def run_defense_work(self, work_id):
"""Runs one defense work.
Args:
work_id: ID of the piece of work to run
Returns:
elapsed_time_sec, submission_id - elapsed time and id of the submission
Raises:
WorkerError: if error occurred during execution.
"""
class_batch_id = (
self.defense_work.work[work_id]['output_classification_batch_id'])
class_batch = self.class_batches.read_batch_from_datastore(class_batch_id)
adversarial_batch_id = class_batch['adversarial_batch_id']
submission_id = class_batch['submission_id']
cloud_result_path = class_batch['result_path']
logging.info('Defense work piece: '
'adversarial_batch_id="%s" submission_id="%s"',
adversarial_batch_id, submission_id)
if submission_id in self.blacklisted_submissions:
raise WorkerError('Blacklisted submission')
# get defense
defense = DefenseSubmission(submission_id, self.submissions,
self.storage_bucket)
defense.download()
# prepare input - copy adversarial batch locally
input_dir = os.path.join(LOCAL_INPUT_DIR, adversarial_batch_id)
if os.path.exists(input_dir):
sudo_remove_dirtree(input_dir)
os.makedirs(input_dir)
try:
shell_call([
'gsutil', '-m', 'cp',
# typical location of adv batch:
# testing-round/adversarial_images/ADVBATCH000/
os.path.join('gs://', self.storage_bucket, self.round_name,
'adversarial_images', adversarial_batch_id, '*'),
input_dir
])
adv_images_files = os.listdir(input_dir)
if (len(adv_images_files) == 1) and adv_images_files[0].endswith('.zip'):
logging.info('Adversarial batch is in zip archive %s',
adv_images_files[0])
shell_call([
'unzip', os.path.join(input_dir, adv_images_files[0]),
'-d', input_dir
])
os.remove(os.path.join(input_dir, adv_images_files[0]))
adv_images_files = os.listdir(input_dir)
logging.info('%d adversarial images copied', len(adv_images_files))
except (subprocess.CalledProcessError, IOError) as e:
raise WorkerError('Can''t copy adversarial batch locally', e)
# prepare output directory
if os.path.exists(LOCAL_OUTPUT_DIR):
sudo_remove_dirtree(LOCAL_OUTPUT_DIR)
os.mkdir(LOCAL_OUTPUT_DIR)
output_filname = os.path.join(LOCAL_OUTPUT_DIR, 'result.csv')
# run defense
elapsed_time_sec = defense.run(input_dir, output_filname)
# evaluate defense result
batch_result = eval_lib.analyze_one_classification_result(
storage_client=None,
file_path=output_filname,
adv_batch=self.adv_batches.data[adversarial_batch_id],
dataset_batches=self.dataset_batches,
dataset_meta=self.dataset_meta)
# copy result of the defense into storage
try:
shell_call([
'gsutil', 'cp', output_filname,
os.path.join('gs://', self.storage_bucket, cloud_result_path)
])
except subprocess.CalledProcessError as e:
raise WorkerError('Can''t result to Cloud Storage', e)
return elapsed_time_sec, submission_id, batch_result
def run_defense_work(self, work_id):
"""Runs one defense work.
Args:
work_id: ID of the piece of work to run
Returns:
elapsed_time_sec, submission_id - elapsed time and id of the submission
Raises:
WorkerError: if error occurred during execution.
"""
class_batch_id = (
self.defense_work.work[work_id]['output_classification_batch_id'])
class_batch = self.class_batches.read_batch_from_datastore(class_batch_id)
adversarial_batch_id = class_batch['adversarial_batch_id']
submission_id = class_batch['submission_id']
cloud_result_path = class_batch['result_path']
logging.info('Defense work piece: '
'adversarial_batch_id="%s" submission_id="%s"',
adversarial_batch_id, submission_id)
if submission_id in self.blacklisted_submissions:
raise WorkerError('Blacklisted submission')
# get defense
defense = DefenseSubmission(submission_id, self.submissions,
self.storage_bucket)
defense.download()
# prepare input - copy adversarial batch locally
input_dir = os.path.join(LOCAL_INPUT_DIR, adversarial_batch_id)
if os.path.exists(input_dir):
sudo_remove_dirtree(input_dir)
os.makedirs(input_dir)
try:
shell_call([
'gsutil', '-m', 'cp',
# typical location of adv batch:
# testing-round/adversarial_images/ADVBATCH000/
os.path.join('gs://', self.storage_bucket, self.round_name,
'adversarial_images', adversarial_batch_id, '*'),
input_dir
])
adv_images_files = os.listdir(input_dir)
if (len(adv_images_files) == 1) and adv_images_files[0].endswith('.zip'):
logging.info('Adversarial batch is in zip archive %s',
adv_images_files[0])
shell_call([
'unzip', os.path.join(input_dir, adv_images_files[0]),
'-d', input_dir
])
os.remove(os.path.join(input_dir, adv_images_files[0]))
adv_images_files = os.listdir(input_dir)
logging.info('%d adversarial images copied', len(adv_images_files))
except (subprocess.CalledProcessError, IOError) as e:
raise WorkerError('Can''t copy adversarial batch locally', e)
# prepare output directory
if os.path.exists(LOCAL_OUTPUT_DIR):
sudo_remove_dirtree(LOCAL_OUTPUT_DIR)
os.mkdir(LOCAL_OUTPUT_DIR)
output_filname = os.path.join(LOCAL_OUTPUT_DIR, 'result.csv')
# run defense
elapsed_time_sec = defense.run(input_dir, output_filname)
# evaluate defense result
batch_result = eval_lib.analyze_one_classification_result(
storage_client=None,
file_path=output_filname,
adv_batch=self.adv_batches.data[adversarial_batch_id],
dataset_batches=self.dataset_batches,
dataset_meta=self.dataset_meta)
# copy result of the defense into storage
try:
shell_call([
'gsutil', 'cp', output_filname,
os.path.join('gs://', self.storage_bucket, cloud_result_path)
])
except subprocess.CalledProcessError as e:
raise WorkerError('Can''t result to Cloud Storage', e)
return elapsed_time_sec, submission_id, batch_result