def check_files(): import os from common import paths def check_file(filepath): if not os.path.exists(filepath): print('File %s not found.' % filepath) check_file(paths.database_file()) check_file(paths.images_file()) check_file(paths.theta_file()) check_file(paths.codes_file()) check_file(paths.test_images_file()) check_file(paths.train_images_file()) check_file(paths.test_theta_file()) check_file(paths.train_theta_file()) check_file(paths.test_codes_file()) check_file(paths.train_codes_file()) check_file(paths.emnist_test_images_file()) check_file(paths.emnist_train_images_file()) check_file(paths.emnist_test_labels_file()) check_file(paths.emnist_train_labels_file()) check_file(paths.fashion_test_images_file()) check_file(paths.fashion_train_images_file()) check_file(paths.fashion_test_labels_file()) check_file(paths.fashion_train_labels_file()) check_file(paths.celeba_test_images_file()) check_file(paths.celeba_train_images_file()) check_file(paths.celeba_test_labels_file()) check_file(paths.celeba_train_labels_file())
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser(description='Attack classifier.') parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing images.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing codes.', type=str) parser.add_argument('-label_index', default=2, help='Label index.', type=int) parser.add_argument('-classifier_file', default=paths.state_file('classifier'), help='Snapshot state file of classifier.', type=str) parser.add_argument('-perturbations_file', default=paths.results_file('classifier/perturbations'), help='HDF5 file containing perturbations.', type=str) parser.add_argument('-original_success_file', default=paths.results_file('classifier/success'), help='HDF5 file containing success.', type=str) parser.add_argument('-transfer_success_file', default=paths.results_file('classifier/transfer_success', help='HDF5 file containing transfer success.'), type=str) parser.add_argument('-original_accuracy_file', default=paths.results_file('classifier/accuracy'), help='HDF5 file containing accuracy.', type=str) parser.add_argument('-transfer_accuracy_file', default=paths.results_file('classifier/transfer_accuracy', help='HDF5 file containing transfer accuracy.'), type=str) parser.add_argument('-log_file', default=paths.log_file('classifier/attacks'), help='Log file.', type=str) parser.add_argument('-batch_size', default=128, help='Batch size of attack.', type=int) parser.add_argument('-no_gpu', dest='use_gpu', action='store_false') # Some network parameters. parser.add_argument('-network_architecture', default='standard', help='Classifier architecture to use.', type=str) parser.add_argument('-network_activation', default='relu', help='Activation function to use.', type=str) parser.add_argument('-network_no_batch_normalization', default=False, help='Do not use batch normalization.', action='store_true') parser.add_argument('-network_channels', default=16, help='Channels of first convolutional layer, afterwards channels are doubled.', type=int) parser.add_argument('-network_dropout', default=False, action='store_true', help='Whether to use dropout.') parser.add_argument('-network_units', default='1024,1024,1024,1024', help='Units for MLP.') return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser(description='Test attacks on classifier.') parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-train_images_file', default=paths.train_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing labels.', type=str) parser.add_argument('-label_index', default=2, help='Column index in label file.', type=int) parser.add_argument('-accuracy_file', default=paths.results_file('learned_decoder/accuracy'), help='Correctly classified test samples of classifier.', type=str) parser.add_argument('-perturbations_file', default=paths.results_file('learned_decoder/perturbations'), help='HDF5 file containing perturbations.', type=str) parser.add_argument('-success_file', default=paths.results_file('learned_decoder/success'), help='HDF5 file indicating attack success.', type=str) parser.add_argument('-plot_directory', default=paths.experiment_dir('learned_decoder'), help='Path to PNG plot file for success rate.', type=str) parser.add_argument('-results_file', default='', help='Path to pickled results file.', type=str) parser.add_argument('-batch_size', default=128, help='Batch size of attack.', type=int) parser.add_argument('-plot_manifolds', default=False, action='store_true', help='Whether to plot manifolds.') parser.add_argument('-no_gpu', dest='use_gpu', action='store_false') # Network. parser.add_argument('-N_theta', default=6, help='Numer of transformations.', type=int) return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser( description='Split generated dataset into training and test sets.') parser.add_argument('-codes_file', default=paths.codes_file(), help='HDF5 file containing codes.', type=str) parser.add_argument('-theta_file', default=paths.theta_file(), help='HDF5 file containing transformations.', type=str) parser.add_argument('-images_file', default=paths.images_file(), help='HDF5 file containing transformed images.', type=str) parser.add_argument('-train_codes_file', default=paths.train_codes_file(), help='HDF5 file containing transformed images.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing transformed images.', type=str) parser.add_argument('-train_theta_file', default=paths.train_theta_file(), help='HDF5 file containing transformed images.', type=str) parser.add_argument('-test_theta_file', default=paths.test_theta_file(), help='HDF5 file containing transformed images.', type=str) parser.add_argument('-train_images_file', default=paths.train_images_file(), help='HDF5 file containing transformed images.', type=str) parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing transformed images.', type=str) parser.add_argument('-N_train', default=960000, help='Train/test split.', type=int) return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser( description='Attack decoder and classifier.') parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file with test images.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing test codes.', type=str) parser.add_argument('-label_index', default=2, help='Label index.', type=int) parser.add_argument( '-perturbations_file', default=paths.results_file('decoder/perturbations'), help='HDF5 file containing perturbations.', type=str) parser.add_argument( '-perturbation_images_file', default=paths.results_file('decoder/perturbation_images'), help='HDF5 file for perturbation images.', type=str) parser.add_argument('-log_file', default=paths.log_file('decoder/attacks'), help='Log file.', type=str) parser.add_argument('-batch_size', default=128, help='Batch size of attack.', type=int) parser.add_argument('-no_gpu', dest='use_gpu', action='store_false') # Some decoder parameters. parser.add_argument('-N_theta', default=6, help='Numer of transformations.', type=int) return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser(description='Train auto encoder.') parser.add_argument('-train_images_file', default=paths.train_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-train_codes_file', default=paths.train_codes_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-label_index', default=2, help='Label index.', type=int) parser.add_argument('-label', default=-1, help='Label to constrain to.', type=int) parser.add_argument('-encoder_file', default=paths.state_file('encoder'), help='Snapshot state file.', type=str) parser.add_argument('-decoder_file', default=paths.state_file('decoder'), help='Snapshot state file.', type=str) parser.add_argument('-reconstruction_file', default=paths.results_file('reconstructions'), help='Reconstructions file.', type=str) parser.add_argument('-interpolation_file', default=paths.results_file('interpolations'), help='Interpolation file.', type=str) parser.add_argument('-random_file', default=paths.results_file('random'), help='Reconstructions file.', type=str) parser.add_argument('-log_file', default=paths.log_file('auto_encoder'), help='Log file.', type=str) parser.add_argument('-batch_size', default=64, help='Batch size.', type=int) parser.add_argument('-latent_space_size', default=10, help='Size of latent space.', type=int) parser.add_argument('-epochs', default=20, help='Number of epochs.', type=int) parser.add_argument('-no_gpu', dest='use_gpu', action='store_false') parser.add_argument('-base_lr', default=0.01, type=float, help='Base learning rate.') parser.add_argument('-base_lr_decay', default=0.9, type=float, help='Base learning rate.') parser.add_argument('-results_file', default='', help='Results file for evaluation.', type=str) parser.add_argument('-training_file', default=paths.results_file('auto_encoder_training'), help='Training statistics file.', type=str) parser.add_argument('-testing_file', default=paths.results_file('auto_encoder_testing'), help='Testing statistics file.', type=str) parser.add_argument('-error_file', default=paths.image_file('auto_encoder_error'), help='Error plot file.', type=str) parser.add_argument('-beta', default=1, help='Weight of KLD.', type=float) parser.add_argument('-weight_decay', default=0.0001, help='Weight decay importance.', type=float) parser.add_argument('-absolute_error', default=False, action='store_true', help='Use absolute loss.') # Some network parameters. parser.add_argument('-network_architecture', default='standard', help='Classifier architecture to use.', type=str) parser.add_argument('-network_activation', default='relu', help='Activation function to use.', type=str) parser.add_argument('-network_no_batch_normalization', default=False, help='Do not use batch normalization.', action='store_true') parser.add_argument('-network_channels', default=16, help='Channels of first convolutional layer, afterwards channels are doubled.', type=int) parser.add_argument('-network_dropout', default=False, action='store_true', help='Whether to use dropout.') parser.add_argument('-network_units', default='1024,1024,1024,1024', help='Units for MLP.') return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser( description='Attack decoder and classifier.') parser.add_argument('-database_file', default=paths.database_file(), help='HDF5 file containing font prototype images.', type=str) parser.add_argument('-test_theta_file', default=paths.test_theta_file(), help='HDF5 file for thetas.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing codes.', type=str) parser.add_argument( '-perturbations_file', default=paths.results_file('decoder/perturbations'), help='HDF5 file containing perturbations.', type=str) parser.add_argument( '-perturbation_images_file', default=paths.results_file('decoder/perturbation_images'), help='HDF5 file for perturbation images.', type=str) parser.add_argument('-log_file', default=paths.log_file('decoder/attacks'), help='Log file.', type=str) parser.add_argument('-batch_size', default=128, help='Batch size of attack.', type=int) parser.add_argument('-no_gpu', dest='use_gpu', action='store_false') return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser(description='Attack decoder and classifier.') parser.add_argument('-database_file', default=paths.database_file(), help='HDF5 file containing font prototype images.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing codes.', type=str) parser.add_argument('-test_theta_file', default=paths.test_theta_file(), help='HDF5 file containing transformations.', type=str) parser.add_argument('-classifier_file', default=paths.state_file('classifier'), help='Snapshot state file of classifier.', type=str) parser.add_argument('-accuracy_file', default=paths.results_file('decoder/accuracy'), help='Correctly classified test samples of classifier.', type=str) parser.add_argument('-perturbations_file', default=paths.results_file('decoder/perturbations'), help='HDF5 file containing perturbations.', type=str) parser.add_argument('-success_file', default=paths.results_file('decoder/success'), help='HDF5 file containing perturbations.', type=str) parser.add_argument('-log_file', default=paths.log_file('decoder/attacks'), help='Log file.', type=str) parser.add_argument('-attack', default='UntargetedBatchL2ClippedGradientDescent', help='Attack to try.', type=str) parser.add_argument('-objective', default='UntargetedF6', help='Objective to use.', type=str) parser.add_argument('-max_attempts', default=1, help='Maximum number of attempts per attack.', type=int) parser.add_argument('-max_samples', default=20*128, help='How many samples from the test set to attack.', type=int) parser.add_argument('-batch_size', default=128, help='Batch size of attack.', type=int) parser.add_argument('-epsilon', default=0.1, help='Epsilon allowed for attacks.', type=float) parser.add_argument('-c_0', default=0., help='Weight of norm.', type=float) parser.add_argument('-c_1', default=0.1, help='Weight of bound, if not enforced through clipping or reparameterization.', type=float) parser.add_argument('-c_2', default=0.5, help='Weight of objective.', type=float) parser.add_argument('-max_iterations', default=100, help='Number of iterations for attack.', type=int) parser.add_argument('-max_projections', default=5, help='Number of projections for alternating projection.', type=int) parser.add_argument('-base_lr', default=0.005, help='Learning rate for attack.', type=float) parser.add_argument('-no_gpu', dest='use_gpu', action='store_false') parser.add_argument('-no_label_leaking', default=False, dest='no_label_leaking', action='store_true') parser.add_argument('-on_manifold', default=False, dest='on_manifold', action='store_true') parser.add_argument('-initialize_zero', default=False, action='store_true', help='Initialize attack at zero.') # Some network parameters. parser.add_argument('-network_architecture', default='standard', help='Classifier architecture to use.', type=str) parser.add_argument('-network_activation', default='relu', help='Activation function to use.', type=str) parser.add_argument('-network_no_batch_normalization', default=False, help='Do not use batch normalization.', action='store_true') parser.add_argument('-network_channels', default=16, help='Channels of first convolutional layer, afterwards channels are doubled.', type=int) parser.add_argument('-network_dropout', default=False, action='store_true', help='Whether to use dropout.') parser.add_argument('-network_units', default='1024,1024,1024,1024', help='Units for MLP.') return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser( description='Inspect transformed images.') parser.add_argument('-database_file', default=paths.database_file(), type=str) parser.add_argument('-codes_file', default=paths.codes_file(), type=str) parser.add_argument('-theta_file', default=paths.theta_file(), type=str) parser.add_argument('-images_file', default=paths.images_file(), type=str) parser.add_argument('-train_codes_file', default=paths.train_codes_file(), type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), type=str) parser.add_argument('-train_theta_file', default=paths.train_theta_file(), type=str) parser.add_argument('-test_theta_file', default=paths.test_theta_file(), type=str) parser.add_argument('-train_images_file', default=paths.train_images_file(), type=str) parser.add_argument('-test_images_file', default=paths.test_images_file(), type=str) return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser(description='Detect attacks on classifier.') parser.add_argument('-mode', default='svd', help='Mode.', type=str) parser.add_argument('-database_file', default=paths.database_file(), help='HDF5 file containing font prototype images.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file codes dataset.', type=str) parser.add_argument('-test_theta_file', default=paths.test_theta_file(), help='HDF5 file containing transformations.', type=str) parser.add_argument('-train_images_file', default=paths.train_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-perturbations_file', default=paths.results_file('classifier/perturbations'), help='HDF5 file containing perturbations.', type=str) parser.add_argument('-success_file', default=paths.results_file('classifier/success'), help='HDF5 file containing success indicators.', type=str) parser.add_argument('-accuracy_file', default=paths.results_file('classifier/accuracy'), help='HDF5 file containing accuracy indicators.', type=str) parser.add_argument('-batch_size', default=64, help='Batch size.', type=int) parser.add_argument('-no_gpu', dest='use_gpu', action='store_false') parser.add_argument('-pre_pca', default=20, help='PCA dimensionality reduction ebfore NN.', type=int) parser.add_argument('-n_nearest_neighbors', default=50, help='Number of NNs to consider.', type=int) parser.add_argument('-n_pca', default=10, help='Number of NNs to consider.', type=int) parser.add_argument('-n_fit', default=100000, help='Training images to fit.', type=int) parser.add_argument('-plot_directory', default=paths.experiment_dir('classifier/detection'), help='Plot directory.', type=str) parser.add_argument('-max_samples', default=1000, help='Number of samples.', type=int) # Some decoder parameters. parser.add_argument('-decoder_files', default=paths.state_file('decoder'), help='Decoder files.', type=str) parser.add_argument('-latent_space_size', default=10, help='Size of latent space.', type=int) parser.add_argument('-decoder_architecture', default='standard', help='Architecture to use.', type=str) parser.add_argument('-decoder_activation', default='relu', help='Activation function to use.', type=str) parser.add_argument('-decoder_no_batch_normalization', default=False, help='Do not use batch normalization.', action='store_true') parser.add_argument('-decoder_channels', default=16, help='Channels of first convolutional layer, afterwards channels are doubled.', type=int) parser.add_argument('-decoder_dropout', default=False, action='store_true', help='Whether to use dropout.') parser.add_argument('-decoder_units', default='1024,1024,1024,1024', help='Units for MLP.') return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser(description='Test attacks on classifier.') parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-train_images_file', default=paths.train_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-test_theta_file', default=paths.results_file('test_theta'), help='HDF5 file containing dataset.', type=str) parser.add_argument('-train_theta_file', default=paths.results_file('train_theta'), help='HDF5 file containing dataset.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing labels.', type=str) parser.add_argument('-decoder_files', default=paths.state_file('decoder'), help='Decoder files.', type=str) parser.add_argument('-label_index', default=2, help='Column index in label file.', type=int) parser.add_argument('-accuracy_file', default=paths.results_file('learned_decoder/accuracy'), help='Correctly classified test samples of classifier.', type=str) parser.add_argument('-perturbations_file', default=paths.results_file('learned_decoder/perturbations'), help='HDF5 file containing perturbations.', type=str) parser.add_argument('-success_file', default=paths.results_file('learned_decoder/success'), help='HDF5 file indicating attack success.', type=str) parser.add_argument('-plot_directory', default=paths.experiment_dir('learned_decoder'), help='Path to PNG plot file for success rate.', type=str) parser.add_argument('-results_file', default='', help='Path to pickled results file.', type=str) parser.add_argument('-batch_size', default=128, help='Batch size of attack.', type=int) parser.add_argument('-plot_manifolds', default=False, action='store_true', help='Whether to plot manifolds.') parser.add_argument('-no_gpu', dest='use_gpu', action='store_false') parser.add_argument('-bound', default=2, help='Bound to consider for samples in latent space.', type=float) # Some decoder parameters. parser.add_argument('-latent_space_size', default=10, help='Size of latent space.', type=int) parser.add_argument('-decoder_architecture', default='standard', help='Architecture to use.', type=str) parser.add_argument('-decoder_activation', default='relu', help='Activation function to use.', type=str) parser.add_argument('-decoder_no_batch_normalization', default=False, help='Do not use batch normalization.', action='store_true') parser.add_argument('-decoder_channels', default=16, help='Channels of first convolutional layer, afterwards channels are doubled.', type=int) parser.add_argument('-decoder_dropout', default=False, action='store_true', help='Whether to use dropout.') parser.add_argument('-decoder_units', default='1024,1024,1024,1024', help='Units for MLP.') return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser(description='Train classifier.') parser.add_argument('-train_images_file', default=paths.train_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-train_codes_file', default=paths.train_codes_file(), help='HDF5 file containing codes.', type=str) parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing codes.', type=str) parser.add_argument('-state_file', default=paths.state_file('classifier'), help='Snapshot state file.', type=str) parser.add_argument('-log_file', default=paths.log_file('classifier'), help='Log file.', type=str) parser.add_argument('-training_file', default=paths.results_file('training'), help='Training statistics file.', type=str) parser.add_argument('-testing_file', default=paths.results_file('testing'), help='Testing statistics file.', type=str) parser.add_argument('-loss_file', default=paths.image_file('loss'), help='Loss plot file.', type=str) parser.add_argument('-error_file', default=paths.image_file('error'), help='Error plot file.', type=str) parser.add_argument('-gradient_file', default=paths.image_file('gradient'), help='Gradient plot file.', type=str) parser.add_argument('-label_index', default=2, help='Label index.', type=int) parser.add_argument('-training_samples', default=-1, help='Number of samples used for training.', type=int) parser.add_argument('-validation_samples', default=0, help='Number of samples for validation.', type=int) parser.add_argument('-test_samples', default=-1, help='Number of samples for validation.', type=int) parser.add_argument('-early_stopping', default=False, action='store_true', help='Use early stopping.') parser.add_argument( '-random_samples', default=False, action='store_true', help='Randomize the subsampling of the training set.') parser.add_argument('-batch_size', default=64, help='Batch size.', type=int) parser.add_argument('-epochs', default=10, help='Number of epochs.', type=int) parser.add_argument('-weight_decay', default=0.0001, help='Weight decay importance.', type=float) parser.add_argument('-logit_decay', default=0, help='Logit decay importance.', type=float) parser.add_argument('-no_gpu', dest='use_gpu', action='store_false') parser.add_argument('-skip', default=5, help='Verbosity in iterations.', type=int) parser.add_argument('-lr', default=0.01, type=float, help='Base learning rate.') parser.add_argument('-lr_decay', default=0.9, type=float, help='Learning rate decay.') parser.add_argument('-results_file', default='', help='Results file for evaluation.', type=str) parser.add_argument('-debug_directory', default='', help='Debug directory.', type=str) # Some network parameters. parser.add_argument('-network_architecture', default='standard', help='Classifier architecture to use.', type=str) parser.add_argument('-network_activation', default='relu', help='Activation function to use.', type=str) parser.add_argument('-network_no_batch_normalization', default=False, help='Do not use batch normalization.', action='store_true') parser.add_argument( '-network_channels', default=16, help= 'Channels of first convolutional layer, afterwards channels are doubled.', type=int) parser.add_argument('-network_dropout', default=False, action='store_true', help='Whether to use dropout.') parser.add_argument('-network_units', default='1024,1024,1024,1024', help='Units for MLP.') return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser( description='Attack decoder and classifier.') parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing codes.', type=str) parser.add_argument('-label_index', default=2, help='Label index.', type=int) parser.add_argument('-classifier_file', default=paths.state_file('classifier'), help='Snapshot state file of classifier.', type=str) parser.add_argument('-output_directory', default=paths.experiment_dir('output'), help='Output directory.', type=str) parser.add_argument('-log_file', default=paths.log_file('learned_decoder/attacks'), help='Log file.', type=str) parser.add_argument('-objective', default='UntargetedF0', help='Objective to use.', type=str) parser.add_argument( '-max_samples', default=10, help='How many samples from the test set to attack.', type=int) parser.add_argument('-no_gpu', dest='use_gpu', action='store_false') parser.add_argument('-N_theta', default=6, help='Numer of transformations.', type=int) parser.add_argument('-translation_x', default='-0.1,0.1', type=str, help='Minimum and maximum translation in x.') parser.add_argument('-translation_y', default='-0.1,0.1', type=str, help='Minimum and maximum translation in y') parser.add_argument('-shear_x', default='-0.25,0.25', type=str, help='Minimum and maximum shear in x.') parser.add_argument('-shear_y', default='-0.25,0.25', type=str, help='Minimum and maximum shear in y.') parser.add_argument('-scale', default='0.95,1.05', type=str, help='Minimum and maximum scale.') parser.add_argument('-rotation', default='%g,%g' % (-math.pi / 4, math.pi / 4), type=str, help='Minimum and maximum rotation.') parser.add_argument('-color', default=0.5, help='Minimum color value, maximum is 1.', type=float) # Some network parameters. parser.add_argument('-network_architecture', default='standard', help='Classifier architecture to use.', type=str) parser.add_argument('-network_activation', default='relu', help='Activation function to use.', type=str) parser.add_argument('-network_no_batch_normalization', default=False, help='Do not use batch normalization.', action='store_true') parser.add_argument( '-network_channels', default=16, help= 'Channels of first convolutional layer, afterwards channels are doubled.', type=int) parser.add_argument('-network_dropout', default=False, action='store_true', help='Whether to use dropout.') parser.add_argument('-network_units', default='1024,1024,1024,1024', help='Units for MLP.') return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser(description='Test auto encoder.') parser.add_argument('-train_images_file', default=paths.train_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-train_codes_file', default=paths.train_codes_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-train_theta_file', default=paths.results_file('train_theta'), help='HDF5 file for codes.', type=str) parser.add_argument('-test_theta_file', default=paths.results_file('test_theta'), help='HDF5 file for codes.', type=str) parser.add_argument('-label_index', default=2, help='Label index.', type=int) parser.add_argument('-label', default=-1, help='Label to constrain to.', type=int) parser.add_argument('-encoder_file', default=paths.state_file('encoder'), help='Snapshot state file.', type=str) parser.add_argument('-decoder_file', default=paths.state_file('decoder'), help='Snapshot state file.', type=str) parser.add_argument('-reconstruction_file', default=paths.results_file('reconstructions'), help='Reconstructions file.', type=str) parser.add_argument('-train_reconstruction_file', default='', help='Reconstructions file.', type=str) parser.add_argument('-random_file', default=paths.results_file('random'), help='Reconstructions file.', type=str) parser.add_argument('-interpolation_file', default=paths.results_file('interpolation'), help='Interpolations file.', type=str) parser.add_argument('-batch_size', default=64, help='Batch size.', type=int) parser.add_argument('-latent_space_size', default=10, help='Size of latent space.', type=int) parser.add_argument('-no_gpu', dest='use_gpu', action='store_false') parser.add_argument('-results_file', default='', help='Results file for evaluation.', type=str) parser.add_argument('-output_directory', default='', help='Output directory for plots.', type=str) parser.add_argument('-log_file', default=paths.log_file('test_auto_encoder'), help='Log file.', type=str) # Some network parameters. parser.add_argument('-network_architecture', default='standard', help='Architecture type.') parser.add_argument('-network_activation', default='relu', help='Activation function to use.', type=str) parser.add_argument('-network_no_batch_normalization', default=False, help='Do not use batch normalization.', action='store_true') parser.add_argument( '-network_channels', default=16, help= 'Channels of first convolutional layer, afterwards channels are doubled.', type=int) parser.add_argument('-network_dropout', default=False, action='store_true', help='Whether to use dropout.') parser.add_argument('-network_units', default='1024,1024,1024,1024', help='Units for MLP.') return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser(description='Train classifier.') parser.add_argument('-train_images_file', default=paths.train_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-train_codes_file', default=paths.train_codes_file(), help='HDF5 file containing codes.', type=str) parser.add_argument('-train_theta_file', default=paths.results_file('train_theta'), help='HDF5 file containing transformations.', type=str) parser.add_argument('-test_theta_file', default=paths.results_file('test_theta'), help='HDF5 file containing transformations.', type=str) parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing codes.', type=str) parser.add_argument('-decoder_files', default=paths.state_file('decoder'), help='Decoder files.', type=str) parser.add_argument('-latent_space_size', default=10, help='Size of latent space.', type=int) parser.add_argument('-label_index', default=2, help='Column index in label file.', type=int) parser.add_argument( '-state_file', default=paths.state_file('robust_manifold_classifier'), help='Snapshot state file.', type=str) parser.add_argument( '-log_file', default=paths.log_file('robust_manifold_classifier'), help='Log file.', type=str) parser.add_argument( '-training_file', default=paths.results_file('robust_manifold_training'), help='Training statistics file.', type=str) parser.add_argument( '-testing_file', default=paths.results_file('robust_manifold_testing'), help='Testing statistics file.', type=str) parser.add_argument('-loss_file', default=paths.image_file('loss'), help='Loss plot file.', type=str) parser.add_argument('-error_file', default=paths.image_file('error'), help='Error plot file.', type=str) parser.add_argument('-success_file', default=paths.image_file('robust_success'), help='Success rate plot file.', type=str) parser.add_argument('-gradient_file', default='', help='Gradient plot file.', type=str) parser.add_argument( '-random_samples', default=False, action='store_true', help='Randomize the subsampling of the training set.') parser.add_argument('-training_samples', default=-1, help='Number of samples used for training.', type=int) parser.add_argument('-test_samples', default=-1, help='Number of samples for validation.', type=int) parser.add_argument('-validation_samples', default=0, help='Number of samples for validation.', type=int) parser.add_argument('-early_stopping', default=False, action='store_true', help='Use early stopping.') parser.add_argument('-attack_samples', default=1000, help='Samples to attack.', type=int) parser.add_argument('-batch_size', default=64, help='Batch size.', type=int) parser.add_argument('-epochs', default=10, help='Number of epochs.', type=int) parser.add_argument('-weight_decay', default=0.0001, help='Weight decay importance.', type=float) parser.add_argument('-logit_decay', default=0, help='Logit decay importance.', type=float) parser.add_argument('-no_gpu', dest='use_gpu', action='store_false') parser.add_argument('-skip', default=5, help='Verbosity in iterations.', type=int) parser.add_argument('-lr', default=0.005, type=float, help='Base learning rate.') parser.add_argument('-lr_decay', default=0.9, type=float, help='Learning rate decay.') parser.add_argument('-results_file', default='', help='Results file for evaluation.', type=str) parser.add_argument( '-bound', default=2, help= 'Bound used to define "safe" latent codes to compute adversarial examples on.', type=float) parser.add_argument('-debug_directory', default='', help='Debug directory.', type=str) # Some network parameters. parser.add_argument('-network_architecture', default='standard', help='Classifier architecture to use.', type=str) parser.add_argument('-network_activation', default='relu', help='Activation function to use.', type=str) parser.add_argument('-network_no_batch_normalization', default=False, help='Do not use batch normalization.', action='store_true') parser.add_argument( '-network_channels', default=16, help= 'Channels of first convolutional layer, afterwards channels are doubled.', type=int) parser.add_argument('-network_dropout', default=False, action='store_true', help='Whether to use dropout.') parser.add_argument('-network_units', default='1024,1024,1024,1024', help='Units for MLP.') # Decoder parameters. parser.add_argument('-decoder_architecture', default='standard', help='Architecture to use.', type=str) parser.add_argument('-decoder_activation', default='relu', help='Activation function to use.', type=str) parser.add_argument('-decoder_no_batch_normalization', default=False, help='Do not use batch normalization.', action='store_true') parser.add_argument( '-decoder_channels', default=16, help= 'Channels of first convolutional layer, afterwards channels are doubled.', type=int) parser.add_argument('-decoder_dropout', default=False, action='store_true', help='Whether to use dropout.') parser.add_argument('-decoder_units', default='1024,1024,1024,1024', help='Units for MLP.') # Attack parameters. parser.add_argument('-attack', default='UntargetedBatchL2ClippedGradientDescent', help='Attack to try.', type=str) parser.add_argument('-objective', default='UntargetedF6', help='Objective to use.', type=str) parser.add_argument('-epsilon', default=1, help='Epsilon allowed for attacks.', type=float) parser.add_argument('-c_0', default=0., help='Weight of norm.', type=float) parser.add_argument( '-c_1', default=0.1, help= 'Weight of bound, if not enforced through clipping or reparameterization.', type=float) parser.add_argument('-c_2', default=0.5, help='Weight of objective.', type=float) parser.add_argument('-max_iterations', default=10, help='Number of iterations for attack.', type=int) parser.add_argument( '-max_projections', default=5, help='Number of projections for alternating projection.', type=int) parser.add_argument('-base_lr', default=0.005, help='Learning rate for attack.', type=float) parser.add_argument('-decoder_epsilon', default=1, help='Epsilon allowed for attacks.', type=float) parser.add_argument('-decoder_c_0', default=0., help='Weight of norm.', type=float) parser.add_argument( '-decoder_c_1', default=0.1, help= 'Weight of bound, if not enforced through clipping or reparameterization.', type=float) parser.add_argument('-decoder_c_2', default=0.5, help='Weight of objective.', type=float) parser.add_argument('-decoder_max_iterations', default=10, help='Number of iterations for attack.', type=int) parser.add_argument( '-decoder_max_projections', default=5, help='Number of projections for alternating projection.', type=int) parser.add_argument('-decoder_base_lr', default=0.005, help='Learning rate for attack.', type=float) parser.add_argument('-verbose', action='store_true', default=False, help='Verbose attacks.') parser.add_argument('-anneal_epochs', default=0, help='Anneal iterations in the first epochs.', type=int) # Variants. parser.add_argument('-full_variant', default=False, action='store_true', help='100% variant.') parser.add_argument('-safe', default=False, action='store_true', help='Save variant.') parser.add_argument('-training_mode', default=False, action='store_true', help='Training mode variant for attack.') return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser(description='Test classifier.') parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing codes.', type=str) parser.add_argument('-state_file', default=paths.state_file('classifier'), help='Snapshot state file.', type=str) parser.add_argument( '-accuracy_file', default=paths.results_file('classifier/accuracy'), help='Correctly classified test samples of classifier.', type=str) parser.add_argument('-label_index', default=2, help='Label index.', type=int) parser.add_argument('-results_file', default='', help='Results file for evaluation.', type=str) parser.add_argument('-batch_size', default=64, help='Batch size.', type=int) parser.add_argument('-no_gpu', dest='use_gpu', action='store_false') parser.add_argument('-log_file', default=paths.log_file('test_classifier'), help='Log file.', type=str) # Some network parameters. parser.add_argument('-network_architecture', default='standard', help='Classifier architecture to use.', type=str) parser.add_argument('-network_activation', default='relu', help='Activation function to use.', type=str) parser.add_argument('-network_no_batch_normalization', default=False, help='Do not use batch normalization.', action='store_true') parser.add_argument( '-network_channels', default=16, help= 'Channels of first convolutional layer, afterwards channels are doubled.', type=int) parser.add_argument('-network_dropout', default=False, action='store_true', help='Whether to use dropout.') parser.add_argument('-network_units', default='1024,1024,1024,1024', help='Units for MLP.') return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser( description='Summarize dataset including plots and statistics.') parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing codes.', type=str) parser.add_argument('-test_theta_file', default=paths.test_theta_file(), help='HDF5 file containing transformations.', type=str) parser.add_argument( '-skip', default=10, help='Number of samples to skip for visualization.', type=int) parser.add_argument('-pca_images_file', default=paths.image_file('data/images_pca'), help='File for PCA visualization.', type=str) parser.add_argument('-umap_images_file', default=paths.image_file('data/images_umap'), help='File for Umap visualization.', type=str) parser.add_argument('-lle_images_file', default=None, help='File for PLLE visualization.', type=str) parser.add_argument('-mlle_images_file', default=None, help='File for MLLE visualization.', type=str) parser.add_argument('-mds_images_file', default=paths.image_file('data/images_mds'), help='File for MDS visualization.', type=str) parser.add_argument('-tsne_images_file', default=paths.image_file('data/images_tsne'), help='File for t-SNE visualization.', type=str) parser.add_argument('-pca_theta_file', default=paths.image_file('data/theta_pca'), help='File for PCA visualization.', type=str) parser.add_argument('-umap_theta_file', default=paths.image_file('data/theta_umap'), help='File for Umap visualization.', type=str) parser.add_argument('-lle_theta_file', default=None, help='File for PLLE visualization.', type=str) parser.add_argument('-mlle_theta_file', default=None, help='File for MLLE visualization.', type=str) parser.add_argument('-mds_theta_file', default=paths.image_file('data/theta_mds'), help='File for MDS visualization.', type=str) parser.add_argument('-tsne_theta_file', default=paths.image_file('data/theta_tsne'), help='File for t-SNE visualization.', type=str) return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser( description='Test attacks on classifier.') parser.add_argument('-classifier_file', default=paths.state_file('classifier'), help='Snapshot state file of classifier.', type=str) parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-label_index', default=2, help='Column index in label file.', type=int) parser.add_argument( '-perturbations_file', default=paths.results_file('classifier/perturbations'), help='HDF5 file containing perturbations.', type=str) parser.add_argument('-success_file', default=paths.results_file('classifier/success'), help='HDF5 file indicating attack success.', type=str) parser.add_argument( '-probabilities_file', default=paths.results_file('classifier/probabilities'), help='HDF5 file containing attack probabilities.') parser.add_argument('-results_file', default='', help='Path to pickled results file.', type=str) parser.add_argument('-plot_directory', default=paths.experiment_dir('classifier'), help='Path to PNG plot file for success rate.', type=str) parser.add_argument('-plot_manifolds', default=False, action='store_true', help='Whether to plot manifolds.') parser.add_argument('-no_gpu', dest='use_gpu', action='store_false') parser.add_argument('-batch_size', default=128, help='Batch size of attack.', type=int) # Some network parameters. parser.add_argument('-network_architecture', default='standard', help='Classifier architecture to use.', type=str) parser.add_argument('-network_activation', default='relu', help='Activation function to use.', type=str) parser.add_argument('-network_no_batch_normalization', default=False, help='Do not use batch normalization.', action='store_true') parser.add_argument( '-network_channels', default=16, help= 'Channels of first convolutional layer, afterwards channels are doubled.', type=int) parser.add_argument('-network_dropout', default=False, action='store_true', help='Whether to use dropout.') parser.add_argument('-network_units', default='1024,1024,1024,1024', help='Units for MLP.') return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser( description='Test attacks on classifier.') parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-train_images_file', default=paths.train_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-test_theta_file', default=paths.test_theta_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-train_theta_file', default=paths.train_theta_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing labels.', type=str) parser.add_argument('-label_index', default=2, help='Column index in label file.', type=int) parser.add_argument( '-accuracy_file', default=paths.results_file('classifier/accuracy'), help='Correctly classified test samples of classifier.', type=str) parser.add_argument( '-perturbations_file', default=paths.results_file('classifier/perturbations'), help='HDF5 file containing perturbations.', type=str) parser.add_argument('-success_file', default=paths.results_file('classifier/success'), help='HDF5 file indicating attack success.', type=str) parser.add_argument('-results_file', default='', help='Path to pickled results file.', type=str) parser.add_argument('-plot_directory', default=paths.experiment_dir('classifier'), help='Path to PNG plot file for success rate.', type=str) parser.add_argument('-plot_manifolds', default=False, action='store_true', help='Whether to plot manifolds.') parser.add_argument('-latent', default=False, action='store_true', help='Latent statistics.') return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser( description='Visualize attacks on decoder and classifier.') parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing images.', type=str) parser.add_argument('-test_theta_file', default=paths.results_file('test_theta'), help='HDF5 file containing transformations.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing codes.', type=str) parser.add_argument('-label_index', default=2, help='Label index.', type=int) parser.add_argument('-decoder_files', default=paths.state_file('decoder'), help='Decoder state file.', type=str) parser.add_argument('-classifier_file', default=paths.state_file('classifier'), help='Snapshot state file of classifier.', type=str) parser.add_argument( '-perturbations_file', default=paths.results_file('learned_decoder/perturbations'), help='HDF5 file containing perturbations.', type=str) parser.add_argument( '-success_file', default=paths.results_file('learned_decoder/success'), help='HDF5 file containing perturbations.', type=str) parser.add_argument( '-accuracy_file', default=paths.results_file('learned_decoder/accuracy'), help='Correctly classified test samples of classifier.', type=str) parser.add_argument( '-output_directory', default=paths.experiment_dir('decoder/perturbations'), help='Directory to store visualizations.', type=str) parser.add_argument('-batch_size', default=128, help='Batch size of attack.', type=int) parser.add_argument('-no_gpu', dest='use_gpu', action='store_false') # Some network parameters. parser.add_argument('-network_architecture', default='standard', help='Classifier architecture to use.', type=str) parser.add_argument('-network_activation', default='relu', help='Activation function to use.', type=str) parser.add_argument('-network_no_batch_normalization', default=False, help='Do not use batch normalization.', action='store_true') parser.add_argument( '-network_channels', default=16, help= 'Channels of first convolutional layer, afterwards channels are doubled.', type=int) parser.add_argument('-network_dropout', default=False, action='store_true', help='Whether to use dropout.') parser.add_argument('-network_units', default='1024,1024,1024,1024', help='Units for MLP.') # Some decoder parameters. parser.add_argument('-latent_space_size', default=10, help='Size of latent space.', type=int) parser.add_argument('-decoder_architecture', default='standard', help='Architecture to use.', type=str) parser.add_argument('-decoder_activation', default='relu', help='Activation function to use.', type=str) parser.add_argument('-decoder_no_batch_normalization', default=False, help='Do not use batch normalization.', action='store_true') parser.add_argument( '-decoder_channels', default=16, help= 'Channels of first convolutional layer, afterwards channels are doubled.', type=int) parser.add_argument('-decoder_units', default='1024,1024,1024,1024', help='Units for MLP.') return parser
def __init__(self, args=None): """ Constructor. """ 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() assert self.args.suffix in ['Hard', 'Moderate', 'Easy'] paths.set_globals(experiment=self.experiment(), characters='ABCDEFGHIJ', fonts=1000, transformations=6, size=28, suffix=self.args.suffix) self.train_images_file = paths.train_images_file() self.train_codes_file = paths.train_codes_file() self.test_images_file = paths.test_images_file() self.test_codes_file = paths.test_codes_file() self.label_index = 2 self.results = dict() # self.betas = [ # 7, # 0 - latent space size 10 # 7, # 1 # 7.5, # 2 # 7, # 3 # 7, # 4 # 7, # 5 # 7, # 6 # 7, # 7 # 13, # 8 # 9.5, # 9 # 20, # -1 - latent space size 20 # ] # self.betas = [ # 3, # 3, # 3, # 3, # 3, # 3, # 3, # 3, # 3, # 3, # 3 # ] # # # loss should roughly be half of reconstruction loss # self.gammas = [ # 1, # 1, # 1, # 1, # 1, # 1, # 1, # 1, # 1, # 1, # 1, # ] # self.classifier_channels = 32 # self.network_channels = 128 self.classifier_channels = 64 self.network_channels = 64 self.training_parameters = [ '-base_lr=0.005', '-weight_decay=0.0001', '-base_lr_decay=0.9', '-batch_size=100', '-absolute_error', ] self.classifier_parameters = [ '-classifier_architecture=standard', '-classifier_activation=relu', '-classifier_channels=%d' % self.classifier_channels, '-classifier_units=1024,1024,1024,1024' ] self.network_parameters = [ '-network_architecture=standard', '-network_activation=relu', '-network_channels=%d' % self.network_channels, '-network_units=1024,1024,1024,1024', ] log('-- ' + self.__class__.__name__) for key in vars(self.args): log('[Experiment] %s=%s' % (key, str(getattr(self.args, key))))
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser(description='Train classifier.') parser.add_argument('-train_images_file', default=paths.train_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-train_codes_file', default=paths.train_codes_file(), help='HDF5 file containing codes.', type=str) parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file containing dataset.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing codes.', type=str) parser.add_argument('-state_file', default=paths.state_file('stn_classifier'), help='Snapshot state file.', type=str) parser.add_argument('-log_file', default=paths.log_file('stn_classifier'), help='Log file.', type=str) parser.add_argument('-training_file', default=paths.results_file('stn_training'), help='Training statistics file.', type=str) parser.add_argument('-testing_file', default=paths.results_file('stn_testing'), help='Testing statistics file.', type=str) parser.add_argument('-loss_file', default=paths.image_file('loss'), help='Loss plot file.', type=str) parser.add_argument('-error_file', default=paths.image_file('error'), help='Error plot file.', type=str) parser.add_argument('-gradient_file', default='', help='Gradient plot file.', type=str) parser.add_argument('-label_index', default=2, help='Label index.', type=int) parser.add_argument( '-random_samples', default=False, action='store_true', help='Randomize the subsampling of the training set.') parser.add_argument('-training_samples', default=-1, help='Number of samples used for training.', type=int) parser.add_argument('-test_samples', default=-1, help='Number of samples for validation.', type=int) parser.add_argument('-validation_samples', default=0, help='Number of samples for validation.', type=int) parser.add_argument('-early_stopping', default=False, action='store_true', help='Use early stopping.') parser.add_argument('-batch_size', default=64, help='Batch size.', type=int) parser.add_argument('-epochs', default=10, help='Number of epochs.', type=int) parser.add_argument('-weight_decay', default=0.0001, help='Weight decay importance.', type=float) parser.add_argument('-logit_decay', default=0, help='Logit decay importance.', type=float) parser.add_argument('-no_gpu', dest='use_gpu', action='store_false') parser.add_argument('-skip', default=5, help='Verbosity in iterations.', type=int) parser.add_argument('-lr', default=0.005, type=float, help='Base learning rate.') parser.add_argument('-lr_decay', default=0.9, type=float, help='Learning rate decay.') parser.add_argument('-results_file', default='', help='Results file for evaluation.', type=str) parser.add_argument('-debug_directory', default='', help='Debug directory.', type=str) # Some network parameters. parser.add_argument('-network_architecture', default='standard', help='Classifier architecture to use.', type=str) parser.add_argument('-network_activation', default='relu', help='Activation function to use.', type=str) parser.add_argument('-network_no_batch_normalization', default=False, help='Do not use batch normalization.', action='store_true') parser.add_argument( '-network_channels', default=16, help= 'Channels of first convolutional layer, afterwards channels are doubled.', type=int) parser.add_argument('-network_dropout', default=False, action='store_true', help='Whether to use dropout.') parser.add_argument('-network_units', default='1024,1024,1024,1024', help='Units for MLP.') # Attack parameters. parser.add_argument('-epsilon', default=1, help='Epsilon allowed for attacks.', type=float) parser.add_argument('-max_iterations', default=10, help='Number of iterations for attack.', type=int) parser.add_argument('-N_theta', default=6, help='Numer of transformations.', type=int) parser.add_argument('-translation_x', default='-0.2,0.2', type=str, help='Minimum and maximum translation in x.') parser.add_argument('-translation_y', default='-0.2,0.2', type=str, help='Minimum and maximum translation in y') parser.add_argument('-shear_x', default='-0.5,0.5', type=str, help='Minimum and maximum shear in x.') parser.add_argument('-shear_y', default='-0.5,0.5', type=str, help='Minimum and maximum shear in y.') parser.add_argument('-scale', default='0.9,1.1', type=str, help='Minimum and maximum scale.') parser.add_argument('-rotation', default='%g,%g' % (-math.pi / 4, math.pi / 4), type=str, help='Minimum and maximum rotation.') parser.add_argument('-color', default=0.5, help='Minimum color value, maximum is 1.', type=float) # Variants. parser.add_argument('-norm', default='inf', help='Norm to use.', type=float) parser.add_argument('-full_variant', default=False, action='store_true', help='100% variant.') parser.add_argument('-strong_variant', default=False, action='store_true', help='Strong data augmentation variant.') return parser
def get_parser(self): """ Get parser. :return: parser :rtype: argparse.ArgumentParser """ parser = argparse.ArgumentParser( description='Attack decoder and classifier.') parser.add_argument('-test_images_file', default=paths.test_images_file(), help='HDF5 file with test images.', type=str) parser.add_argument('-test_codes_file', default=paths.test_codes_file(), help='HDF5 file containing test codes.', type=str) parser.add_argument('-label_index', default=2, help='Label index.', type=int) parser.add_argument('-decoder_files', default=paths.state_file('decoder'), help='Decoder state file.', type=str) parser.add_argument( '-perturbations_file', default=paths.results_file('decoder/perturbations'), help='HDF5 file containing perturbations.', type=str) parser.add_argument( '-perturbation_images_file', default=paths.results_file('decoder/perturbation_images'), help='HDF5 file for perturbation images.', type=str) parser.add_argument('-log_file', default=paths.log_file('decoder/attacks'), help='Log file.', type=str) parser.add_argument('-batch_size', default=128, help='Batch size of attack.', type=int) parser.add_argument('-no_gpu', dest='use_gpu', action='store_false') # Some decoder parameters. parser.add_argument('-latent_space_size', default=10, help='Size of latent space.', type=int) parser.add_argument('-decoder_architecture', default='standard', help='Architecture to use.', type=str) parser.add_argument('-decoder_activation', default='relu', help='Activation function to use.', type=str) parser.add_argument('-decoder_no_batch_normalization', default=False, help='Do not use batch normalization.', action='store_true') parser.add_argument( '-decoder_channels', default=16, help= 'Channels of first convolutional layer, afterwards channels are doubled.', type=int) parser.add_argument('-decoder_units', default='1024,1024,1024,1024', help='Units for MLP.') return parser