240 lines
13 KiB
Python
240 lines
13 KiB
Python
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import click
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import torch
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import logging
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import random
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import numpy as np
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from utils.config import Config
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from utils.visualization.plot_images_grid import plot_images_grid
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from DeepSAD import DeepSAD
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from datasets.main import load_dataset
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################################################################################
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# Settings
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################################################################################
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@click.command()
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@click.argument('dataset_name', type=click.Choice(['mnist', 'fmnist', 'cifar10', 'arrhythmia', 'cardio', 'satellite',
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'satimage-2', 'shuttle', 'thyroid']))
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@click.argument('net_name', type=click.Choice(['mnist_LeNet', 'fmnist_LeNet', 'cifar10_LeNet', 'arrhythmia_mlp',
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'cardio_mlp', 'satellite_mlp', 'satimage-2_mlp', 'shuttle_mlp',
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'thyroid_mlp']))
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@click.argument('xp_path', type=click.Path(exists=True))
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@click.argument('data_path', type=click.Path(exists=True))
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@click.option('--load_config', type=click.Path(exists=True), default=None,
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help='Config JSON-file path (default: None).')
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@click.option('--load_model', type=click.Path(exists=True), default=None,
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help='Model file path (default: None).')
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@click.option('--eta', type=float, default=1.0, help='Deep SAD hyperparameter eta (must be 0 < eta).')
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@click.option('--ratio_known_normal', type=float, default=0.0,
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help='Ratio of known (labeled) normal training examples.')
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@click.option('--ratio_known_outlier', type=float, default=0.0,
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help='Ratio of known (labeled) anomalous training examples.')
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@click.option('--ratio_pollution', type=float, default=0.0,
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help='Pollution ratio of unlabeled training data with unknown (unlabeled) anomalies.')
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@click.option('--device', type=str, default='cuda', help='Computation device to use ("cpu", "cuda", "cuda:2", etc.).')
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@click.option('--seed', type=int, default=-1, help='Set seed. If -1, use randomization.')
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@click.option('--optimizer_name', type=click.Choice(['adam']), default='adam',
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help='Name of the optimizer to use for Deep SAD network training.')
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@click.option('--lr', type=float, default=0.001,
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help='Initial learning rate for Deep SAD network training. Default=0.001')
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@click.option('--n_epochs', type=int, default=50, help='Number of epochs to train.')
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@click.option('--lr_milestone', type=int, default=0, multiple=True,
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help='Lr scheduler milestones at which lr is multiplied by 0.1. Can be multiple and must be increasing.')
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@click.option('--batch_size', type=int, default=128, help='Batch size for mini-batch training.')
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@click.option('--weight_decay', type=float, default=1e-6,
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help='Weight decay (L2 penalty) hyperparameter for Deep SAD objective.')
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@click.option('--pretrain', type=bool, default=True,
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help='Pretrain neural network parameters via autoencoder.')
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@click.option('--ae_optimizer_name', type=click.Choice(['adam']), default='adam',
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help='Name of the optimizer to use for autoencoder pretraining.')
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@click.option('--ae_lr', type=float, default=0.001,
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help='Initial learning rate for autoencoder pretraining. Default=0.001')
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@click.option('--ae_n_epochs', type=int, default=100, help='Number of epochs to train autoencoder.')
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@click.option('--ae_lr_milestone', type=int, default=0, multiple=True,
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help='Lr scheduler milestones at which lr is multiplied by 0.1. Can be multiple and must be increasing.')
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@click.option('--ae_batch_size', type=int, default=128, help='Batch size for mini-batch autoencoder training.')
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@click.option('--ae_weight_decay', type=float, default=1e-6,
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help='Weight decay (L2 penalty) hyperparameter for autoencoder objective.')
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@click.option('--num_threads', type=int, default=0,
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help='Number of threads used for parallelizing CPU operations. 0 means that all resources are used.')
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@click.option('--n_jobs_dataloader', type=int, default=0,
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help='Number of workers for data loading. 0 means that the data will be loaded in the main process.')
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@click.option('--normal_class', type=int, default=0,
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help='Specify the normal class of the dataset (all other classes are considered anomalous).')
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@click.option('--known_outlier_class', type=int, default=1,
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help='Specify the known outlier class of the dataset for semi-supervised anomaly detection.')
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@click.option('--n_known_outlier_classes', type=int, default=0,
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help='Number of known outlier classes.'
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'If 0, no anomalies are known.'
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'If 1, outlier class as specified in --known_outlier_class option.'
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'If > 1, the specified number of outlier classes will be sampled at random.')
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def main(dataset_name, net_name, xp_path, data_path, load_config, load_model, eta,
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ratio_known_normal, ratio_known_outlier, ratio_pollution, device, seed,
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optimizer_name, lr, n_epochs, lr_milestone, batch_size, weight_decay,
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pretrain, ae_optimizer_name, ae_lr, ae_n_epochs, ae_lr_milestone, ae_batch_size, ae_weight_decay,
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num_threads, n_jobs_dataloader, normal_class, known_outlier_class, n_known_outlier_classes):
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"""
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Deep SAD, a method for deep semi-supervised anomaly detection.
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:arg DATASET_NAME: Name of the dataset to load.
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:arg NET_NAME: Name of the neural network to use.
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:arg XP_PATH: Export path for logging the experiment.
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:arg DATA_PATH: Root path of data.
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"""
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# Get configuration
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cfg = Config(locals().copy())
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# Set up logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger()
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logger.setLevel(logging.INFO)
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formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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log_file = xp_path + '/log.txt'
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file_handler = logging.FileHandler(log_file)
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file_handler.setLevel(logging.INFO)
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file_handler.setFormatter(formatter)
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logger.addHandler(file_handler)
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# Print paths
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logger.info('Log file is %s' % log_file)
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logger.info('Data path is %s' % data_path)
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logger.info('Export path is %s' % xp_path)
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# Print experimental setup
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logger.info('Dataset: %s' % dataset_name)
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logger.info('Normal class: %d' % normal_class)
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logger.info('Ratio of labeled normal train samples: %.2f' % ratio_known_normal)
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logger.info('Ratio of labeled anomalous samples: %.2f' % ratio_known_outlier)
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logger.info('Pollution ratio of unlabeled train data: %.2f' % ratio_pollution)
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if n_known_outlier_classes == 1:
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logger.info('Known anomaly class: %d' % known_outlier_class)
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else:
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logger.info('Number of known anomaly classes: %d' % n_known_outlier_classes)
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logger.info('Network: %s' % net_name)
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# If specified, load experiment config from JSON-file
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if load_config:
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cfg.load_config(import_json=load_config)
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logger.info('Loaded configuration from %s.' % load_config)
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# Print model configuration
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logger.info('Eta-parameter: %.2f' % cfg.settings['eta'])
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# Set seed
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if cfg.settings['seed'] != -1:
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random.seed(cfg.settings['seed'])
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np.random.seed(cfg.settings['seed'])
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torch.manual_seed(cfg.settings['seed'])
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torch.cuda.manual_seed(cfg.settings['seed'])
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torch.backends.cudnn.deterministic = True
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logger.info('Set seed to %d.' % cfg.settings['seed'])
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# Default device to 'cpu' if cuda is not available
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if not torch.cuda.is_available():
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device = 'cpu'
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# Set the number of threads used for parallelizing CPU operations
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if num_threads > 0:
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torch.set_num_threads(num_threads)
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logger.info('Computation device: %s' % device)
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logger.info('Number of threads: %d' % num_threads)
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logger.info('Number of dataloader workers: %d' % n_jobs_dataloader)
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# Load data
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dataset = load_dataset(dataset_name, data_path, normal_class, known_outlier_class, n_known_outlier_classes,
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ratio_known_normal, ratio_known_outlier, ratio_pollution,
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random_state=np.random.RandomState(cfg.settings['seed']))
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# Log random sample of known anomaly classes if more than 1 class
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if n_known_outlier_classes > 1:
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logger.info('Known anomaly classes: %s' % (dataset.known_outlier_classes,))
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# Initialize DeepSAD model and set neural network phi
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deepSAD = DeepSAD(cfg.settings['eta'])
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deepSAD.set_network(net_name)
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# If specified, load Deep SAD model (center c, network weights, and possibly autoencoder weights)
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if load_model:
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deepSAD.load_model(model_path=load_model, load_ae=True, map_location=device)
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logger.info('Loading model from %s.' % load_model)
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logger.info('Pretraining: %s' % pretrain)
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if pretrain:
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# Log pretraining details
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logger.info('Pretraining optimizer: %s' % cfg.settings['ae_optimizer_name'])
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logger.info('Pretraining learning rate: %g' % cfg.settings['ae_lr'])
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logger.info('Pretraining epochs: %d' % cfg.settings['ae_n_epochs'])
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logger.info('Pretraining learning rate scheduler milestones: %s' % (cfg.settings['ae_lr_milestone'],))
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logger.info('Pretraining batch size: %d' % cfg.settings['ae_batch_size'])
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logger.info('Pretraining weight decay: %g' % cfg.settings['ae_weight_decay'])
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# Pretrain model on dataset (via autoencoder)
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deepSAD.pretrain(dataset,
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optimizer_name=cfg.settings['ae_optimizer_name'],
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lr=cfg.settings['ae_lr'],
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n_epochs=cfg.settings['ae_n_epochs'],
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lr_milestones=cfg.settings['ae_lr_milestone'],
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batch_size=cfg.settings['ae_batch_size'],
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weight_decay=cfg.settings['ae_weight_decay'],
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device=device,
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n_jobs_dataloader=n_jobs_dataloader)
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# Save pretraining results
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deepSAD.save_ae_results(export_json=xp_path + '/ae_results.json')
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# Log training details
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logger.info('Training optimizer: %s' % cfg.settings['optimizer_name'])
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logger.info('Training learning rate: %g' % cfg.settings['lr'])
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logger.info('Training epochs: %d' % cfg.settings['n_epochs'])
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logger.info('Training learning rate scheduler milestones: %s' % (cfg.settings['lr_milestone'],))
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logger.info('Training batch size: %d' % cfg.settings['batch_size'])
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logger.info('Training weight decay: %g' % cfg.settings['weight_decay'])
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# Train model on dataset
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deepSAD.train(dataset,
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optimizer_name=cfg.settings['optimizer_name'],
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lr=cfg.settings['lr'],
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n_epochs=cfg.settings['n_epochs'],
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lr_milestones=cfg.settings['lr_milestone'],
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batch_size=cfg.settings['batch_size'],
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weight_decay=cfg.settings['weight_decay'],
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device=device,
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n_jobs_dataloader=n_jobs_dataloader)
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# Test model
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deepSAD.test(dataset, device=device, n_jobs_dataloader=n_jobs_dataloader)
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# Save results, model, and configuration
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deepSAD.save_results(export_json=xp_path + '/results.json')
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deepSAD.save_model(export_model=xp_path + '/model.tar')
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cfg.save_config(export_json=xp_path + '/config.json')
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# Plot most anomalous and most normal test samples
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indices, labels, scores = zip(*deepSAD.results['test_scores'])
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indices, labels, scores = np.array(indices), np.array(labels), np.array(scores)
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idx_all_sorted = indices[np.argsort(scores)] # from lowest to highest score
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idx_normal_sorted = indices[labels == 0][np.argsort(scores[labels == 0])] # from lowest to highest score
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if dataset_name in ('mnist', 'fmnist', 'cifar10'):
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if dataset_name in ('mnist', 'fmnist'):
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X_all_low = dataset.test_set.data[idx_all_sorted[:32], ...].unsqueeze(1)
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X_all_high = dataset.test_set.data[idx_all_sorted[-32:], ...].unsqueeze(1)
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X_normal_low = dataset.test_set.data[idx_normal_sorted[:32], ...].unsqueeze(1)
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X_normal_high = dataset.test_set.data[idx_normal_sorted[-32:], ...].unsqueeze(1)
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if dataset_name == 'cifar10':
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X_all_low = torch.tensor(np.transpose(dataset.test_set.data[idx_all_sorted[:32], ...], (0,3,1,2)))
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X_all_high = torch.tensor(np.transpose(dataset.test_set.data[idx_all_sorted[-32:], ...], (0,3,1,2)))
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X_normal_low = torch.tensor(np.transpose(dataset.test_set.data[idx_normal_sorted[:32], ...], (0,3,1,2)))
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X_normal_high = torch.tensor(np.transpose(dataset.test_set.data[idx_normal_sorted[-32:], ...], (0,3,1,2)))
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plot_images_grid(X_all_low, export_img=xp_path + '/all_low', padding=2)
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plot_images_grid(X_all_high, export_img=xp_path + '/all_high', padding=2)
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plot_images_grid(X_normal_low, export_img=xp_path + '/normals_low', padding=2)
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plot_images_grid(X_normal_high, export_img=xp_path + '/normals_high', padding=2)
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if __name__ == '__main__':
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main()
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