def test(self, dataset: BaseADDataset, device: str = 'cuda', n_jobs_dataloader: int = 0): """Tests the Deep SAD model on the test data.""" if self.trainer is None: self.trainer = DeepSADTrainer(self.c, self.eta, device=device, n_jobs_dataloader=n_jobs_dataloader) # To test on two different test sets, one with and one without corner cracks. corner_cracks = False self.trainer.test(dataset, self.net, corner_cracks=corner_cracks) self.results['test_auc'] = self.trainer.test_auc self.results['test_time'] = self.trainer.test_time self.results['test_scores'] = self.trainer.test_scores corner_cracks = True self.trainer.test(dataset, self.net, corner_cracks=corner_cracks) self.results['test_auc (corner)'] = self.trainer.test_auc self.results['test_time (corner)'] = self.trainer.test_time self.results['test_scores (corner)'] = self.trainer.test_scores return self.results
def train(self, dataset: BaseADDataset, optimizer_name: str = 'adam', lr: float = 0.001, n_epochs: int = 50, lr_milestones: tuple = (), batch_size: int = 128, weight_decay: float = 1e-6, device: str = 'cuda', n_jobs_dataloader: int = 0): """Trains the Deep SAD model on the training data.""" self.optimizer_name = optimizer_name self.trainer = DeepSADTrainer(self.c, self.eta, optimizer_name=optimizer_name, lr=lr, n_epochs=n_epochs, lr_milestones=lr_milestones, batch_size=batch_size, weight_decay=weight_decay, device=device, n_jobs_dataloader=n_jobs_dataloader) # Get the model self.net = self.trainer.train(dataset, self.net) self.results['train_time'] = self.trainer.train_time self.c = self.trainer.c.cpu().data.numpy().tolist() # get as list
def test(self, dataset: BaseADDataset, device: str = 'cuda', n_jobs_dataloader: int = 0): """Tests the Deep SAD model on the test data.""" if self.trainer is None: self.trainer = DeepSADTrainer(self.c, self.eta, device=device, n_jobs_dataloader=n_jobs_dataloader) self.trainer.test(dataset, self.net) # Get results self.results['test_auc'] = self.trainer.test_auc self.results['test_time'] = self.trainer.test_time self.results['test_scores'] = self.trainer.test_scores
def train(self, dataset: BaseADDataset, optimizer_name: str = 'adam', lr: float = 0.001, n_epochs: int = 50, lr_milestones: tuple = (), batch_size: int = 128, weight_decay: float = 1e-6, device: str = 'cuda', n_jobs_dataloader: int = 0, reporter=None): """Trains the Deep SAD model on the training data.""" trainer = DeepSADTrainer(self.c, self.eta, optimizer_name=optimizer_name, lr=lr, n_epochs=n_epochs, lr_milestones=lr_milestones, batch_size=batch_size, weight_decay=weight_decay, device=device, n_jobs_dataloader=n_jobs_dataloader, reporter=reporter) self._train(trainer, dataset) self.c = self.trainer.c.cpu().data.numpy().tolist() # get as list return self
def test(self, dataset: BaseADDataset, device: str = 'cuda', n_jobs_dataloader: int = 0): """Tests the Deep SAD model on the test data.""" if self.trainer is None: self.trainer = DeepSADTrainer(self.c, self.eta, device=device, n_jobs_dataloader=n_jobs_dataloader) self._test(self.trainer, dataset) return self
class DeepSAD(object): """A class for the Deep SAD method. Attributes: eta: Deep SAD hyperparameter eta (must be 0 < eta). c: Hypersphere center c. net_name: A string indicating the name of the neural network to use. net: The neural network phi. trainer: DeepSADTrainer to train a Deep SAD model. optimizer_name: A string indicating the optimizer to use for training the Deep SAD network. ae_net: The autoencoder network corresponding to phi for network weights pretraining. ae_trainer: AETrainer to train an autoencoder in pretraining. ae_optimizer_name: A string indicating the optimizer to use for pretraining the autoencoder. results: A dictionary to save the results. ae_results: A dictionary to save the autoencoder results. """ def __init__(self, eta: float = 1.0): """Inits DeepSAD with hyperparameter eta.""" self.eta = eta self.c = None # hypersphere center c self.net_name = None self.net = None # neural network phi self.trainer = None self.optimizer_name = None self.ae_net = None # autoencoder network for pretraining self.ae_trainer = None self.ae_optimizer_name = None self.results = { 'train_time': None, 'test_auc': None, 'test_time': None, 'test_scores': None, 'train_time (corner)': None, 'test_auc (corner)': None, 'test_time (corner)': None, 'test_scores (corner)': None, } self.ae_results = { 'train_time': None, 'test_auc': None, 'test_time': None } def set_network(self, net_name): """Builds the neural network phi.""" self.net_name = net_name self.net = build_network(net_name) def train(self, dataset: BaseADDataset, optimizer_name: str = 'adam', lr: float = 0.001, n_epochs: int = 50, lr_milestones: tuple = (), batch_size: int = 128, weight_decay: float = 1e-6, device: str = 'cuda', n_jobs_dataloader: int = 0): """Trains the Deep SAD model on the training data.""" self.optimizer_name = optimizer_name self.trainer = DeepSADTrainer(self.c, self.eta, optimizer_name=optimizer_name, lr=lr, n_epochs=n_epochs, lr_milestones=lr_milestones, batch_size=batch_size, weight_decay=weight_decay, device=device, n_jobs_dataloader=n_jobs_dataloader) # Get the model self.net = self.trainer.train(dataset, self.net) self.results['train_time'] = self.trainer.train_time self.c = self.trainer.c.cpu().data.numpy().tolist() # get as list def test(self, dataset: BaseADDataset, device: str = 'cuda', n_jobs_dataloader: int = 0): """Tests the Deep SAD model on the test data.""" if self.trainer is None: self.trainer = DeepSADTrainer(self.c, self.eta, device=device, n_jobs_dataloader=n_jobs_dataloader) # To test on two different test sets, one with and one without corner cracks. corner_cracks = False self.trainer.test(dataset, self.net, corner_cracks=corner_cracks) self.results['test_auc'] = self.trainer.test_auc self.results['test_time'] = self.trainer.test_time self.results['test_scores'] = self.trainer.test_scores corner_cracks = True self.trainer.test(dataset, self.net, corner_cracks=corner_cracks) self.results['test_auc (corner)'] = self.trainer.test_auc self.results['test_time (corner)'] = self.trainer.test_time self.results['test_scores (corner)'] = self.trainer.test_scores return self.results def pretrain(self, dataset: BaseADDataset, optimizer_name: str = 'adam', lr: float = 0.001, n_epochs: int = 100, lr_milestones: tuple = (), batch_size: int = 128, weight_decay: float = 1e-6, device: str = 'cuda', n_jobs_dataloader: int = 0): """Pretrains the weights for the Deep SAD network phi via autoencoder.""" # Set autoencoder network self.ae_net = build_autoencoder(self.net_name) # Train self.ae_optimizer_name = optimizer_name self.ae_trainer = AETrainer(optimizer_name, lr=lr, n_epochs=n_epochs, lr_milestones=lr_milestones, batch_size=batch_size, weight_decay=weight_decay, device=device, n_jobs_dataloader=n_jobs_dataloader) self.ae_net = self.ae_trainer.train(dataset, self.ae_net) # Get train results self.ae_results['train_time'] = self.ae_trainer.train_time # Test self.ae_trainer.test(dataset, self.ae_net) # Get test results self.ae_results['test_auc'] = self.ae_trainer.test_auc self.ae_results['test_time'] = self.ae_trainer.test_time # Initialize Deep SAD network weights from pre-trained encoder self.init_network_weights_from_pretraining() def init_network_weights_from_pretraining(self): """Initialize the Deep SAD network weights from the encoder weights of the pretraining autoencoder.""" net_dict = self.net.state_dict() ae_net_dict = self.ae_net.state_dict() # Filter out decoder network keys ae_net_dict = {k: v for k, v in ae_net_dict.items() if k in net_dict} # Overwrite values in the existing state_dict net_dict.update(ae_net_dict) # Load the new state_dict self.net.load_state_dict(net_dict) def save_model(self, export_model, save_ae=True): """Save Deep SAD model to export_model.""" net_dict = self.net.state_dict() ae_net_dict = self.ae_net.state_dict() if save_ae else None torch.save( { 'c': self.c, 'net_dict': net_dict, 'ae_net_dict': ae_net_dict }, export_model) def load_model(self, model_path, load_ae=False, map_location='cpu'): """Load Deep SAD model from model_path.""" model_dict = torch.load(model_path, map_location=map_location) self.c = model_dict['c'] self.net.load_state_dict(model_dict['net_dict']) # load autoencoder parameters if specified if load_ae: if self.ae_net is None: self.ae_net = build_autoencoder(self.net_name) self.ae_net.load_state_dict(model_dict['ae_net_dict']) def save_results(self, export_json): """Save results dict to a JSON-file.""" with open(export_json, 'w') as fp: json.dump(self.results, fp) def save_ae_results(self, export_json): """Save autoencoder results dict to a JSON-file.""" with open(export_json, 'w') as fp: json.dump(self.ae_results, fp)