def optimize(self, E, iterations):
M = self.M
W, H = utils.init_wh(M, self.k)
for i in range(iterations):
print(i, utils.objective(M, W, H, E))
pH = H
H = H + self.step * (W.T.mm(E * (M - W.mm(H))))
H = utils.proj(H, pH, self.sticky)
pW = W
W = W + self.step * ((E * (M - W.mm(H))).mm(H.T))
W = utils.proj(W, pW, self.sticky)
return W, H
def optimize(self, E: Tensor, iterations):
M = self.M
W, H = utils.init_wh(M, self.k)
mW, mH = 0, 0
for i in range(iterations):
print(i, utils.objective(M, W, H, E))
prevW, prevH = W, H
W = W + (1 - self.momentum) * mW
H = H + (1 - self.momentum) * mH
(gW, gH) = self.grad(M, W, H, E)
# nW = np.random.normal(0, 0.01 * self.step, (n, self.k))
# nH = np.random.normal(0, 0.01 * self.step, (self.k, m))
W = utils.proj(W - self.step * gW, W, self.sticky)
H = utils.proj(H - self.step * gH, H, self.sticky)
mW = W - prevW
mH = H - prevH
return W, H
def visualize(param, w_default=300):
image_file = param['image_file']
model_file = param['model_file']
proj_param = param['proj_param']
# load image
if not os.path.exists(os.path.join(image_file)):
print "Image file does not exsists. Skip %s"%(image_file)
return
img = Image.open(image_file)
print "Processing %s"%(image_file)
w = w_default
h = int(w * img.height / float(img.width))
img = img.resize((w, h), Image.ANTIALIAS)
img_array = np.array(img)
# load CAD model
if not os.path.exists(os.path.join(model_file)):
print "Model file does not exsists. Skip %s"%(model_file)
return
data_3d = utils.read_obj(model_file)
vertices_3d = data_3d['vertices']
faces = data_3d['faces']
# projection
vertices_2d = utils.proj(
vertices_3d, proj_param['R'], proj_param['d'],
proj_param['uv'], proj_param['f'])
# lay projection onto the image
num_vertices = vertices_2d.shape[0]
num_faces = faces.shape[0]
x_min = np.min(vertices_2d[:, 0])
y_min = np.min(vertices_2d[:, 1])
x_max = np.max(vertices_2d[:, 0])
y_max = np.max(vertices_2d[:, 1])
patches = []
for i in range(0, num_faces):
trivert = np.zeros((3, 2))
trivert[0, :] = vertices_2d[faces[i, 0], :]
trivert[1, :] = vertices_2d[faces[i, 1], :]
trivert[2, :] = vertices_2d[faces[i, 2], :]
triangle = Polygon(trivert)
patches.append(triangle)
p = PatchCollection(patches, alpha=0.2, linewidth=0)
fig,ax = plt.subplots()
plt.imshow(img_array)
ax.add_collection(p)
plt.axis('off')
plt.show()
plt.close(fig)
def get_faces_vertices_2d(param):
model_file = param['model_file']
proj_param = param['proj_param']
# load CAD model
if not os.path.exists(os.path.join(model_file)):
print("Model file does not exsists. Skip %s" % model_file)
return
data_3d = utils.read_obj(model_file)
vertices_3d = data_3d['vertices']
faces = data_3d['faces']
# projection
vertices_2d = utils.proj(vertices_3d, proj_param['R'], proj_param['d'],
proj_param['uv'], proj_param['f'])
return faces, vertices_2d
def decompose(self):
"""
Starting initially with Gram-Schmidt.
"""
self.Q = np.array(self.A)
self.R = np.zeros_like(self.A)
M, N = self.Q.shape
# for each column
for i in range(N):
# for each column on the left of current
for j in range(i):
# calculate projection of ith col on jth col
p = proj(self.Q[:, i], self.Q[:, j])
self.Q[:, i] -= p
# normalize ith colum
self.Q[:, i] /= l2_norm(self.Q[:, i])
# compute R
self.R = np.dot(self.Q.T, self.A)
return self.Q, self.R
def obj_func(x, x0, y0):
# evaluate objective function
# if hard label: -1 if image is correctly classified, 0 otherwise
# (done this way because BayesOpt assumes we want to maximize)
# if soft label, correct logit - highest logit other than correct logit
# in both cases, successful adversarial perturbation iff objective function >= 0
x = transform(x, args.dset, args.arch, args.cos, args.sin).to(device)
x = proj(x, args.eps, args.inf_norm, args.discrete)
with torch.no_grad():
y = cnn_model.predict_scores(x + x0)
if not args.hard_label:
y = torch.log_softmax(y, dim=1)
max_score = y[:, y0]
y, index = torch.sort(y, dim=1, descending=True)
select_index = (index[:, 0] == y0).long()
next_max = y.gather(1, select_index.view(-1, 1)).squeeze()
f = torch.max(max_score - next_max, torch.zeros_like(max_score))
else:
index = torch.argmax(y, dim=1)
f = torch.where(index == y0, torch.ones_like(index),
torch.zeros_like(index)).float()
return -f
from utils import proj
TEMPLATE_LOADERS = (
'django.template.loaders.filesystem.Loader',
'django.template.loaders.app_directories.Loader',
)
TEMPLATE_DIRS = (proj('templates/'))
import sys
from utils import proj
DEBUG = True
TEMPLATE_DEBUG = DEBUG
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': proj('db.sqlite3'),
'USER': '',
'PASSWORD': '',
'HOST': '',
'PORT': '',
}
}
if 'test' in sys.argv:
DATABASES['default']['NAME'] = ':memory:'
SITE_ID = 1
TIME_ZONE = 'America/Chicago'
LANGUAGE_CODE = 'en-us'
USE_TZ = False
USE_I18N = True
USE_L10N = True
SECRET_KEY = 'f3=dg%b600m5aivknk-tld04ucg0a1yj&4+1nxj#a!p3m6kkbp'
def bayes_opt(x0, y0):
"""
Main Bayesian optimization loop. Begins by initializing model, then for each
iteration, it fits the GP to the data, gets a new point with the acquisition
function, adds it to the dataset, and exits if it's a successful attack
"""
best_observed = []
query_count, success = 0, 0
# call helper function to initialize model
train_x, train_obj, mll, model, best_value, mean, std = initialize_model(
x0, y0, n=args.initial_samples)
if args.standardize_every_iter:
train_obj = (train_obj - train_obj.mean()) / train_obj.std()
best_observed.append(best_value)
query_count += args.initial_samples
# run args.iter rounds of BayesOpt after the initial random batch
for _ in range(args.iter):
# fit the model
fit_gpytorch_model(mll)
# define the qNEI acquisition module using a QMC sampler
if args.q != 1:
qmc_sampler = SobolQMCNormalSampler(num_samples=2000, seed=seed)
qEI = qExpectedImprovement(model=model,
sampler=qmc_sampler,
best_f=best_value)
else:
if args.acqf == 'EI':
qEI = ExpectedImprovement(model=model, best_f=best_value)
elif args.acqf == 'PM':
qEI = PosteriorMean(model)
elif args.acqf == 'POI':
qEI = ProbabilityOfImprovement(model, best_f=best_value)
elif args.acqf == 'UCB':
qEI = UpperConfidenceBound(model, beta=args.beta)
# optimize and get new observation
new_x, new_obj = optimize_acqf_and_get_observation(qEI, x0, y0)
if args.standardize:
new_obj = (new_obj - mean) / std
# update training points
train_x = torch.cat((train_x, new_x))
train_obj = torch.cat((train_obj, new_obj))
if args.standardize_every_iter:
train_obj = (train_obj - train_obj.mean()) / train_obj.std()
# update progress
best_value, best_index = train_obj.max(0)
best_observed.append(best_value.item())
best_candidate = train_x[best_index]
# reinitialize the model so it is ready for fitting on next iteration
torch.cuda.empty_cache()
model.set_train_data(train_x, train_obj, strict=False)
# get objective value of best candidate; if we found an adversary, exit
best_candidate = best_candidate.view(1, -1)
best_candidate = transform(best_candidate, args.dset, args.arch,
args.cos, args.sin).to(device)
best_candidate = proj(best_candidate, args.eps, args.inf_norm,
args.discrete)
with torch.no_grad():
adv_label = torch.argmax(
cnn_model.predict_scores(best_candidate + x0))
if adv_label != y0:
success = 1
if args.inf_norm:
print('Adversarial Label', adv_label.item(), 'Norm:',
best_candidate.abs().max().item())
else:
print('Adversarial Label', adv_label.item(), 'Norm:',
best_candidate.norm().item())
return query_count, success
query_count += args.q
# not successful (ran out of query budget)
return query_count, success
import sys
from utils import proj
DEBUG = True
TEMPLATE_DEBUG = DEBUG
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': proj('db.sqlite3'),
'USER': '',
'PASSWORD': '',
'HOST': '',
'PORT': '',
}
}
if 'test' in sys.argv:
DATABASES['default']['NAME'] = ':memory:'
SITE_ID = 1
TIME_ZONE = 'America/Chicago'
LANGUAGE_CODE = 'en-us'
USE_TZ = False
USE_I18N = True
USE_L10N = True
SECRET_KEY = 'f3=dg%b600m5aivknk-tld04ucg0a1yj&4+1nxj#a!p3m6kkbp'
from utils import proj
DEBUG = True
TEMPLATE_DEBUG = DEBUG
DATABASES = {
"default": {
"ENGINE": "django.db.backends.sqlite3",
"NAME": proj("db.sqlite3"),
"USER": "",
"PASSWORD": "",
"HOST": "",
"PORT": "",
}
}
SITE_ID = 1
TIME_ZONE = "America/Los_Angeles"
LANGUAGE_CODE = "en-us"
USE_TZ = True
USE_I18N = True
USE_L10N = True
SECRET_KEY = "f3=dg%b600m5aivknk-tld04ucg0a1yj&4+1nxj#a!p3m6kkbp"
ROOT_URLCONF = "cghub.urls"
WSGI_APPLICATION = "cghub.wsgi.application"
from utils import proj
TEMPLATE_LOADERS = (
'django.template.loaders.filesystem.Loader',
'django.template.loaders.app_directories.Loader',
)
TEMPLATE_DIRS = (
proj('templates/')
)
