def main(dataset_name, net_name, xp_path, data_path, load_config, load_model, objective, nu, device, seed,
optimizer_name, lr, n_epochs, lr_milestone, batch_size, weight_decay, pretrain, ae_optimizer_name, ae_lr,
ae_n_epochs, ae_lr_milestone, ae_batch_size, ae_weight_decay, n_jobs_dataloader, normal_class):
"""
Deep SVDD, a fully deep method for anomaly detection.
:arg DATASET_NAME: Name of the dataset to load.
:arg NET_NAME: Name of the neural network to use.
:arg XP_PATH: Export path for logging the experiment.
:arg DATA_PATH: Root path of data.
"""
# Get configuration
cfg = Config(locals().copy())
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
log_file = xp_path + '/log.txt'
file_handler = logging.FileHandler(log_file)
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
# Print arguments
logger.info('Log file is %s.' % log_file)
logger.info('Data path is %s.' % data_path)
logger.info('Export path is %s.' % xp_path)
logger.info('Dataset: %s' % dataset_name)
logger.info('Normal class: %d' % normal_class)
logger.info('Network: %s' % net_name)
# If specified, load experiment config from JSON-file
if load_config:
cfg.load_config(import_json=load_config)
logger.info('Loaded configuration from %s.' % load_config)
# Print configuration
logger.info('Deep SVDD objective: %s' % cfg.settings['objective'])
logger.info('Nu-paramerter: %.2f' % cfg.settings['nu'])
# Set seed
if cfg.settings['seed'] != -1:
random.seed(cfg.settings['seed'])
np.random.seed(cfg.settings['seed'])
torch.manual_seed(cfg.settings['seed'])
logger.info('Set seed to %d.' % cfg.settings['seed'])
# Default device to 'cpu' if cuda is not available
if not torch.cuda.is_available():
device = 'cpu'
logger.info('Computation device: %s' % device)
logger.info('Number of dataloader workers: %d' % n_jobs_dataloader)
# Load data
dataset = load_dataset(dataset_name, data_path, normal_class)
# Initialize DeepSVDD model and set neural network \phi
deep_SVDD = DeepSVDD(cfg.settings['objective'], cfg.settings['nu'])
deep_SVDD.set_network(net_name)
# If specified, load Deep SVDD model (radius R, center c, network weights, and possibly autoencoder weights)
if load_model:
deep_SVDD.load_model(model_path=load_model, load_ae=True)
logger.info('Loading model from %s.' % load_model)
logger.info('Pretraining: %s' % pretrain)
if pretrain:
# Log pretraining details
logger.info('Pretraining optimizer: %s' % cfg.settings['ae_optimizer_name'])
logger.info('Pretraining learning rate: %g' % cfg.settings['ae_lr'])
logger.info('Pretraining epochs: %d' % cfg.settings['ae_n_epochs'])
logger.info('Pretraining learning rate scheduler milestones: %s' % (cfg.settings['ae_lr_milestone'],))
logger.info('Pretraining batch size: %d' % cfg.settings['ae_batch_size'])
logger.info('Pretraining weight decay: %g' % cfg.settings['ae_weight_decay'])
# Pretrain model on dataset (via autoencoder)
deep_SVDD.pretrain(dataset,
optimizer_name=cfg.settings['ae_optimizer_name'],
lr=cfg.settings['ae_lr'],
n_epochs=cfg.settings['ae_n_epochs'],
lr_milestones=cfg.settings['ae_lr_milestone'],
batch_size=cfg.settings['ae_batch_size'],
weight_decay=cfg.settings['ae_weight_decay'],
device=device,
n_jobs_dataloader=n_jobs_dataloader)
# Log training details
logger.info('Training optimizer: %s' % cfg.settings['optimizer_name'])
logger.info('Training learning rate: %g' % cfg.settings['lr'])
logger.info('Training epochs: %d' % cfg.settings['n_epochs'])
logger.info('Training learning rate scheduler milestones: %s' % (cfg.settings['lr_milestone'],))
logger.info('Training batch size: %d' % cfg.settings['batch_size'])
logger.info('Training weight decay: %g' % cfg.settings['weight_decay'])
# Train model on dataset
deep_SVDD.train(dataset,
optimizer_name=cfg.settings['optimizer_name'],
lr=cfg.settings['lr'],
n_epochs=cfg.settings['n_epochs'],
lr_milestones=cfg.settings['lr_milestone'],
batch_size=cfg.settings['batch_size'],
weight_decay=cfg.settings['weight_decay'],
device=device,
n_jobs_dataloader=n_jobs_dataloader)
# Test model
deep_SVDD.test(dataset, device=device, n_jobs_dataloader=n_jobs_dataloader)
# Plot most anomalous and most normal (within-class) test samples
indices, labels, scores = zip(*deep_SVDD.results['test_scores'])
indices, labels, scores = np.array(indices), np.array(labels), np.array(scores)
idx_sorted = indices[labels == 0][np.argsort(scores[labels == 0])] # sorted from lowest to highest anomaly score
if dataset_name in ('hits', 'mnist', 'cifar10'):
if dataset_name == 'mnist' or dataset_name == 'hits':
X_normals = dataset.test_set.test_data[idx_sorted[:32], ...].unsqueeze(1)
X_outliers = dataset.test_set.test_data[idx_sorted[-32:], ...].unsqueeze(1)
if dataset_name == 'cifar10':
X_normals = torch.tensor(np.transpose(dataset.test_set.test_data[idx_sorted[:32], ...], (0, 3, 1, 2)))
X_outliers = torch.tensor(np.transpose(dataset.test_set.test_data[idx_sorted[-32:], ...], (0, 3, 1, 2)))
plot_images_grid(X_normals, export_img=xp_path + '/normals', title='Most normal examples', padding=2)
plot_images_grid(X_outliers, export_img=xp_path + '/outliers', title='Most anomalous examples', padding=2)
# Save results, model, and configuration
deep_SVDD.save_results(export_json=xp_path + '/results.json')
deep_SVDD.save_model(export_model=xp_path + '/model.tar')
cfg.save_config(export_json=xp_path + '/config.json')
outlier_class = 0
for data_name in data_names:
roc_list = [data_name]
gmean_list = [data_name]
roc_mat = np.zeros([1, 1])
gmean_mat = np.zeros([1, 1])
args.feat_dim = 3 if data_name == 'cifar10' else 1
#for i in range(0, n_ite):
dataset = load_dataset(data_name,
data_root,
args.normal_class,
outlier_class,
args.n_known_outlier_classes,
args.ratio_known_normal,
args.ratio_known_outlier,
args.ratio_pollution,
random_state=args.seed,
resolution=data_resolution[data_name])
#todo: add my method
cb_gan = EAL_GAN(args, dataset)
best_auc, best_gmean = cb_gan.fit()
print('AUC:%.4f, Gmean:%.4f ' % (best_auc, best_gmean))
roc_mat[0, 0] = best_auc
gmean_mat[0, 0] = best_gmean
roc_list = roc_list + np.mean(roc_mat, axis=0).tolist() + np.std(
# Set seed
if cfg.settings['seed'] != -1:
random.seed(cfg.settings['seed'])
np.random.seed(cfg.settings['seed'])
torch.manual_seed(cfg.settings['seed'])
logger.info('Set seed to %d.' % cfg.settings['seed'])
# Default device to 'cpu' if cuda is not available
if not torch.cuda.is_available():
device = 'cpu'
logger.info('Computation device: %s' % device)
logger.info('Number of dataloader workers: %d' % n_jobs_dataloader)
# Load data
dataset = load_dataset(dataset_name, data_path, normal_class)
# Initialize DeepSVDD model and set neural network \phi
deep_SVDD = DeepSVDD(cfg.settings['objective'], cfg.settings['nu'])
deep_SVDD.set_network(net_name)
# If specified, load Deep SVDD model (radius R, center c, network weights, and possibly autoencoder weights)
if load_model:
deep_SVDD.load_model(model_path=load_model, load_ae=True)
logger.info('Loading model from %s.' % load_model)
# Test model
deep_SVDD.test(dataset, device=device, n_jobs_dataloader=n_jobs_dataloader)
# Plot most anomalous and most normal (within-class) test samples
indices, labels, scores = zip(*deep_SVDD.results['test_scores'])
indices, labels, scores = np.array(indices), np.array(labels), np.array(