added deepsad base code

Deep-SAD-PyTorch/src/optim/DeepSAD_trainer.py

@@ -0,0 +1,173 @@
+from base.base_trainer import BaseTrainer
+from base.base_dataset import BaseADDataset
+from base.base_net import BaseNet
+from torch.utils.data.dataloader import DataLoader
+from sklearn.metrics import roc_auc_score
+
+import logging
+import time
+import torch
+import torch.optim as optim
+import numpy as np
+
+
+class DeepSADTrainer(BaseTrainer):
+
+    def __init__(self, c, eta: float, optimizer_name: str = 'adam', lr: float = 0.001, n_epochs: int = 150,
+                 lr_milestones: tuple = (), batch_size: int = 128, weight_decay: float = 1e-6, device: str = 'cuda',
+                 n_jobs_dataloader: int = 0):
+        super().__init__(optimizer_name, lr, n_epochs, lr_milestones, batch_size, weight_decay, device,
+                         n_jobs_dataloader)
+
+        # Deep SAD parameters
+        self.c = torch.tensor(c, device=self.device) if c is not None else None
+        self.eta = eta
+
+        # Optimization parameters
+        self.eps = 1e-6
+
+        # Results
+        self.train_time = None
+        self.test_auc = None
+        self.test_time = None
+        self.test_scores = None
+
+    def train(self, dataset: BaseADDataset, net: BaseNet):
+        logger = logging.getLogger()
+
+        # Get train data loader
+        train_loader, _ = dataset.loaders(batch_size=self.batch_size, num_workers=self.n_jobs_dataloader)
+
+        # Set device for network
+        net = net.to(self.device)
+
+        # Set optimizer (Adam optimizer for now)
+        optimizer = optim.Adam(net.parameters(), lr=self.lr, weight_decay=self.weight_decay)
+
+        # Set learning rate scheduler
+        scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=self.lr_milestones, gamma=0.1)
+
+        # Initialize hypersphere center c (if c not loaded)
+        if self.c is None:
+            logger.info('Initializing center c...')
+            self.c = self.init_center_c(train_loader, net)
+            logger.info('Center c initialized.')
+
+        # Training
+        logger.info('Starting training...')
+        start_time = time.time()
+        net.train()
+        for epoch in range(self.n_epochs):
+
+            scheduler.step()
+            if epoch in self.lr_milestones:
+                logger.info('  LR scheduler: new learning rate is %g' % float(scheduler.get_lr()[0]))
+
+            epoch_loss = 0.0
+            n_batches = 0
+            epoch_start_time = time.time()
+            for data in train_loader:
+                inputs, _, semi_targets, _ = data
+                inputs, semi_targets = inputs.to(self.device), semi_targets.to(self.device)
+
+                # Zero the network parameter gradients
+                optimizer.zero_grad()
+
+                # Update network parameters via backpropagation: forward + backward + optimize
+                outputs = net(inputs)
+                dist = torch.sum((outputs - self.c) ** 2, dim=1)
+                losses = torch.where(semi_targets == 0, dist, self.eta * ((dist + self.eps) ** semi_targets.float()))
+                loss = torch.mean(losses)
+                loss.backward()
+                optimizer.step()
+
+                epoch_loss += loss.item()
+                n_batches += 1
+
+            # log epoch statistics
+            epoch_train_time = time.time() - epoch_start_time
+            logger.info(f'| Epoch: {epoch + 1:03}/{self.n_epochs:03} | Train Time: {epoch_train_time:.3f}s '
+                        f'| Train Loss: {epoch_loss / n_batches:.6f} |')
+
+        self.train_time = time.time() - start_time
+        logger.info('Training Time: {:.3f}s'.format(self.train_time))
+        logger.info('Finished training.')
+
+        return net
+
+    def test(self, dataset: BaseADDataset, net: BaseNet):
+        logger = logging.getLogger()
+
+        # Get test data loader
+        _, test_loader = dataset.loaders(batch_size=self.batch_size, num_workers=self.n_jobs_dataloader)
+
+        # Set device for network
+        net = net.to(self.device)
+
+        # Testing
+        logger.info('Starting testing...')
+        epoch_loss = 0.0
+        n_batches = 0
+        start_time = time.time()
+        idx_label_score = []
+        net.eval()
+        with torch.no_grad():
+            for data in test_loader:
+                inputs, labels, semi_targets, idx = data
+
+                inputs = inputs.to(self.device)
+                labels = labels.to(self.device)
+                semi_targets = semi_targets.to(self.device)
+                idx = idx.to(self.device)
+
+                outputs = net(inputs)
+                dist = torch.sum((outputs - self.c) ** 2, dim=1)
+                losses = torch.where(semi_targets == 0, dist, self.eta * ((dist + self.eps) ** semi_targets.float()))
+                loss = torch.mean(losses)
+                scores = dist
+
+                # Save triples of (idx, label, score) in a list
+                idx_label_score += list(zip(idx.cpu().data.numpy().tolist(),
+                                            labels.cpu().data.numpy().tolist(),
+                                            scores.cpu().data.numpy().tolist()))
+
+                epoch_loss += loss.item()
+                n_batches += 1
+
+        self.test_time = time.time() - start_time
+        self.test_scores = idx_label_score
+
+        # Compute AUC
+        _, labels, scores = zip(*idx_label_score)
+        labels = np.array(labels)
+        scores = np.array(scores)
+        self.test_auc = roc_auc_score(labels, scores)
+
+        # Log results
+        logger.info('Test Loss: {:.6f}'.format(epoch_loss / n_batches))
+        logger.info('Test AUC: {:.2f}%'.format(100. * self.test_auc))
+        logger.info('Test Time: {:.3f}s'.format(self.test_time))
+        logger.info('Finished testing.')
+
+    def init_center_c(self, train_loader: DataLoader, net: BaseNet, eps=0.1):
+        """Initialize hypersphere center c as the mean from an initial forward pass on the data."""
+        n_samples = 0
+        c = torch.zeros(net.rep_dim, device=self.device)
+
+        net.eval()
+        with torch.no_grad():
+            for data in train_loader:
+                # get the inputs of the batch
+                inputs, _, _, _ = data
+                inputs = inputs.to(self.device)
+                outputs = net(inputs)
+                n_samples += outputs.shape[0]
+                c += torch.sum(outputs, dim=0)
+
+        c /= n_samples
+
+        # If c_i is too close to 0, set to +-eps. Reason: a zero unit can be trivially matched with zero weights.
+        c[(abs(c) < eps) & (c < 0)] = -eps
+        c[(abs(c) < eps) & (c > 0)] = eps
+
+        return c