def __del__(self):
"""
Remove log file.
"""
if self.args is not None:
if self.args.log_file:
Log.get_instance().detach(self.args.log_file)
def __init__(self, args=None):
"""
Initialize.
:param args: optional arguments if not to use sys.argv
:type args: [str]
"""
self.args = None
""" Arguments of program. """
parser = self.get_parser()
if args is not None:
self.args = parser.parse_args(args)
else:
self.args = parser.parse_args()
self.test_codes = None
""" (numpy.ndarray) Test classes. """
self.test_theta = None
""" (numpy.ndarray) Transformations for testing. """
self.N_class = None
""" (int) Number of classes. """
self.attack_class = None
""" (attacks.UntargetedAttack) Attack to use (as class). """
self.objective_class = None
""" (attacks.UntargetedObjective) Objective to use (as class). """
self.model = None
""" (encoder.Encoder) Model to train. """
self.perturbations = None
""" (numpy.ndarray) Perturbations per test image. """
self.success = None
""" (numpy.ndarray) Success per test image. """
self.min_bound = None
""" (numpy.ndarray) Minimum bound for codes. """
self.max_bound = None
""" (numpy.ndarray) Maximum bound for codes. """
self.accuracy = None
""" (numpy.ndarray) Accuracy. """
if self.args.log_file:
utils.makedir(os.path.dirname(self.args.log_file))
Log.get_instance().attach(open(self.args.log_file, 'w'))
log('-- ' + self.__class__.__name__)
for key in vars(self.args):
log('[Attack] %s=%s' % (key, str(getattr(self.args, key))))
def __init__(self, args=None):
"""
Initialize.
:param args: optional arguments if not to use sys.argv
:type args: [str]
"""
self.args = None
""" Arguments of program. """
parser = self.get_parser()
if args is not None:
self.args = parser.parse_args(args)
else:
self.args = parser.parse_args()
self.test_fonts = None
""" (numpy.ndarray) Font classes. """
self.test_classes = None
""" (numpy.ndarray) Character classes. """
self.N_attempts = None
""" (int) Number of attempts. """
self.N_samples = None
""" (int) Number of samples. """
self.N_font = None
""" (int) Number of fonts. """
self.N_class = None
""" (int) Number of classes. """
self.model = None
""" (encoder.Encoder) Model to train. """
self.perturbations = None
""" (numpy.ndarray) Perturbations per test image. """
self.perturbation_images = None
""" (numpy.ndarray) Perturbation images. """
if self.args.log_file:
utils.makedir(os.path.dirname(self.args.log_file))
Log.get_instance().attach(open(self.args.log_file, 'w'))
log('-- ' + self.__class__.__name__)
for key in vars(self.args):
log('[Testing] %s=%s' % (key, str(getattr(self.args, key))))
def __init__(self, args=None):
"""
Initialize.
:param args: optional arguments if not to use sys.argv
:type args: [str]
"""
self.args = None
""" Arguments of program. """
parser = self.get_parser()
if args is not None:
self.args = parser.parse_args(args)
else:
self.args = parser.parse_args()
self.test_codes = None
""" (numpy.ndarray) Codes for testing. """
self.perturbation_codes = None
""" (numpy.ndarray) Perturbation codes for testing. """
self.model = None
""" (encoder.Encoder) Model to train. """
self.perturbations = None
""" (numpy.ndarray) Perturbations per test image. """
self.original_accuracy = None
""" (numpy.ndarray) Success of classifier. """
self.transfer_accuracy = None
""" (numpy.ndarray) Success of classifier. """
self.original_success = None
""" (numpy.ndarray) Success per test image. """
self.transfer_success = None
""" (numpy.ndarray) Success per test image. """
if self.args.log_file:
utils.makedir(os.path.dirname(self.args.log_file))
Log.get_instance().attach(open(self.args.log_file, 'w'))
log('-- ' + self.__class__.__name__)
for key in vars(self.args):
log('[Testing] %s=%s' % (key, str(getattr(self.args, key))))
def __init__(self, args=None):
"""
Initialize.
:param args: arguments
:type args: list
"""
self.args = None
""" Arguments of program. """
self.test_images = None
""" (numpy.ndarray) Images to test on. """
self.test_codes = None
""" (numpy.ndarray) Codes for testing. """
self.model = None
""" (encoder.Encoder) Model to train. """
self.loss = None
""" (float) Will hold evalauted loss. """
self.error = None
""" (float) Will hold evaluated error. """
self.accuracy = None
""" (numpy.ndarray) Will hold success. """
self.results = dict()
""" (dict) Will hold evaluation results. """
parser = self.get_parser()
if args is not None:
self.args = parser.parse_args(args)
else:
self.args = parser.parse_args() # sys.args
utils.makedir(os.path.dirname(self.args.log_file))
if self.args.log_file:
Log.get_instance().attach(open(self.args.log_file, 'w'))
log('-- ' + self.__class__.__name__)
for key in vars(self.args):
log('[Testing] %s=%s' % (key, str(getattr(self.args, key))))
def __init__(self, args=None):
"""
Initialize.
:param args: optional arguments if not to use sys.argv
:type args: [str]
"""
self.args = None
""" Arguments of program. """
parser = self.get_parser()
if args is not None:
self.args = parser.parse_args(args)
else:
self.args = parser.parse_args()
self.train_images = None
""" (numpy.ndarray) Images to train on. """
self.train_codes = None
""" (numpy.ndarray) Codes for training. """
self.val_images = None
""" (numpy.ndarray) Images to validate on. """
self.val_codes = None
""" (numpy.ndarray) Codes to validate on. """
self.val_error = None
""" (float) Validation error. """
self.test_images = None
""" (numpy.ndarray) Images to test on. """
self.test_codes = None
""" (numpy.ndarray) Codes for testing. """
self.model = None
""" (encoder.Encoder) Model to train. """
self.scheduler = None
""" (Scheduler) Scheduler for training. """
self.train_statistics = numpy.zeros((0, 6))
""" (numpy.ndarray) Will hold training statistics. """
self.test_statistics = numpy.zeros((0, 5))
""" (numpy.ndarray) Will hold testing statistics. """
self.epoch = 0
""" (int) Current epoch. """
self.N_class = None
""" (int) Number of classes. """
self.results = dict()
""" (dict) Results. """
utils.makedir(os.path.dirname(self.args.state_file))
utils.makedir(os.path.dirname(self.args.log_file))
if self.args.log_file:
Log.get_instance().attach(open(self.args.log_file, 'w'))
log('-- ' + self.__class__.__name__)
for key in vars(self.args):
log('[Training] %s=%s' % (key, str(getattr(self.args, key))))
def __init__(self, args=None):
"""
Initialize.
:param args: optional arguments if not to use sys.argv
:type args: [str]
"""
self.args = None
""" Arguments of program. """
parser = self.get_parser()
if args is not None:
self.args = parser.parse_args(args)
else:
self.args = parser.parse_args()
self.train_images = None
""" (numpy.ndarray) Images to train on. """
self.test_images = None
""" (numpy.ndarray) Images to test on. """
self.train_codes = None
""" (numpy.ndarray) Labels to train on. """
self.test_codes = None
""" (numpy.ndarray) Labels to test on. """
self.resolution = None
""" (int) Resolution. """
self.encoder = None
""" (models.LearnedEncoder) Encoder. """
self.decoder = None
""" (models.LearnedDecoder) Decoder. """
self.reconstruction_error = 0
""" (int) Reconstruction error. """
self.code_mean = 0
""" (int) Reconstruction error. """
self.code_var = 0
""" (int) Reconstruction error. """
self.pred_images = None
""" (numpy.ndarray) Test images reconstructed. """
self.pred_codes = None
""" (numpy.ndarray) Test latent codes. """
self.results = dict()
""" (dict) Results. """
utils.makedir(os.path.dirname(self.args.log_file))
if self.args.log_file:
Log.get_instance().attach(open(self.args.log_file, 'w'))
log('-- ' + self.__class__.__name__)
for key in vars(self.args):
log('[Testing] %s=%s' % (key, str(getattr(self.args, key))))
def __init__(self, args=None):
"""
Initialize.
:param args: optional arguments if not to use sys.argv
:type args: [str]
"""
self.args = None
""" Arguments of program. """
parser = self.get_parser()
if args is not None:
self.args = parser.parse_args(args)
else:
self.args = parser.parse_args()
self.train_images = None
""" (numpy.ndarray) Images to train on. """
self.test_images = None
""" (numpy.ndarray) Images to test on. """
self.train_codes = None
""" (numpy.ndarray) Labels to train on. """
self.test_codes = None
""" (numpy.ndarray) Labels to test on. """
if self.args.log_file:
utils.makedir(os.path.dirname(self.args.log_file))
Log.get_instance().attach(open(self.args.log_file, 'w'))
log('-- ' + self.__class__.__name__)
for key in vars(self.args):
log('[Training] %s=%s' % (key, str(getattr(self.args, key))))
utils.makedir(os.path.dirname(self.args.encoder_file))
utils.makedir(os.path.dirname(self.args.decoder_file))
utils.makedir(os.path.dirname(self.args.log_file))
self.resolution = None
""" (int) Resolution. """
self.encoder = None
""" (models.LearnedVariationalEncoder) Encoder. """
self.decoder = None
""" (models.LearnedDecoder) Decoder. """
self.classifier = None
""" (models.Classifier) Classifier. """
self.encoder_scheduler = None
""" (scheduler.Scheduler) Encoder schduler. """
self.decoder_scheduler = None
""" (scheduler.Scheduler) Decoder schduler. """
self.classifier_scheduler = None
""" (scheduler.Scheduler) Classifier schduler. """
self.random_codes = None
""" (numyp.ndarray) Random codes. """
self.train_statistics = numpy.zeros((0, 15))
""" (numpy.ndarray) Will hold training statistics. """
self.test_statistics = numpy.zeros((0, 12))
""" (numpy.ndarray) Will hold testing statistics. """
self.results = dict()
""" (dict) Results. """
self.logvar = -2.5
""" (float) Log-variance hyper parameter. """
