def rankConfOnTestSets(self, batch_size):
inf = Inference(self.f, self.cm, self.max_len, batch_size)
scores = []
paths = glob(self.home_tests)
for path in paths:
name = os.path.basename(path).split('-')[0]
print(name)
X, F = readPC(path, self.max_len - 1, encoding='ascii')
R = rank(F)
R = np.array(R)
# apply model
UP = inf.applyBatch(X,
INCLUDE_END_SYMBOL=self.hparams['append_end'])
UP = np.array(UP)
score = getScore(UP, R)
scores += [(name, score)]
return scores
def rankConfOnTestSets(self, batch_size):
inf = Inference(self.AE_model, self.charmap, self.max_len, batch_size)
scores = []
paths = glob(self.home_tests) # 获得路径home_tests('./HOME/TESTs/*.txt')
for path in paths:
name = os.path.basename(path).split('-')[0]
print(name)
X, F = readPC(path, self.max_len - 1, encoding='ascii')
rank_result = rank(F) # 去重并获得排序序号
rank_result = np.array(rank_result)
# apply model
UP = inf.applyBatch(X,
INCLUDE_END_SYMBOL=self.hparams['append_end'])
UP = np.array(UP)
score = getScore(UP, rank_result)
scores += [(name, score)]
return scores
x = x[:-1]
if len(x) <= MAX_LEN and len(x) >= MIN_LEN:
X.append(x)
return X
def write_tsv(output, X, P, encoding=ENCODING):
assert len(X) == len(P)
n = len(X)
with open(output, 'w', encoding=encoding) as f:
for x, p in zip(X, P):
print("%s\t%f" % (x, p), file=f)
if __name__ == '__main__':
try:
model_path = sys.argv[1]
password_file = sys.argv[2]
output_path = sys.argv[3]
except:
print("USAGE: model_path.h5 password_path.txt output_path.txt")
sys.exit(1)
X = read_passwords(password_file)
cm = myPickle.load(CHARMAP)
model = tf.keras.models.load_model(model_path, compile=False)
S = Inference(model, cm, MAX_LEN, BATCH_SIZE)
logP = S.applyBatch(X, TERMINAL_SYMBOL)
write_tsv(output_path, X, logP)
passwords: 口令列表
log_probability: 非归一化的概率列表(log)
encoding: 编码
Returns:
"""
assert len(passwords) == len(log_probability)
n = len(passwords)
with open(output, 'w', encoding=encoding) as f:
for x, p in zip(passwords, log_probability):
print("%s\t%f" % (x, p), file=f)
if __name__ == '__main__':
try:
model_path = sys.argv[1]
password_file = sys.argv[2]
output_path = sys.argv[3]
except:
print("USAGE: model_path.h5 password_path.txt output_path.txt")
sys.exit(1)
passwords = read_passwords(password_file)
charmap = myPickle.load(CHARMAP)
model = tf.keras.models.load_model(model_path, compile=False)
infer = Inference(model, charmap, MAX_LEN, BATCH_SIZE)
logP = infer.applyBatch(passwords, TERMINAL_SYMBOL) # 计算概率
write_tsv(output_path, passwords, logP)
