def test_jtr_file_one(self):
import argparse, sys
sys.argv = [
'demo_guess_count_file.py', '-w',
'../data/wordlists/test_demo_file_JtR.lst', '-r',
'../data/rulelists/test_demo_file_JtR.rule', '-p',
'../data/testsets/test_demo_file_JtR1.txt', '-s', 'j'
]
args = setup_args() # set up args
parse_args(args)
self.run_guess_count()
def main():
args = setup_args() # set up args
try:
parse_args(args) # parse args
except:
raise
print("Your Running Configuration: {}\n".format(RUNTIME_CONFIG.short_config_string()))
start_processing()
def test_hc_file_three(self):
import argparse, sys
sys.argv = [
'demo_guess_count_file.py', '-w',
'../data/wordlists/test_demo_file_HC.lst', '-r',
'../data/rulelists/test_demo_file_HC.rule', '-p',
'../data/testsets/test_demo_file_HC3.txt', '-s', 'h'
]
args = setup_args() # set up args
parse_args(args)
RUNTIME_CONFIG['batch_size_of_words'] = 1024 * 1024
self.run_guess_count()
from sklearn.preprocessing import LabelEncoder
from sklearn.cross_validation import train_test_split
from sklearn.utils import shuffle
from models import inception_v3
from models import ST_ResNet_FullPre, ResNet_FullPre, ResNet_FullPre_Wide
from utils import load_train_cv, batch_iterator_train, batch_iterator_valid, load_pseudo
from crossvalidation import load_cv_fold
from matplotlib import pyplot
import warnings
warnings.filterwarnings("ignore")
import argparsing
args, unknown_args = argparsing.parse_args()
#TODO: Get pixel mean or Whatever preproc values they used for GoogLeNet and ImageNet
# training params
experiment_label = args.label
PIXELS = 299
ITERS = args.epochs
BATCHSIZE = args.batchsize
LR_SCHEDULE = {
0: 0.0001,
10: 0.00001,
20: 0.000001
}