def random_attack(
sentence,
y_true,
net,
vocab,
tokenizer,
maxlen,
verbose=False,
sub_rate_limit=None):
doc = nlp(sentence)
if sub_rate_limit: sub_rate_limit = int(sub_rate_limit * len(doc))
else: sub_rate_limit = len(doc)
def halt_conditionh_func(perturbed_text):
perturbed_vector = str2seq(perturbed_text, vocab, tokenizer, maxlen).to(config_device)
predict = net.predict_class(perturbed_vector)[0]
return predict != y_true
candidates_list = []
for idx, token in enumerate(doc):
if idx >= maxlen: break
candidates = _generate_synonym_candidates(token=token, token_position=idx, rank_fn=None)
if len(candidates) > 0: candidates_list.append((idx, candidates))
upper = min(len(candidates_list), sub_rate_limit)
lower = upper // 3
sub_num = get_random(lower, upper)
sub_pos = random.sample(candidates_list, sub_num)
change_tuple_list = []
accepted_candidates = []
for token_pos, candidates in sub_pos:
substitution = random.sample(candidates, 1)[0]
accepted_candidates.append(substitution)
change_tuple_list.append((token_pos, substitution.original_token, substitution.candidate_word, None, 'tagNONE'))
perturbed_text = ' '.join(
_compile_perturbed_tokens(doc, accepted_candidates))
if halt_conditionh_func(perturbed_text): break
if verbose:
print(f'origin token pos {token_pos}, origin token {substitution.original_token}, candidate token {substitution.candidate_word}')
perturbed_text = ' '.join(
_compile_perturbed_tokens(doc, accepted_candidates))
sub_rate = len(change_tuple_list) / len(doc)
ne_rate = 0.0
adv_vec = str2seq(perturbed_text, vocab, tokenizer, maxlen).to(config_device)
adv_y = net.predict_class(adv_vec)[0]
return perturbed_text, adv_y, sub_rate, ne_rate, change_tuple_list
def build_word_index_map(self, vocab):
assert len(self.syn_dict) > 0
assert len(vocab.word_dict) > 0
self.syn_index_dict[0] = set()
for key, value in self.syn_dict.items():
res = {vocab.get_index(v) for v in value}
if get_random(0, 1) == 1:
res.add(0)
idx = vocab.get_index(key)
if idx == 0:
self.syn_index_dict[idx] |= res
else:
self.syn_index_dict[idx] = res
unknowns = set(random.sample(self.syn_index_dict.keys(), 10))
unknowns.add(0)
self.syn_index_dict[0] |= set(unknowns)
def random_mask(self, X: torch.Tensor, mask_low=1, mask_rate=0.15):
limit = int(X.size()[-1] * mask_rate)
limit = get_random(mask_low, limit)
temp = [i for i in range(X.size()[-1])]
count = 0
replaced = set()
loop = 0
while count < limit and loop < 5:
loop += 1
pos = random.sample(temp, limit - count)
for p in pos:
if count == limit: break
if p in replaced: continue
ori_word = X[p].item()
res = self.get_syn_words_index(ori_word)
if len(res) > 0:
s = random.sample(res, 1)
X[p] = torch.tensor(s)
count += 1
# print(f'{count} sub with {p} {s}')
replaced.add(p)
flag = True if count > 0 else False
return X, flag
# Loop; break the loop with a keystroke
while(keep_running):
light = T = C = dC = L = Z = 0
T_correction = -5.1 # difference between on-wire and on-board temperature sensors
# measure sensors
if (full_functional):
light = drdaq.get_light() # light intensity [%]
T = drdaq.get_ext1() # temperature [degC]
#T = max(drdaq.get_ext1(), drdaq.get_temperature()+T_correction) # temperature [degC]
#C = fdc2214.read_ch1() # capacitance [pF]
#dC = fdc2214.read_ch10() # capacitance diffrence [pF]
L = ldc1000.read_inductance() # inductance [uH]
Z = ldc1000.read_impedance() # impedance [kOhm]
else:
light = tools.get_random()
T = tools.get_random()
C = tools.get_random()
dC = tools.get_random()
L = tools.get_random()
Z = tools.get_random()
# encode temperature, inductance and impedance into LED RGB values
red = min(255, max(0, math.exp(T * 0.22) - 210))
#green = min(255, math.exp(L * 0.26))
green = 255 * light / 100
#blue = min(255, math.exp(Z * 0.44))
blue = 0
drdaq.set_led(int(red), int(green), int(blue))
# compose string to the console
ssh = paramiko.SSHClient()
cmd_to_execute = "python3 /home/robot/motor_movement/angle_example.py"
ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
ssh.connect(server, username=username, password=password)
'''
imax = 30
L_min = math.inf
L_max = -math.inf
print("Calibrating for " + str(imax*0.1) + " seconds, please wait...")
for i in range(imax):
# measure sensors
if (full_functional):
L = ldc1000.read_inductance() # inductance [uH]
else:
L = tools.get_random()
if (L < L_min): L_min = L
if (L > L_max): L_max = L
time.sleep(0.1)
L_0 = (L_max + L_min)/2
L_step = L_max - L_min
print("Calibration ready: L_0 = " + format_d(L_0) + ", L_step = " + format_d(L_step))
time.sleep(0.5)
print("L[uH], Z[kOHm]")
# Loop; break the loop with a keystroke
while(keep_running):
# measure sensors
while (keep_running):
#light = T = C = dC = L = Z = 0
T_correction = -5.1 # difference between on-wire and on-board temperature sensors
# measure sensors
if (full_functional):
#light = drdaq.get_light() # light intensity [%]
#T = drdaq.get_ext1() # temperature [degC]
#T = max(drdaq.get_ext1(), drdaq.get_temperature()+T_correction) # temperature [degC]
C = fdc2214.read_ch0() # capacitance [pF]
#dC = fdc2214.read_ch10() # capacitance diffrence [pF]
#L = ldc1000.read_inductance() # inductance [uH]
#Z = ldc1000.read_impedance() # impedance [kOhm]
else:
#light = tools.get_random()
T = tools.get_random()
C = tools.get_random()
dC = tools.get_random()
#L = tools.get_random()
#Z = tools.get_random()
# encode temperature, inductance and impedance into LED RGB values
red = min(255, max(0, math.exp(T * 0.22) - 210))
#green = min(255, math.exp(L * 0.26))
green = 255 * light / 100
#blue = min(255, math.exp(Z * 0.44))
blue = 0
drdaq.set_led(int(red), int(green), int(blue))
# compose string to the console
results = ""