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. """