def run(self):
self.output = make_output_folder(self.input, self.output, self.force)
train()
if self.input is not None:
model = get_model()
from fifty.commands.whatis import WhatIs
classifier = WhatIs(self.args)
gen_files = read_files(self.input, self.block_size, self.recursive)
try:
while True:
file, file_name = next(gen_files)
pred_probability = classifier.infer(model, file)
classifier.output_predictions(pred_probability, file_name)
del file, file_name
except:
pass
else:
print('No input file given for inference on trained model.')
return
def run(self):
if self.input is None:
raise Exception("Input not provided, please see usage")
if self.verbose >= 1:
self.output = make_output_folder(self.input, self.output, self.force)
model = self.get_model()
gen_files = read_files(self.input, self.block_size, self.recursive)
if self.verbose >= 2:
print('Predicting..... Please be patient!')
try:
while True:
file, file_name = next(gen_files)
pred_probability = self.infer(model, file)
self.output_predictions(pred_probability, file_name)
del file, file_name
except Exception as e:
print(e)
if self.verbose >= 2:
print('Prediction Complete!')
return
def run(self):
if self.input is None:
parser.print_usage()
sys.exit(1)
if self.verbose >= 1:
self.output = make_output_folder(self.input, self.output, self.force)
model = self.get_model()
gen_files = read_files(self.input, self.block_size, self.recursive)
if self.verbose >= 2:
print('Predicting..... Please be patient!')
try:
while True:
file, file_name = next(gen_files)
pred_probability = self.infer(model, file)
self.output_predictions(pred_probability, file_name)
del file, file_name
except:
pass
if self.verbose >= 2:
print('Prediction Complete!')
return
def run(self):
self.output = make_output_folder(self.input, self.output, self.force)
self.train_model()
if self.input is None:
raise Exception(
'No input blocks given for inference on trained model.')
model = self.get_model()
print('evaluating model...')
gen_files = read_files(self.input, self.block_size, self.recursive)
if self.is_train_autoencoder:
try:
start_time = time.time()
for i, (blocks, file_name) in enumerate(gen_files):
# keep only 1/100 of the elements (for performance)
blocks = blocks[:int(np.ceil(len(blocks) * 0.01))]
print('loaded files in\t{:.2f}s.\t'.format(time.time() -
start_time),
end='')
time_ = time.time()
output = model.predict(blocks)
print('predicted in\t{:.2f}s.\t'.format(time.time() -
time_),
end='')
time_ = time.time()
# flattening
plt.plot(blocks.flatten(),
label='input',
alpha=0.7,
linewidth=0.5)
plt.plot(output.flatten(),
label='output',
alpha=0.7,
linewidth=0.5)
plt.title(f'Autoencoder reconstruction {i}')
plt.xlabel(f'Byte index')
plt.ylabel(f'Byte value')
plt.legend(loc='upper right')
plt.savefig(
os.path.join(self.model_dir(),
f'reconstruction_{i}_{time.time()}.png'))
plt.show()
print('plotted in\t{:.2f}s, total time:\t{:.2f}s'.format(
time.time() - time_,
time.time() - start_time))
del blocks, file_name
start_time = time.time()
except Exception as e:
print("!Error encountered while predicting: " + str(e))
import traceback
traceback.print_exc()
else:
from fifty.commands.whatis import WhatIs
classifier = WhatIs(self.options, *self.args)
try:
start_time = time.time()
for blocks, file_name in gen_files:
print('loaded files in\t{:.2f}s.\t'.format(time.time() -
start_time),
end='')
time_ = time.time()
pred_probability = classifier.infer(model, blocks)
print('predicted in\t{:.2f}s.\t'.format(time.time() -
time_),
end='')
time_ = time.time()
classifier.output_predictions(pred_probability, file_name)
print('plotted in\t{:.2f}s, total time:\t{:.2f}s'.format(
time.time() - time_,
time.time() - start_time))
del blocks, file_name
start_time = time.time()
except Exception as e:
print("!Error encountered while predicting: " + str(e))
import traceback
traceback.print_exc()