def search(target, worklist, callbacks):
global ninput
callback_start_scoring = callbacks[6]
callback_scored = callbacks[7]
callback_start_check = callbacks[8]
callback_checked = callbacks[9]
while worklist:
input = worklist.pop()
#print '[+] input %s' % input.filename
child_inputs = expand_execution(input, callbacks)
if not callback_start_check:
print '[+] checking each new input'
else:
callback_start_check(len(child_inputs))
for input in child_inputs:
if not callback_checked:
os.write(sys.stdout.fileno(), ' %s' % input.filename.split('/')[-1])
fault = check(PARAM['PROGNAME'], PARAM['PROGARG'], input.filename, PARAM['FAULT_CHECKER'], PARAM['TAINT_STDIN'])
if not callback_checked:
os.write(sys.stdout.fileno(), '\b' * (len(input.filename.split('/')[-1]) + 4))
if fault:
print '[+] ' + ('@' * 75)
print ' Fault detected on file %s' % input.filename.split('/')[-1]
print ' ' + ('@' * 75)
else:
callback_checked(input.number, fault)
if fault:
filecopy = os.path.join(PARAM['CRASH_FOLDER'], os.path.basename(input.filename))
shutil.copy(input.filename, filecopy)
if not callback_start_scoring:
print '[+] scoring each new input'
else:
callback_start_scoring(len(child_inputs))
for input in child_inputs:
if not callback_scored:
os.write(sys.stdout.fileno(), ' %s' % input.filename.split('/')[-1])
input.note = score(PARAM['PROGNAME'], PARAM['PROGARG'], input.filename, PARAM['TAINT_STDIN'])
#input.note = random_score()
if not callback_scored:
os.write(sys.stdout.fileno(), '\b' * (len(input.filename.split('/')[-1]) + 4))
else:
callback_scored(input)
worklist += child_inputs
worklist.sort(key=lambda x: x.note)
# this is couner-intuitive, but a lot of blocks are executed on
# completely wrong images
if PARAM['PROGNAME'] == '/usr/bin/convert':
worklist.reverse()
session.save(target, PARAM, ninput, worklist)
def train(self, learning_rate, step_num, init_step=None, restoring_file=None):
print('\n%s: training...' % datetime.now())
sys.stdout.flush()
session = Session(self._graph, self.models_dir)
init_step = session.init(self._network, init_step, restoring_file)
session.start()
last_step = init_step+step_num
print('%s: training till: %d steps' %(datetime.now(), last_step))
print_loss = 0
train_loss = None
save_loss = 0
save_step = 0
total_loss = 0
feed_dict={self._lr_placeholder: learning_rate}
for step in range(init_step+1, last_step+1):
start_time = time.time()
_, total_loss_batch, loss_batch = session.run(
[self._train, self._total_loss, self._cross_entropy_losses], feed_dict=feed_dict
)
duration = time.time() - start_time
assert not np.isnan(total_loss_batch), 'Model diverged with loss = NaN'
cross_entropy_loss_value = np.mean(loss_batch)
print_loss += cross_entropy_loss_value
save_loss += cross_entropy_loss_value
total_loss += total_loss_batch
save_step += 1
if ((step - init_step) % Trainer.PRINT_FREQUENCY == 0):
examples_per_sec = Trainer.BATCH_SIZE / duration
format_str = ('%s: step %d, loss = %.2f, lr = %f, '
'(%.1f examples/sec; %.3f sec/batch)')
print_loss /= Trainer.PRINT_FREQUENCY
print(format_str % (datetime.now(), step, print_loss, learning_rate,
examples_per_sec, float(duration)))
print_loss = 0
# Save the model checkpoint and summaries periodically.
if (step == last_step or
(Trainer.SAVE_FREQUENCY is not None and (step - init_step) % Trainer.SAVE_FREQUENCY == 0)):
session.save(step)
total_loss /= save_step
train_loss = save_loss / save_step
print('%s: train_loss = %.3f' % (datetime.now(), train_loss))
if (self.writer):
summary_str = session.run(self._all_summaries, feed_dict=feed_dict)
self.writer.write_summaries(summary_str, step)
self.writer.write_scalars({'losses/training/cross_entropy_loss': train_loss,
'losses/training/total_loss': total_loss}, step)
total_loss = 0
save_loss = 0
save_step = 0
session.stop()
return step, train_loss
def yes_fun():
path, _ = QFileDialog.getSaveFileName(self, filter='(*.yaml)')
try:
session.save(path, self.d_fp, self.s_fp, self.min_dur,
str(self.f_ind))
self.exit()
except FileNotFoundError:
self.annotator.status('unable to find: \'{}\''.format(path))
self.open()
def _writeImage(request):
number = str(random.randrange(1,99999,1))
session = request.getSession()
session["nospam"] = number
session.save()
image = _generateImage(number)
response = request.getResponse()
response.addHeader('Content-Type', 'image/png')
image.save(response, "PNG")
def train(self, learning_rate, step_num, init_step=None, restoring_file=None):
print('%s: training...' % datetime.now())
sys.stdout.flush()
session = Session(self._graph, self.models_dir)
init_step = session.init(self._network, init_step, restoring_file)
session.start()
last_step = init_step+step_num
print('%s: training till: %d steps' %(datetime.now(), last_step))
print_loss = 0
train_loss = None
save_loss = 0
save_step = 0
feed_dict={self._lr_placeholder: learning_rate}
for step in range(init_step+1, last_step+1):
start_time = time.time()
_, loss_batch = session.run([self._train, self._loss],
feed_dict=feed_dict)
duration = time.time() - start_time
assert not np.isnan(loss_batch), 'Model diverged with loss = NaN'
print_loss += loss_batch
save_loss += loss_batch
save_step += 1
if ((step - init_step) % Trainer.PRINT_FREQUENCY == 0):
examples_per_sec = Trainer.BATCH_SIZE / duration
format_str = ('%s: step %d, loss = %.2f, lr = %f, '
'(%.1f examples/sec; %.3f sec/batch)')
print_loss /= Trainer.PRINT_FREQUENCY
print(format_str % (datetime.now(), step, print_loss, learning_rate,
examples_per_sec, float(duration)))
print_loss = 0
# Save the model checkpoint and summaries periodically.
if (step == last_step or
(Trainer.SAVE_FREQUENCY is not None and (step - init_step) % Trainer.SAVE_FREQUENCY == 0)):
session.save(step)
train_loss = save_loss / save_step
print('%s: train_loss = %.3f' % (datetime.now(), train_loss))
save_loss = 0
save_step = 0
if (self.writer):
summary_str = session.run(self._all_summaries, feed_dict=feed_dict)
self.writer.write_summaries(summary_str, step)
self.writer.write_scalars({'losses/training/total_loss': train_loss}, step)
session.stop()
return step, train_loss
def search(target, worklist, callbacks):
global ninput
global paths
global elapsed
global querytime
global start
global pathssub
global totalcon
global cva_constraints
global cva_paths
callback_start_scoring = callbacks[6]
callback_scored = callbacks[7]
callback_start_check = callbacks[8]
callback_checked = callbacks[9]
accumlist = list(worklist)
current = ninput
#session.save(target, PARAM, ninput, worklist)
#start = time.time()
while worklist:
#start = time.time()
input = worklist.pop()
#print '[+] input %s' % input.filename
child_inputs = expand_execution(input, callbacks)
if USE_ACCUM:
continue
if PARAM['PATH_BOUND'] > 0 and paths >= PARAM['PATH_BOUND']:
if not USE_ACCUM:
accumlist += child_inputs
break;
if not callback_start_check:
print '[+] checking each new input'
else:
callback_start_check(len(child_inputs))
for input in child_inputs:
if not callback_checked:
os.write(sys.stdout.fileno(), ' %s' % input.filename.split('/')[-1])
fault = check(PARAM['PROGNAME'], PARAM['PROGARG'], input.filename, PARAM['FAULT_CHECKER'], PARAM['TAINT_STDIN'])
if not callback_checked:
os.write(sys.stdout.fileno(), '\b' * (len(input.filename.split('/')[-1]) + 4))
if fault:
print '[+] ' + ('@' * 75)
print ' Fault detected on file %s' % input.filename.split('/')[-1]
print ' ' + ('@' * 75)
else:
callback_checked(input.number, fault)
if fault:
filecopy = os.path.join(PARAM['CRASH_FOLDER'], os.path.basename(input.filename))
shutil.copy(input.filename, filecopy)
#elapsed = elapsed + (time.time() - start)
if not callback_start_scoring:
print '[+] scoring each new input'
else:
callback_start_scoring(len(child_inputs))
for input in child_inputs:
if not callback_scored:
os.write(sys.stdout.fileno(), ' %s' % input.filename.split('/')[-1])
input.note = score(PARAM['PROGNAME'], PARAM['PROGARG'], input.filename, PARAM['TAINT_STDIN'])
#input.note = random_score()
if not callback_scored:
os.write(sys.stdout.fileno(), '\b' * (len(input.filename.split('/')[-1]) + 4))
else:
callback_scored(input)
worklist += child_inputs
accumlist += child_inputs
worklist.sort(key=lambda x: x.note)
#worklist.sort(key=lambda x: x.note, reverse = True)
#worklist.reverse()
# this is counter-intuitive, but a lot of blocks are executed on
# completely wrong images
if PARAM['PROGNAME'] == '/usr/bin/convert':
worklist.reverse()
#session.save(target, PARAM, ninput, worklist)
elapsed = (time.time() - start)
session.save(target, PARAM, ninput, accumlist)
print 'Paths Explored: %s Feasible Paths: %s Total Constraints: %s Actual Constraints: %s Time Taken: %s Valgrind Time: %s'\
% (paths,ninput - current,totalcon,pathssub,round(elapsed,2),round(querytime,2))
def search(target, worklist, callbacks):
global ninput
global paths
global elapsed
global querytime
global start
global pathssub
global totalcon
global cva_constraints
global cva_paths
global graphplot
global x
global y
global pcstrings
callback_start_scoring = callbacks[6]
callback_scored = callbacks[7]
callback_start_check = callbacks[8]
callback_checked = callbacks[9]
accumlist = list(worklist)
current = ninput
#session.save(target, PARAM, ninput, worklist)
#start = time.time()
while worklist:
#start = time.time()
input = worklist.pop()
#print '[+] input %s' % input.filename
child_inputs = expand_execution(input, callbacks)
if USE_ACCUM:
continue
if PARAM['PATH_BOUND'] > 0 and paths >= PARAM['PATH_BOUND']:
if not USE_ACCUM:
accumlist += child_inputs
break;
if not callback_start_check:
print '[+] checking each new input'
else:
callback_start_check(len(child_inputs))
for input in child_inputs:
if not callback_checked:
os.write(sys.stdout.fileno(), ' %s' % input.filename.split('/')[-1])
fault = check(PARAM['PROGNAME'], PARAM['PROGARG'], input.filename, PARAM['FAULT_CHECKER'], PARAM['TAINT_STDIN'])
if not callback_checked:
os.write(sys.stdout.fileno(), '\b' * (len(input.filename.split('/')[-1]) + 4))
if fault:
print '[+] ' + ('@' * 75)
print ' Fault detected on file %s' % input.filename.split('/')[-1]
print ' ' + ('@' * 75)
else:
callback_checked(input.number, fault)
if fault:
filecopy = os.path.join(PARAM['CRASH_FOLDER'], os.path.basename(input.filename))
shutil.copy(input.filename, filecopy)
#elapsed = elapsed + (time.time() - start)
if not callback_start_scoring:
print '[+] scoring each new input'
else:
callback_start_scoring(len(child_inputs))
for input in child_inputs:
if not callback_scored:
os.write(sys.stdout.fileno(), ' %s' % input.filename.split('/')[-1])
input.note = score(PARAM['PROGNAME'], PARAM['PROGARG'], input.filename, PARAM['TAINT_STDIN'])
#input.note = random_score()
if not callback_scored:
os.write(sys.stdout.fileno(), '\b' * (len(input.filename.split('/')[-1]) + 4))
else:
callback_scored(input)
worklist += child_inputs
accumlist += child_inputs
worklist.sort(key=lambda x: x.note)
#worklist.sort(key=lambda x: x.note, reverse = True)
#worklist.reverse()
# this is counter-intuitive, but a lot of blocks are executed on
# completely wrong images
if PARAM['PROGNAME'] == '/usr/bin/convert':
worklist.reverse()
#session.save(target, PARAM, ninput, worklist)
elapsed = (time.time() - start)
session.save(target, PARAM, ninput, accumlist)
print 'Paths Explored: %s Feasible Paths: %s Total Constraints: %s Actual Constraints: %s Time Taken: %s Valgrind Time: %s'\
% (paths,ninput - current,totalcon,pathssub,round(elapsed,2),round(querytime,2))
sx = []
radii = []
sy = []
i = 0;
j = 0;
for s1 in pcstrings :
j = 0;
for s2 in pcstrings :
sx.append(i)
sy.append(j)
#print 's1 : %s' %s1
#print 's2 : %s' %s2
#sim = difflib.SequenceMatcher(a=s1.lower(),b=s2.lower()).ratio()
sim = Levenshtein.seqratio(s1,s2)
radii.append(0.05+(sim/5))
#print 'sx : %d' %i
#print 'sy : %d' %j
#print 'sim : %s' %round(sim,2)
j += 1
i += 1
output_server("Similarity Visualization")
TOOLS="resize,crosshair,pan,wheel_zoom,box_zoom,reset,tap,previewsave,box_select,poly_select,lasso_select"
p = figure(tools=TOOLS)
p.scatter(sx,sy,radius=radii,color='red',fill_alpha=0.6,name='pathsscatter')
p.title = "Path Similarity : "+target
p.xaxis.axis_label = 'Number'
p.yaxis.axis_label = 'Number'
show(p)
)
print(
"Terima kasih telah menggunakan aplikasi Kantong Ajaib >_<"
)
status_sesi = False
status_apps = False
else:
aksi = str(
input(
"Data perubahan terakhir belum disave, save sekarang (Y/N)? "
))
if aksi == 'y' or aksi == "Y":
session.save(array_of_user, array_of_gadget,
array_of_consumable,
array_of_gadget_borrow_history,
array_of_gadget_return_history,
array_of_consumable_history)
# akan tetap melakukan save di directory awal (walaupun pilihan folder save di directory lain)
saved_data_gadget = preparation.loadAndRefreshCSV(
str(rootbaru + "\\" + folderbaru + "\\" +
"gadget.csv"))
saved_data_consumable = preparation.loadAndRefreshCSV(
str(rootbaru + "\\" + folderbaru + "\\" +
"consumable.csv"))
saved_data_gadget_borrow_history = preparation.loadAndRefreshCSV(
str(rootbaru + "\\" + folderbaru + "\\" +
"gadget_borrow_history.csv"))
saved_data_gadget_return_history = preparation.loadAndRefreshCSV(
str(rootbaru + "\\" + folderbaru + "\\" +
def save_session(self, app, session, response):
session.save()
def search(target, worklist, callbacks):
global ninput
global paths
global elapsed
global querytime
global start
global pathssub
global totalcon
global cva_constraints
global cva_paths
callback_start_scoring = callbacks[6]
callback_scored = callbacks[7]
callback_start_check = callbacks[8]
callback_checked = callbacks[9]
accumlist = list(worklist)
current = ninput
#session.save(target, PARAM, ninput, worklist)
#start = time.time()
while worklist:
#start = time.time()
input = worklist.pop()
#print '[+] input %s' % input.filename
child_inputs = expand_execution(input, callbacks)
if USE_ACCUM:
continue
if PARAM['PATH_BOUND'] > 0 and paths >= PARAM['PATH_BOUND']:
if not USE_ACCUM:
accumlist += child_inputs
break
if not callback_start_check:
print '[+] checking each new input'
else:
callback_start_check(len(child_inputs))
for input in child_inputs:
if not callback_checked:
os.write(sys.stdout.fileno(),
' %s' % input.filename.split('/')[-1])
fault = check(PARAM['PROGNAME'], PARAM['PROGARG'], input.filename,
PARAM['FAULT_CHECKER'], PARAM['TAINT_STDIN'])
if not callback_checked:
os.write(sys.stdout.fileno(),
'\b' * (len(input.filename.split('/')[-1]) + 4))
if fault:
print '[+] ' + ('@' * 75)
print ' Fault detected on file %s' % input.filename.split(
'/')[-1]
print ' ' + ('@' * 75)
else:
callback_checked(input.number, fault)
if fault:
filecopy = os.path.join(PARAM['CRASH_FOLDER'],
os.path.basename(input.filename))
shutil.copy(input.filename, filecopy)
#elapsed = elapsed + (time.time() - start)
if not callback_start_scoring:
print '[+] scoring each new input'
else:
callback_start_scoring(len(child_inputs))
for input in child_inputs:
if not callback_scored:
os.write(sys.stdout.fileno(),
' %s' % input.filename.split('/')[-1])
input.note = score(PARAM['PROGNAME'], PARAM['PROGARG'],
input.filename, PARAM['TAINT_STDIN'])
#input.note = random_score()
if not callback_scored:
os.write(sys.stdout.fileno(),
'\b' * (len(input.filename.split('/')[-1]) + 4))
else:
callback_scored(input)
worklist += child_inputs
accumlist += child_inputs
worklist.sort(key=lambda x: x.note)
#worklist.sort(key=lambda x: x.note, reverse = True)
#worklist.reverse()
# this is counter-intuitive, but a lot of blocks are executed on
# completely wrong images
if PARAM['PROGNAME'] == '/usr/bin/convert':
worklist.reverse()
#session.save(target, PARAM, ninput, worklist)
elapsed = (time.time() - start)
session.save(target, PARAM, ninput, accumlist)
print 'Paths Explored: %s Feasible Paths: %s Total Constraints: %s Actual Constraints: %s Time Taken: %s Valgrind Time: %s'\
% (paths,ninput - current,totalcon,pathssub,round(elapsed,2),round(querytime,2))
def search(target, worklist, callbacks):
global ninput
callback_start_scoring = callbacks[6]
callback_scored = callbacks[7]
callback_start_check = callbacks[8]
callback_checked = callbacks[9]
while worklist:
input = worklist.pop()
#print '[+] input %s' % input.filename
child_inputs = expand_execution(input, callbacks)
if not callback_start_check:
print '[+] checking each new input'
else:
callback_start_check(len(child_inputs))
for input in child_inputs:
if not callback_checked:
os.write(sys.stdout.fileno(),
' %s' % input.filename.split('/')[-1])
fault = check(PARAM['PROGNAME'], PARAM['PROGARG'], input.filename,
PARAM['FAULT_CHECKER'], PARAM['TAINT_STDIN'])
if not callback_checked:
os.write(sys.stdout.fileno(),
'\b' * (len(input.filename.split('/')[-1]) + 4))
if fault:
print '[+] ' + ('@' * 75)
print ' Fault detected on file %s' % input.filename.split(
'/')[-1]
print ' ' + ('@' * 75)
else:
callback_checked(input.number, fault)
if fault:
filecopy = os.path.join(PARAM['CRASH_FOLDER'],
os.path.basename(input.filename))
shutil.copy(input.filename, filecopy)
if not callback_start_scoring:
print '[+] scoring each new input'
else:
callback_start_scoring(len(child_inputs))
for input in child_inputs:
if not callback_scored:
os.write(sys.stdout.fileno(),
' %s' % input.filename.split('/')[-1])
input.note = score(PARAM['PROGNAME'], PARAM['PROGARG'],
input.filename, PARAM['TAINT_STDIN'])
#input.note = random_score()
if not callback_scored:
os.write(sys.stdout.fileno(),
'\b' * (len(input.filename.split('/')[-1]) + 4))
else:
callback_scored(input)
worklist += child_inputs
worklist.sort(key=lambda x: x.note)
# this is couner-intuitive, but a lot of blocks are executed on
# completely wrong images
if PARAM['PROGNAME'] == '/usr/bin/convert':
worklist.reverse()
session.save(target, PARAM, ninput, worklist)
def shutdown():
global shutting_down
print('*Shutting down...')
shutting_down = True
session.save()
def search(target, worklist, callbacks):
global ninput
global paths
global elapsed
global querytime
global start
global pathssub
global totalcon
global cva_constraints
global cva_paths
global graphplot
global x
global y
global pcstrings
callback_start_scoring = callbacks[6]
callback_scored = callbacks[7]
callback_start_check = callbacks[8]
callback_checked = callbacks[9]
accumlist = list(worklist)
current = ninput
#session.save(target, PARAM, ninput, worklist)
#start = time.time()
while worklist:
#start = time.time()
input = worklist.pop()
#print '[+] input %s' % input.filename
child_inputs = expand_execution(input, callbacks)
if USE_ACCUM:
continue
if PARAM['PATH_BOUND'] > 0 and paths >= PARAM['PATH_BOUND']:
if not USE_ACCUM:
accumlist += child_inputs
break
if not callback_start_check:
print '[+] checking each new input'
else:
callback_start_check(len(child_inputs))
for input in child_inputs:
if not callback_checked:
os.write(sys.stdout.fileno(),
' %s' % input.filename.split('/')[-1])
fault = check(PARAM['PROGNAME'], PARAM['PROGARG'], input.filename,
PARAM['FAULT_CHECKER'], PARAM['TAINT_STDIN'])
if not callback_checked:
os.write(sys.stdout.fileno(),
'\b' * (len(input.filename.split('/')[-1]) + 4))
if fault:
print '[+] ' + ('@' * 75)
print ' Fault detected on file %s' % input.filename.split(
'/')[-1]
print ' ' + ('@' * 75)
else:
callback_checked(input.number, fault)
if fault:
filecopy = os.path.join(PARAM['CRASH_FOLDER'],
os.path.basename(input.filename))
shutil.copy(input.filename, filecopy)
#elapsed = elapsed + (time.time() - start)
if not callback_start_scoring:
print '[+] scoring each new input'
else:
callback_start_scoring(len(child_inputs))
for input in child_inputs:
if not callback_scored:
os.write(sys.stdout.fileno(),
' %s' % input.filename.split('/')[-1])
input.note = score(PARAM['PROGNAME'], PARAM['PROGARG'],
input.filename, PARAM['TAINT_STDIN'])
#input.note = random_score()
if not callback_scored:
os.write(sys.stdout.fileno(),
'\b' * (len(input.filename.split('/')[-1]) + 4))
else:
callback_scored(input)
worklist += child_inputs
accumlist += child_inputs
worklist.sort(key=lambda x: x.note)
#worklist.sort(key=lambda x: x.note, reverse = True)
#worklist.reverse()
# this is counter-intuitive, but a lot of blocks are executed on
# completely wrong images
if PARAM['PROGNAME'] == '/usr/bin/convert':
worklist.reverse()
#session.save(target, PARAM, ninput, worklist)
elapsed = (time.time() - start)
session.save(target, PARAM, ninput, accumlist)
print 'Paths Explored: %s Feasible Paths: %s Total Constraints: %s Actual Constraints: %s Time Taken: %s Valgrind Time: %s'\
% (paths,ninput - current,totalcon,pathssub,round(elapsed,2),round(querytime,2))
sx = []
radii = []
sy = []
i = 0
j = 0
for s1 in pcstrings:
j = 0
for s2 in pcstrings:
sx.append(i)
sy.append(j)
#print 's1 : %s' %s1
#print 's2 : %s' %s2
#sim = difflib.SequenceMatcher(a=s1.lower(),b=s2.lower()).ratio()
sim = Levenshtein.seqratio(s1, s2)
radii.append(0.05 + (sim / 5))
#print 'sx : %d' %i
#print 'sy : %d' %j
#print 'sim : %s' %round(sim,2)
j += 1
i += 1
output_server("Similarity Visualization")
TOOLS = "resize,crosshair,pan,wheel_zoom,box_zoom,reset,tap,previewsave,box_select,poly_select,lasso_select"
p = figure(tools=TOOLS)
p.scatter(sx,
sy,
radius=radii,
color='red',
fill_alpha=0.6,
name='pathsscatter')
p.title = "Path Similarity : " + target
p.xaxis.axis_label = 'Number'
p.yaxis.axis_label = 'Number'
show(p)
