_counter], dtype=np.float32)
Save_LID_pos = np.concatenate((Save_LID, Save_LID_noisy))
LID_data, LID_labels = lib_generation.merge_and_generate_labels(
Save_LID_adv, Save_LID_pos)
file_name = os.path.join(
args.outf,
'LID_%s_%s_%s.npy' % (overlap, args.dataset, args.adv_type))
LID_data = np.concatenate((LID_data, LID_labels), axis=1)
np.save(file_name, LID_data)
list_counter += 1
print('get Mahalanobis scores')
m_list = [0.0, 0.01, 0.005, 0.002, 0.0014, 0.001, 0.0005]
for magnitude in m_list:
print('\nNoise: ' + str(magnitude))
for i in range(num_output):
M_in \
= lib_generation.get_Mahalanobis_score_adv(model, test_clean_data, test_label, \
args.num_classes, args.outf, args.net_type, \
sample_mean, precision, i, magnitude)
M_in = np.asarray(M_in, dtype=np.float32)
if i == 0:
Mahalanobis_in = M_in.reshape((M_in.shape[0], -1))
else:
Mahalanobis_in = np.concatenate(
(Mahalanobis_in, M_in.reshape((M_in.shape[0], -1))),
axis=1)
for i in range(num_output):
M_out \
= lib_generation.get_Mahalanobis_score_adv(model, test_adv_data, test_label, \
args.num_classes, args.outf, args.net_type, \
sample_mean, precision, i, magnitude)
M_out = np.asarray(M_out, dtype=np.float32)
if i == 0:
Mahalanobis_out = M_out.reshape((M_out.shape[0], -1))
else:
Mahalanobis_out = np.concatenate(
(Mahalanobis_out, M_out.reshape((M_out.shape[0], -1))),
axis=1)
for i in range(num_output):
M_noisy \
= lib_generation.get_Mahalanobis_score_adv(model, test_noisy_data, test_label, \
args.num_classes, args.outf, args.net_type, \
sample_mean, precision, i, magnitude)
M_noisy = np.asarray(M_noisy, dtype=np.float32)
if i == 0:
Mahalanobis_noisy = M_noisy.reshape((M_noisy.shape[0], -1))
else:
Mahalanobis_noisy = np.concatenate(
(Mahalanobis_noisy, M_noisy.reshape(
(M_noisy.shape[0], -1))),
axis=1)
Mahalanobis_in = np.asarray(Mahalanobis_in, dtype=np.float32)
Mahalanobis_out = np.asarray(Mahalanobis_out, dtype=np.float32)
Mahalanobis_noisy = np.asarray(Mahalanobis_noisy, dtype=np.float32)
Mahalanobis_pos = np.concatenate((Mahalanobis_in, Mahalanobis_noisy))
Mahalanobis_data, Mahalanobis_labels = lib_generation.merge_and_generate_labels(
Mahalanobis_out, Mahalanobis_pos)
file_name = os.path.join(
args.outf, 'Mahalanobis_%s_%s_%s.npy' %
(str(magnitude), args.dataset, args.adv_type))
Mahalanobis_data = np.concatenate(
(Mahalanobis_data, Mahalanobis_labels), axis=1)
np.save(file_name, Mahalanobis_data)
def main():
# set the path to pre-trained model and output
pre_trained_net = "./pre_trained/" + args.net_type + "_" + args.dataset + ".pth"
args.outf = args.outf + args.net_type + "_" + args.dataset + "/"
if os.path.isdir(args.outf) == False:
os.mkdir(args.outf)
torch.cuda.manual_seed(0)
torch.cuda.set_device(args.gpu)
# check the in-distribution dataset
if args.dataset == "cifar100":
args.num_classes = 100
if args.dataset == "svhn":
out_dist_list = ["cifar10", "imagenet_resize", "lsun_resize"]
else:
out_dist_list = ["svhn", "imagenet_resize", "lsun_resize"]
# load networks
if args.net_type == "densenet":
if args.dataset == "svhn":
model = models.DenseNet3(100, int(args.num_classes))
model.load_state_dict(
torch.load(pre_trained_net,
map_location="cuda:" + str(args.gpu)))
else:
model = torch.load(pre_trained_net,
map_location="cuda:" + str(args.gpu))
in_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
(125.3 / 255, 123.0 / 255, 113.9 / 255),
(63.0 / 255, 62.1 / 255.0, 66.7 / 255.0),
),
])
elif args.net_type == "resnet":
model = models.ResNet34(num_c=args.num_classes)
model.load_state_dict(
torch.load(pre_trained_net, map_location="cuda:" + str(args.gpu)))
in_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
model.cuda()
print("load model: " + args.net_type)
# load dataset
print("load target data: ", args.dataset)
train_loader, test_loader = data_loader.getTargetDataSet(
args.dataset, args.batch_size, in_transform, args.dataroot)
# set information about feature extaction
model.eval()
temp_x = torch.rand(2, 3, 32, 32).cuda()
temp_x = Variable(temp_x)
temp_list = model.feature_list(temp_x)[1]
num_output = len(temp_list)
feature_list = np.empty(num_output)
count = 0
for out in temp_list:
feature_list[count] = out.size(1)
count += 1
print("get sample mean and covariance")
sample_mean, precision = lib_generation.sample_estimator(
model, args.num_classes, feature_list, train_loader)
print("get Mahalanobis scores")
m_list = [0.0, 0.01, 0.005, 0.002, 0.0014, 0.001, 0.0005]
for magnitude in m_list:
print("Noise: " + str(magnitude))
for i in range(num_output):
M_in = lib_generation.get_Mahalanobis_score(
model,
test_loader,
args.num_classes,
args.outf,
True,
args.net_type,
sample_mean,
precision,
i,
magnitude,
)
M_in = np.asarray(M_in, dtype=np.float32)
if i == 0:
Mahalanobis_in = M_in.reshape((M_in.shape[0], -1))
else:
Mahalanobis_in = np.concatenate(
(Mahalanobis_in, M_in.reshape((M_in.shape[0], -1))),
axis=1)
for out_dist in out_dist_list:
out_test_loader = data_loader.getNonTargetDataSet(
out_dist, args.batch_size, in_transform, args.dataroot)
print("Out-distribution: " + out_dist)
for i in range(num_output):
M_out = lib_generation.get_Mahalanobis_score(
model,
out_test_loader,
args.num_classes,
args.outf,
False,
args.net_type,
sample_mean,
precision,
i,
magnitude,
)
M_out = np.asarray(M_out, dtype=np.float32)
if i == 0:
Mahalanobis_out = M_out.reshape((M_out.shape[0], -1))
else:
Mahalanobis_out = np.concatenate(
(Mahalanobis_out, M_out.reshape((M_out.shape[0], -1))),
axis=1)
Mahalanobis_in = np.asarray(Mahalanobis_in, dtype=np.float32)
Mahalanobis_out = np.asarray(Mahalanobis_out, dtype=np.float32)
(
Mahalanobis_data,
Mahalanobis_labels,
) = lib_generation.merge_and_generate_labels(
Mahalanobis_out, Mahalanobis_in)
file_name = os.path.join(
args.outf,
"Mahalanobis_%s_%s_%s.npy" %
(str(magnitude), args.dataset, out_dist),
)
Mahalanobis_data = np.concatenate(
(Mahalanobis_data, Mahalanobis_labels), axis=1)
np.save(file_name, Mahalanobis_data)
def main():
if args.dataset == 'svhn':
out_dist_list = ['cifar10', 'imagenet_resize', 'lsun_resize']
else:
out_dist_list = ['svhn', 'imagenet_resize', 'lsun_resize']
outf = os.path.join(args.outf,
args.net_type + '_' + args.dataset + 'RealNVP')
if os.path.isdir(outf) == False:
os.mkdir(outf)
# torch.cuda.manual_seed(0)
torch.cuda.set_device(args.cuda_index)
if args.dataset == 'cifar100':
args.num_classes = 100
else:
args.num_classes = 10
with open(
os.path.join(
'feature_lists',
'feature_lists_{}_imagenet_resize_{}_Wlinear.pickle'.format(
args.net_type, args.dataset)), 'rb') as f:
[
sample_class_mean, list_features, list_features_test,
list_features_out, A, A_inv, log_abs_det_A_inv
] = pickle.load(f)
num_validation = 10000
v_size_per_class = int(num_validation / args.num_classes)
X = []
X_validation = []
for i in range(args.num_classes):
X_validation.append(
list_features[args.layer][i][:v_size_per_class, :] -
sample_class_mean[args.layer][i])
X.append(list_features[args.layer][i][v_size_per_class:, :] -
sample_class_mean[args.layer][i])
train_loader_X = []
validation_loader_X = []
X_all = 0
for i in range(args.num_classes):
if i == 0:
X_all = X[i]
else:
X_all = torch.cat((X_all, X[i]), 0)
for i in range(args.num_classes):
train_loader_X.append(
torch.utils.data.DataLoader(X[i],
batch_size=args.batch_size,
shuffle=True))
validation_loader_X.append(
torch.utils.data.DataLoader(X_validation[i],
batch_size=args.batch_size,
shuffle=True))
num_features = A_inv[args.layer].shape[0]
num_train = X_all.size(0)
in_features_validation_test = list_features_test[args.layer]
in_features_validation = in_features_validation_test[:2000, :]
out_features_validation_test = list_features_out[args.layer]
out_features_validation = out_features_validation_test[:2000, :]
features_validation = torch.cat(
(in_features_validation, out_features_validation), dim=0)
label_validation = torch.cat(
(torch.cuda.FloatTensor(in_features_validation.size(0)).fill_(1),
torch.cuda.FloatTensor(out_features_validation.size(0)).fill_(0)))
half_features = int(num_features / 2)
zeros = np.zeros(half_features)
ones = np.ones(half_features)
right = np.concatenate((zeros, ones), axis=None)
left = np.concatenate((ones, zeros), axis=None)
masks = torch.from_numpy(
np.array(
[right, left, right, left, right, left, right, left, right,
left]).astype(np.float32)).cuda()
flow = []
optimizer = []
A_layer = torch.tensor(A[args.layer])
A_inv_layer = torch.tensor(A_inv[args.layer])
log_abs_det_A_inv_layer = torch.tensor(log_abs_det_A_inv[args.layer])
t_start = 0
for i in range(args.num_classes):
flow.append(
RealNVP(masks, num_features, args.length_hidden, A_layer,
A_inv_layer, log_abs_det_A_inv_layer))
optimizer.append(
torch.optim.Adam(
[p for p in flow[i].parameters() if p.requires_grad == True],
lr=args.lr))
sample_class_mean_layer = sample_class_mean[args.layer]
loss_vec, auroc_vec, validation_vec = train(train_loader_X, flow, optimizer, args.num_iter, num_train, outf, args.layer, \
args.length_hidden, validation_loader_X, num_validation, features_validation, label_validation, sample_class_mean_layer,\
args.num_classes, args.dataset, t_start)
features_test = list_features_test[args.layer]
test_loader = torch.utils.data.DataLoader(features_test,
batch_size=args.batch_size,
shuffle=False)
score_in = test(test_loader, flow, sample_class_mean_layer,
args.num_classes)
for out_dist in out_dist_list:
with open(
'feature_lists/feature_lists_{}_{}_{}_Wlinear.pickle'.format(
args.net_type, out_dist, args.dataset), 'rb') as f:
[_, _, list_features_test, list_features_out, _, _,
_] = pickle.load(f)
if out_dist == 'FGSM':
features_test = list_features_test[args.layer]
test_loader = torch.utils.data.DataLoader(
features_test, batch_size=args.batch_size, shuffle=False)
score_in = test(test_loader, flow, sample_class_mean_layer,
args.num_classes)
features_out = list_features_out[args.layer]
out_loader = torch.utils.data.DataLoader(features_out,
batch_size=args.batch_size,
shuffle=False)
score_out = test(out_loader, flow, sample_class_mean_layer,
args.num_classes)
pram = {
'out_dist': out_dist,
'Network_type': args.net_type,
'Layer': args.layer,
'Batch_size': args.batch_size,
'num_iter': args.num_iter,
'cuda_index': args.cuda_index,
'lr': args.lr,
'lr_schedule': {
1: args.lr
},
'length_hidden': args.length_hidden,
'dropout': False,
'weight_decay': 0,
'init_zeros': True,
'num_flows': int(len(flow[0].t)),
}
with open(
os.path.join(
outf,
'Residual_flow_%s_%s_layer_%s_%siter_%sflows_%slength_hidden.txt'
% (args.dataset, args.layer, args.num_iter,
int(len(flow[0].t)), args.length_hidden)), 'w') as file:
file.write('date: %s\n' % (datetime.datetime.now()))
file.write(json.dumps(pram))
score_in = np.asarray(score_in, dtype=np.float32)
score_out = np.asarray(score_out, dtype=np.float32)
score_data, score_labels = lib_generation.merge_and_generate_labels(
score_out, score_in)
file_name = os.path.join(
outf,
'Residual_flow_%s_%s_layer_%s_%siter_%sflows_%slength_hidden' %
(args.dataset, out_dist, args.layer, args.num_iter,
int(len(flow[0].t)), args.length_hidden))
score_data = np.concatenate((score_data, score_labels), axis=1)
np.savez(file_name, score_data, loss_vec, pram, auroc_vec,
validation_vec)
std_Z_max = 0
std_Z_min = 1
for i in range(args.num_classes):
std_Z = flow[i].f(X[i], training=False)[0].std(0)
std_Z_max = max(std_Z_max, std_Z.max())
std_Z_min = min(std_Z_min, std_Z.min())
print('std z: \n', std_Z)
print('std z max: \n', std_Z_max)
print('std z min: \n', std_Z_min)
def extract_features(pre_trained_net, in_distribution, in_dist_name,
out_dist_list, out_of_distribution, in_transform, gpu,
batch_size, num_classes):
# set the path to pre-trained model and output
outf = "/output/"
outf = outf + "model" + '_' + in_dist_name + '/'
if os.path.isdir(outf) == False:
os.mkdir(outf)
torch.cuda.manual_seed(0)
torch.cuda.set_device(gpu)
# load networks
model = torch.load(pre_trained_net, map_location="cuda:" + str(gpu))
model.cuda()
print('loaded model')
# load target dataset
validation_split = .2
shuffle_dataset = True
random_seed = 42
# Creating data indices for training and validation splits:
dataset_size = len(in_distribution)
indices = list(range(dataset_size))
split = int(np.floor(validation_split * dataset_size))
if shuffle_dataset:
np.random.seed(random_seed)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
# Creating PT data samplers and loaders:
train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)
if in_transform is not None:
train_loader = torch.utils.data.DataLoader(in_distribution,
batch_size=batch_size,
shuffle=True,
sampler=train_sampler,
**kwargs)
test_loader = torch.utils.data.DataLoader(in_distribution,
batch_size=batch_size,
shuffle=True,
sampler=valid_sampler,
**kwargs)
else:
train_loader = torch.utils.data.DataLoader(in_distribution,
batch_size=batch_size,
shuffle=True,
sampler=train_sampler,
**kwargs)
test_loader = torch.utils.data.DataLoader(in_distribution,
batch_size=batch_size,
shuffle=True,
sampler=valid_sampler,
**kwargs)
print('loaded target data: ', in_dist_name)
# set information about feature extaction
model.eval()
temp_x = torch.rand(*(list(next(iter(train_loader))[0].size()))).cuda()
temp_x = Variable(temp_x)
temp_list = model.feature_list(temp_x)[1]
num_output = len(temp_list)
feature_list = np.empty(num_output)
count = 0
for out in temp_list:
feature_list[count] = out.size(1)
count += 1
print('get sample mean and covariance')
sample_mean, precision = lib_generation.sample_estimator(
model, num_classes, feature_list, train_loader, model_name="model")
print('Generate dataloaders...')
out_test_loaders = []
for out_dist in out_of_distribution:
out_test_loaders.append(
torch.utils.data.DataLoader(out_dist,
batch_size=batch_size,
shuffle=True,
**kwargs))
print('get Mahalanobis scores', num_output)
m_list = [0.0, 0.01, 0.005, 0.002, 0.0014, 0.001, 0.0005]
for magnitude in m_list:
print('Noise: ' + str(magnitude))
for i in range(num_output):
print('layer_num', i)
M_in = lib_generation.get_Mahalanobis_score(model, test_loader, num_classes, outf, \
True, "model", sample_mean, precision, i, magnitude)
M_in = np.asarray(M_in, dtype=np.float32)
if i == 0:
Mahalanobis_in = M_in.reshape((M_in.shape[0], -1))
else:
Mahalanobis_in = np.concatenate(
(Mahalanobis_in, M_in.reshape((M_in.shape[0], -1))),
axis=1)
for out_test_loader, out_dist in zip(out_test_loaders, out_dist_list):
print('Out-distribution: ' + out_dist)
for i in range(num_output):
M_out = lib_generation.get_Mahalanobis_score(model, out_test_loader, num_classes, outf, \
False, "model", sample_mean, precision, i, magnitude)
M_out = np.asarray(M_out, dtype=np.float32)
if i == 0:
Mahalanobis_out = M_out.reshape((M_out.shape[0], -1))
else:
Mahalanobis_out = np.concatenate(
(Mahalanobis_out, M_out.reshape((M_out.shape[0], -1))),
axis=1)
Mahalanobis_in = np.asarray(Mahalanobis_in, dtype=np.float32)
Mahalanobis_out = np.asarray(Mahalanobis_out, dtype=np.float32)
Mahalanobis_data, Mahalanobis_labels = lib_generation.merge_and_generate_labels(
Mahalanobis_out, Mahalanobis_in)
file_name = os.path.join(
outf, 'Mahalanobis_%s_%s_%s.npy' %
(str(magnitude), in_dist_name, out_dist))
Mahalanobis_data = np.concatenate(
(Mahalanobis_data, Mahalanobis_labels), axis=1)
np.save(file_name, Mahalanobis_data)
def main():
if args.validation_src == 'FGSM':
if args.dataset == 'svhn':
out_dist_list = [
'cifar10', 'imagenet_resize', 'lsun_resize', 'FGSM'
]
else:
out_dist_list = ['svhn', 'imagenet_resize', 'lsun_resize', 'FGSM']
else:
if args.dataset == 'svhn':
out_dist_list = ['cifar10', 'imagenet_resize', 'lsun_resize']
else:
out_dist_list = ['svhn', 'imagenet_resize', 'lsun_resize']
outf_load = os.path.join(args.outf,
args.net_type + '_' + args.dataset + 'RealNVP')
outf = os.path.join(
args.outf, args.net_type + '_' + args.dataset + 'RealNVP_magnitude')
if os.path.isdir(outf) == False:
os.mkdir(outf)
# torch.cuda.manual_seed(0)
torch.cuda.set_device(args.cuda_index)
if args.dataset == 'cifar100':
args.num_classes = 100
else:
args.num_classes = 10
with open(
'feature_lists/feature_lists_{}_imagenet_resize_{}_Wlinear.pickle'.
format(args.net_type, args.dataset), 'rb') as f:
[
sample_class_mean, list_features, list_features_test,
list_features_out, A, A_inv, log_abs_det_A_inv
] = pickle.load(f)
pre_trained_net = args.net_type + '_' + args.dataset + '.pth'
pre_trained_net = os.path.join('pre_trained', pre_trained_net)
if args.net_type == 'densenet':
if args.dataset == 'svhn':
model = models.DenseNet3(100, int(args.num_classes))
model.load_state_dict(
torch.load(pre_trained_net,
map_location="cuda:" + str(args.cuda_index)))
else:
model = torch.load(pre_trained_net, map_location="cpu")
for i, (name, module) in enumerate(model._modules.items()):
module = recursion_change_bn(model)
for m in model.modules():
if 'Conv' in str(type(m)):
setattr(m, 'padding_mode', 'zeros')
in_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((125.3 / 255, 123.0 / 255, 113.9 / 255),
(63.0 / 255, 62.1 / 255.0, 66.7 / 255.0)),
])
else:
model = models.ResNet34(num_c=args.num_classes)
model.load_state_dict(
torch.load(pre_trained_net,
map_location="cuda:" + str(args.cuda_index)))
in_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
print('load model: ' + args.net_type)
model.to(device)
model.eval()
# load dataset
print('load in-data: ', args.dataset)
num_layers = len(sample_class_mean)
for layer in range(num_layers):
num_features = A_inv[layer].shape[0]
half_features = int(num_features / 2)
zeros = np.zeros(half_features)
ones = np.ones(half_features)
right = np.concatenate((zeros, ones), axis=None)
left = np.concatenate((ones, zeros), axis=None)
masks = torch.from_numpy(
np.array([
right, left, right, left, right, left, right, left, right, left
]).astype(np.float32)).cuda()
flow = []
# We reduce the number of neurons in the hidden layers due to GPU memory limitations (11 GB in GTX 2080Ti) - comment out this line for larger GPU memory
length_hidden = reture_length_hidden(layer)
A_layer = torch.tensor(A[layer])
A_inv_layer = torch.tensor(A_inv[layer])
log_abs_det_A_inv_layer = torch.tensor(log_abs_det_A_inv[layer])
for i in range(args.num_classes):
MODEL_FLOW = os.path.join(
outf_load,
'model_{}_layer_{}_residual_flow_{}length_hidden'.format(
args.dataset, layer, length_hidden), 'flow_{}'.format(i))
flow.append(
RealNVP(masks, num_features, length_hidden, A_layer,
A_inv_layer, log_abs_det_A_inv_layer))
flow[i].load_state_dict(torch.load(MODEL_FLOW,
map_location="cuda:{}".format(
args.cuda_index)),
strict=False)
flow[i].to(device)
flow[i].eval()
sample_class_mean_layer = sample_class_mean[layer]
m_list = [0.0, 0.01, 0.005, 0.002, 0.0014, 0.001, 0.0005]
for magnitude in m_list:
print('Noise: ' + str(magnitude))
_, test_loader = data_loader.getTargetDataSet(
args.dataset, args.batch_size, in_transform, args.dataroot)
score_in = lib_generation.get_resflow_score(
test_loader, model, layer, args.num_classes, args.net_type,
sample_class_mean_layer, flow, magnitude)
for out_dist in out_dist_list:
print('load out-data: ', out_dist)
if out_dist == 'FGSM':
test_loader, out_loader = data_loader.getFGSM(
args.batch_size, args.dataset, args.net_type)
score_in = lib_generation.get_resflow_score_FGSM(
test_loader, model, layer, args.num_classes,
args.net_type, sample_class_mean_layer, flow,
magnitude)
score_out = lib_generation.get_resflow_score_FGSM(
out_loader, model, layer, args.num_classes,
args.net_type, sample_class_mean_layer, flow,
magnitude)
else:
out_loader = data_loader.getNonTargetDataSet(
out_dist, args.batch_size, in_transform, args.dataroot)
score_out = lib_generation.get_resflow_score(
out_loader, model, layer, args.num_classes,
args.net_type, sample_class_mean_layer, flow,
magnitude)
pram = {
'out_dist': out_dist,
'Network_type': args.net_type,
'Layer': layer,
'Batch_size': args.batch_size,
'cuda_index': args.cuda_index,
'length_hidden': length_hidden,
'dropout': False,
'weight_decay': 0,
'init_zeros': True,
'num_flows': int(len(flow[0].t)),
'magnitude': magnitude,
}
with open(
os.path.join(
outf,
'Residual_flow_%s_%s_layer_%s_%smagnitude.txt' %
(args.dataset, out_dist, layer, magnitude)),
'w') as file:
file.write('date: %s\n' % (datetime.datetime.now()))
file.write(json.dumps(pram))
score_in = np.asarray(score_in, dtype=np.float32)
score_out = np.asarray(score_out, dtype=np.float32)
score_data, score_labels = lib_generation.merge_and_generate_labels(
score_out, score_in)
file_name = os.path.join(
outf, 'Residual_flow_%s_%s_layer_%s_%smagnitude' %
(args.dataset, out_dist, layer, magnitude))
score_data = np.concatenate((score_data, score_labels), axis=1)
np.savez(file_name, score_data, pram)
def main():
# set the path to pre-trained model and output
pre_trained_net = './pre_trained/' + args.net_type + '_' + args.dataset + '.pth'
args.outf = args.outf + args.net_type + '_' + args.dataset + '/'
if os.path.isdir(args.outf) == False:
os.mkdir(args.outf)
torch.cuda.manual_seed(0)
torch.cuda.set_device(args.gpu)
# check the in-distribution dataset
if args.dataset == 'cifar100':
args.num_classes = 100
out_dist_list = ['svhn', 'imagenet_resize', 'lsun_resize']
elif args.dataset == 'svhn':
out_dist_list = ['cifar10', 'imagenet_resize', 'lsun_resize']
elif args.dataset == 'ham10000':
#out_dist_list = ['cifar10', 'imagenet_resize', 'face', 'face_age', 'isic-2017', 'isic-2016']
#out_dist_list = ['cifar10', 'face', 'face_age', 'isic-2017', 'isic-2016']
#out_dist_list = ['cifar10', 'cifar100', 'svhn', 'imagenet_resize', 'lsun_resize', 'face', 'face_age', 'isic-2017', 'isic-2016']
out_dist_list = [
'ham10000-avg-smoothing', 'ham10000-brightness',
'ham10000-contrast', 'ham10000-dilation', 'ham10000-erosion',
'ham10000-med-smoothing', 'ham10000-rotation', 'ham10000-shift'
]
# load networks
if args.net_type == 'densenet':
if args.dataset == 'svhn':
model = models.DenseNet3(100, int(args.num_classes))
model.load_state_dict(
torch.load(pre_trained_net,
map_location="cuda:" + str(args.gpu)))
else:
model = torch.load(pre_trained_net,
map_location="cuda:" + str(args.gpu))
in_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((125.3 / 255, 123.0 / 255, 113.9 / 255),
(63.0 / 255, 62.1 / 255.0, 66.7 / 255.0)),
])
elif args.net_type == 'resnet':
model = models.ResNet34(num_c=args.num_classes)
model.load_state_dict(
torch.load(pre_trained_net, map_location="cuda:" + str(args.gpu)))
in_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
elif args.net_type == 'densenet121':
model = DenseNet121(num_classes=args.num_classes)
model.load_state_dict(
torch.load(pre_trained_net,
map_location="cuda:" + str(args.gpu)).state_dict())
in_transform = transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize((0.7630069, 0.5456578, 0.5700767),
(0.14093237, 0.15263236, 0.17000099))
])
model.cuda()
print('load model: ' + args.net_type)
# load dataset
print('load target data: ', args.dataset)
train_loader, test_loader = data_loader.getTargetDataSet(
args.dataset, args.batch_size, in_transform, args.dataroot)
# set information about feature extaction
model.eval()
temp_x = torch.rand(2, 3, 32, 32).cuda()
temp_x = Variable(temp_x)
temp_list = model.feature_list(temp_x)[1]
num_output = len(temp_list)
feature_list = np.empty(num_output)
count = 0
for out in temp_list:
feature_list[count] = out.size(1)
count += 1
print('get sample mean and covariance')
sample_mean, precision = lib_generation.sample_estimator(
model, args.num_classes, feature_list, train_loader)
print('get Mahalanobis scores', num_output)
m_list = [0.0, 0.01, 0.005, 0.002, 0.0014, 0.001, 0.0005]
for magnitude in m_list:
print('Noise: ' + str(magnitude))
for i in range(num_output):
print('layer_num', i)
M_in = lib_generation.get_Mahalanobis_score(model, test_loader, args.num_classes, args.outf, \
True, args.net_type, sample_mean, precision, i, magnitude)
M_in = np.asarray(M_in, dtype=np.float32)
if i == 0:
Mahalanobis_in = M_in.reshape((M_in.shape[0], -1))
else:
Mahalanobis_in = np.concatenate(
(Mahalanobis_in, M_in.reshape((M_in.shape[0], -1))),
axis=1)
for out_dist in out_dist_list:
out_test_loader = data_loader.getNonTargetDataSet(
out_dist, args.batch_size, in_transform, args.dataroot)
print('Out-distribution: ' + out_dist)
for i in range(num_output):
M_out = lib_generation.get_Mahalanobis_score(model, out_test_loader, args.num_classes, args.outf, \
False, args.net_type, sample_mean, precision, i, magnitude)
M_out = np.asarray(M_out, dtype=np.float32)
if i == 0:
Mahalanobis_out = M_out.reshape((M_out.shape[0], -1))
else:
Mahalanobis_out = np.concatenate(
(Mahalanobis_out, M_out.reshape((M_out.shape[0], -1))),
axis=1)
Mahalanobis_in = np.asarray(Mahalanobis_in, dtype=np.float32)
Mahalanobis_out = np.asarray(Mahalanobis_out, dtype=np.float32)
Mahalanobis_data, Mahalanobis_labels = lib_generation.merge_and_generate_labels(
Mahalanobis_out, Mahalanobis_in)
file_name = os.path.join(
args.outf, 'Mahalanobis_%s_%s_%s.npy' %
(str(magnitude), args.dataset, out_dist))
Mahalanobis_data = np.concatenate(
(Mahalanobis_data, Mahalanobis_labels), axis=1)
np.save(file_name, Mahalanobis_data)
def main():
# set the path to pre-trained model and output
pre_trained_net = "./pre_trained/" + args.net_type + "_" + args.dataset + ".pth"
args.outf = args.outf + args.net_type + "_" + args.dataset + "/"
if os.path.isdir(args.outf) == False:
os.mkdir(args.outf)
torch.cuda.manual_seed(0)
torch.cuda.set_device(args.gpu)
# check the in-distribution dataset
if args.dataset == "cifar100":
args.num_classes = 100
# load networks
if args.net_type == "densenet":
if args.dataset == "svhn":
model = models.DenseNet3(100, int(args.num_classes))
model.load_state_dict(
torch.load(pre_trained_net,
map_location="cuda:" + str(args.gpu)))
else:
model = torch.load(pre_trained_net,
map_location="cuda:" + str(args.gpu))
in_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
(125.3 / 255, 123.0 / 255, 113.9 / 255),
(63.0 / 255, 62.1 / 255.0, 66.7 / 255.0),
),
])
elif args.net_type == "resnet":
model = models.ResNet34(num_c=args.num_classes)
model.load_state_dict(
torch.load(pre_trained_net, map_location="cuda:" + str(args.gpu)))
in_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
model.cuda()
print("load model: " + args.net_type)
# load dataset
print("load target data: ", args.dataset)
train_loader, _ = data_loader.getTargetDataSet(args.dataset,
args.batch_size,
in_transform, args.dataroot)
test_clean_data = torch.load(args.outf + "clean_data_%s_%s_%s.pth" %
(args.net_type, args.dataset, args.adv_type))
test_adv_data = torch.load(args.outf + "adv_data_%s_%s_%s.pth" %
(args.net_type, args.dataset, args.adv_type))
test_noisy_data = torch.load(args.outf + "noisy_data_%s_%s_%s.pth" %
(args.net_type, args.dataset, args.adv_type))
test_label = torch.load(args.outf + "label_%s_%s_%s.pth" %
(args.net_type, args.dataset, args.adv_type))
# set information about feature extaction
model.eval()
temp_x = torch.rand(2, 3, 32, 32).cuda()
temp_x = Variable(temp_x)
temp_list = model.feature_list(temp_x)[1]
num_output = len(temp_list)
feature_list = np.empty(num_output)
count = 0
for out in temp_list:
feature_list[count] = out.size(1)
count += 1
print("get sample mean and covariance")
sample_mean, precision = lib_generation.sample_estimator(
model, args.num_classes, feature_list, train_loader)
print("get LID scores")
LID, LID_adv, LID_noisy = lib_generation.get_LID(model, test_clean_data,
test_adv_data,
test_noisy_data,
test_label, num_output)
overlap_list = [10, 20, 30, 40, 50, 60, 70, 80, 90]
list_counter = 0
for overlap in overlap_list:
Save_LID = np.asarray(LID[list_counter], dtype=np.float32)
Save_LID_adv = np.asarray(LID_adv[list_counter], dtype=np.float32)
Save_LID_noisy = np.asarray(LID_noisy[list_counter], dtype=np.float32)
Save_LID_pos = np.concatenate((Save_LID, Save_LID_noisy))
LID_data, LID_labels = lib_generation.merge_and_generate_labels(
Save_LID_adv, Save_LID_pos)
file_name = os.path.join(
args.outf,
"LID_%s_%s_%s.npy" % (overlap, args.dataset, args.adv_type))
LID_data = np.concatenate((LID_data, LID_labels), axis=1)
np.save(file_name, LID_data)
list_counter += 1
print("get Mahalanobis scores")
m_list = [0.0, 0.01, 0.005, 0.002, 0.0014, 0.001, 0.0005]
for magnitude in m_list:
print("\nNoise: " + str(magnitude))
for i in range(num_output):
M_in = lib_generation.get_Mahalanobis_score_adv(
model,
test_clean_data,
test_label,
args.num_classes,
args.outf,
args.net_type,
sample_mean,
precision,
i,
magnitude,
)
M_in = np.asarray(M_in, dtype=np.float32)
if i == 0:
Mahalanobis_in = M_in.reshape((M_in.shape[0], -1))
else:
Mahalanobis_in = np.concatenate(
(Mahalanobis_in, M_in.reshape((M_in.shape[0], -1))),
axis=1)
for i in range(num_output):
M_out = lib_generation.get_Mahalanobis_score_adv(
model,
test_adv_data,
test_label,
args.num_classes,
args.outf,
args.net_type,
sample_mean,
precision,
i,
magnitude,
)
M_out = np.asarray(M_out, dtype=np.float32)
if i == 0:
Mahalanobis_out = M_out.reshape((M_out.shape[0], -1))
else:
Mahalanobis_out = np.concatenate(
(Mahalanobis_out, M_out.reshape((M_out.shape[0], -1))),
axis=1)
for i in range(num_output):
M_noisy = lib_generation.get_Mahalanobis_score_adv(
model,
test_noisy_data,
test_label,
args.num_classes,
args.outf,
args.net_type,
sample_mean,
precision,
i,
magnitude,
)
M_noisy = np.asarray(M_noisy, dtype=np.float32)
if i == 0:
Mahalanobis_noisy = M_noisy.reshape((M_noisy.shape[0], -1))
else:
Mahalanobis_noisy = np.concatenate(
(Mahalanobis_noisy, M_noisy.reshape(
(M_noisy.shape[0], -1))),
axis=1)
Mahalanobis_in = np.asarray(Mahalanobis_in, dtype=np.float32)
Mahalanobis_out = np.asarray(Mahalanobis_out, dtype=np.float32)
Mahalanobis_noisy = np.asarray(Mahalanobis_noisy, dtype=np.float32)
Mahalanobis_pos = np.concatenate((Mahalanobis_in, Mahalanobis_noisy))
Mahalanobis_data, Mahalanobis_labels = lib_generation.merge_and_generate_labels(
Mahalanobis_out, Mahalanobis_pos)
file_name = os.path.join(
args.outf,
"Mahalanobis_%s_%s_%s.npy" %
(str(magnitude), args.dataset, args.adv_type),
)
Mahalanobis_data = np.concatenate(
(Mahalanobis_data, Mahalanobis_labels), axis=1)
np.save(file_name, Mahalanobis_data)
def main():
# set the path to pre-trained model and output
args.outf = args.outf + args.net_type + '_' + args.dataset + '/'
if os.path.isdir(args.outf) == False:
os.mkdir(args.outf)
torch.cuda.manual_seed(0)
torch.cuda.set_device(args.gpu)
out_dist_list = [
'skin_cli', 'skin_derm', 'corrupted', 'corrupted_70', 'imgnet', 'nct',
'final_test'
]
# load networks
if args.net_type == 'densenet_121':
model = densenet_121.Net(models.densenet121(pretrained=False), 8)
ckpt = torch.load("../checkpoints/densenet-121/checkpoint.pth")
model.load_state_dict(ckpt['model_state_dict'])
model.eval()
model.cuda()
elif args.net_type == 'mobilenet':
model = mobilenet.Net(models.mobilenet_v2(pretrained=False), 8)
ckpt = torch.load("../checkpoints/mobilenet/checkpoint.pth")
model.load_state_dict(ckpt['model_state_dict'])
model.eval()
model.cuda()
print("Done!")
elif args.net_type == 'resnet_50':
model = resnet_50.Net(models.resnet50(pretrained=False), 8)
ckpt = torch.load("../checkpoints/resnet-50/checkpoint.pth")
model.load_state_dict(ckpt['model_state_dict'])
model.eval()
model.cuda()
print("Done!")
elif args.net_type == 'vgg_16':
model = vgg_16.Net(models.vgg16_bn(pretrained=False), 8)
ckpt = torch.load("../checkpoints/vgg-16/checkpoint.pth")
model.load_state_dict(ckpt['model_state_dict'])
model.eval()
model.cuda()
print("Done!")
else:
raise Exception(f"There is no net_type={args.net_type} available.")
in_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
print('load model: ' + args.net_type)
# load dataset
print('load target data: ', args.dataset)
train_loader, test_loader = data_loader.getTargetDataSet(
args.dataset, args.batch_size, in_transform, args.dataroot)
# set information about feature extaction
model.eval()
temp_x = torch.rand(2, 3, 224, 224).cuda()
temp_x = Variable(temp_x)
temp_list = model.feature_list(temp_x)[1]
num_output = len(temp_list)
feature_list = np.empty(num_output)
count = 0
for out in temp_list:
feature_list[count] = out.size(1)
count += 1
print('get sample mean and covariance')
sample_mean, precision = lib_generation.sample_estimator(
model, args.num_classes, feature_list, train_loader)
print('get Mahalanobis scores')
m_list = [0.0, 0.01, 0.005, 0.002, 0.0014, 0.001, 0.0005]
for magnitude in m_list:
print('Noise: ' + str(magnitude))
for i in range(num_output):
M_in = lib_generation.get_Mahalanobis_score(model, test_loader, args.num_classes, args.outf, \
True, args.net_type, sample_mean, precision, i, magnitude)
M_in = np.asarray(M_in, dtype=np.float32)
if i == 0:
Mahalanobis_in = M_in.reshape((M_in.shape[0], -1))
else:
Mahalanobis_in = np.concatenate(
(Mahalanobis_in, M_in.reshape((M_in.shape[0], -1))),
axis=1)
for out_dist in out_dist_list:
out_test_loader = data_loader.getNonTargetDataSet(
out_dist, args.batch_size, in_transform, args.dataroot)
print('Out-distribution: ' + out_dist)
for i in range(num_output):
M_out = lib_generation.get_Mahalanobis_score(model, out_test_loader, args.num_classes, args.outf, \
False, args.net_type, sample_mean, precision, i, magnitude)
M_out = np.asarray(M_out, dtype=np.float32)
if i == 0:
Mahalanobis_out = M_out.reshape((M_out.shape[0], -1))
else:
Mahalanobis_out = np.concatenate(
(Mahalanobis_out, M_out.reshape((M_out.shape[0], -1))),
axis=1)
Mahalanobis_in = np.asarray(Mahalanobis_in, dtype=np.float32)
Mahalanobis_out = np.asarray(Mahalanobis_out, dtype=np.float32)
Mahalanobis_data, Mahalanobis_labels = lib_generation.merge_and_generate_labels(
Mahalanobis_out, Mahalanobis_in)
file_name = os.path.join(
args.outf, 'Mahalanobis_%s_%s_%s.npy' %
(str(magnitude), args.dataset, out_dist))
Mahalanobis_data = np.concatenate(
(Mahalanobis_data, Mahalanobis_labels), axis=1)
np.save(file_name, Mahalanobis_data)
def get_conf(config, device, model, in_transform, train_loader, test_loader):
# get layer list
layer_list = config['exp_params']['feature_layers']
# get feature list
model.eval()
input_dim = config['exp_params']['input_dim'] # 2 x 3 x 32 x 32
if config['exp_params']['dataset'] != 'toy_data':
temp_x = torch.rand(2, input_dim[0], input_dim[1],
input_dim[2]).to(device)
else:
temp_x = torch.rand(2, 2).to(device)
_, temp_list = model.feature_list(temp_x, layer_list)
# ASK : Why this round about way to get feature_list size? Using temp_x etc.
# RESPONSE: number of layers
num_output = len(temp_list)
feature_list = np.empty(num_output)
count = 0
for out in temp_list:
feature_list[count] = out.size(1)
count += 1
# m_list is the list of noise
m_list = config['exp_params']['noise_params']['m_list']
# calculate confidence components to be sent to regressor
regressor_features = config['exp_params']['regressor_features']
class_mean, class_precision, tied_precision, pca_list, knn_search_list, knn_mean, knn_precision = \
get_inputs_for_computing_regressor_feature(regressor_features, model, config, num_output, feature_list, layer_list, train_loader, device)
print("For in-distribution: {}".format(config['exp_params']['dataset']))
init_reg_in = True
for regressor_feature in regressor_features:
# num_output is the number of layers
for i in range(num_output):
in_dist_input = get_features_for_regressor(
regressor_feature, model, config, test_loader,
config['exp_params']['dataset'], i, True, device, class_mean,
class_precision, tied_precision, pca_list, knn_search_list,
knn_mean, knn_precision)
print("in_dist_input shape: ", in_dist_input.shape)
in_dist_input = np.asarray(in_dist_input, dtype=np.float32)
# ASK: what does score of regression mean?
print("Mean score at layer {} for regression type {}: {}".format(
i, regressor_feature, np.mean(in_dist_input)))
if init_reg_in:
regressor_in_dist_input = in_dist_input.reshape(
(in_dist_input.shape[0], -1))
init_reg_in = False
else:
regressor_in_dist_input = np.concatenate(
(regressor_in_dist_input,
in_dist_input.reshape((in_dist_input.shape[0], -1))),
axis=1)
print("Out-distributions to test agains: ",
config['model_params']['out_dist_list'])
for out_dist in config['model_params']['out_dist_list']:
print('Out-distribution: ' + out_dist)
if out_dist == 'subset_cifar100':
out_test_loader = data_loader.getNonTargetDataSet(
out_dist,
config['trainer_params']['batch_size'],
in_transform,
config['exp_params']['dataroot'],
idx=config['model_params']['out_idx'],
num_oods=config['model_params']['num_ood_samples'])
else:
out_test_loader = data_loader.getNonTargetDataSet(
out_dist, config['trainer_params']['batch_size'], in_transform,
config['exp_params']['dataroot'])
init_reg_in = True
for regressor_feature in regressor_features:
# num_output is the number of layers
for i in range(num_output):
out_dist_input = get_features_for_regressor(
regressor_feature, model, config, out_test_loader,
out_dist, i, False, device, class_mean, class_precision,
tied_precision, pca_list, knn_search_list, knn_mean,
knn_precision)
print("out_dist_input shape- ", out_dist_input.shape)
out_dist_input = np.asarray(out_dist_input, dtype=np.float32)
print(
"Mean score at layer {} for regression type {}: {}".format(
i, regressor_feature, np.mean(out_dist_input)))
if init_reg_in:
regressor_out_dist_input = out_dist_input.reshape(
(out_dist_input.shape[0], -1))
init_reg_in = False
else:
regressor_out_dist_input = np.concatenate(
(regressor_out_dist_input,
out_dist_input.reshape(
(out_dist_input.shape[0], -1))),
axis=1)
regressor_in_dist_input = np.asarray(regressor_in_dist_input,
dtype=np.float32)
regressor_out_dist_input = np.asarray(regressor_out_dist_input,
dtype=np.float32)
ood_output, Mahalanobis_labels = lib_generation.merge_and_generate_labels(
regressor_out_dist_input, regressor_in_dist_input)
file_name = os.path.join(
config['logging_params']['outf'], 'Mahalanobis_%s_%s_%s.npy' %
(str(m_list[0]), config['exp_params']['dataset'], out_dist))
ood_output = np.concatenate((ood_output, Mahalanobis_labels), axis=1)
np.save(file_name, ood_output)
return ood_output