implemented inference

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

@@ -54,7 +54,7 @@ class DeepSADTrainer(BaseTrainer):
         logger = logging.getLogger()
 
         # Get train data loader
-        train_loader, _ = dataset.loaders(
+        train_loader, _, _ = dataset.loaders(
             batch_size=self.batch_size, num_workers=self.n_jobs_dataloader
         )
 
@@ -130,11 +130,49 @@ class DeepSADTrainer(BaseTrainer):
 
         return net
 
+    def infer(self, dataset: BaseADDataset, net: BaseNet):
+        logger = logging.getLogger()
+
+        # Get test data loader
+        _, _, inference_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 inference...")
+        n_batches = 0
+        start_time = time.time()
+        scores = []
+        net.eval()
+        with torch.no_grad():
+            for data in inference_loader:
+                inputs, idx = data
+
+                inputs = inputs.to(self.device)
+                idx = idx.to(self.device)
+
+                outputs = net(inputs)
+                dist = torch.sum((outputs - self.c) ** 2, dim=1)
+                scores += dist.cpu().data.numpy().tolist()
+
+                n_batches += 1
+
+        self.inference_time = time.time() - start_time
+
+        # Log results
+        logger.info("Inference Time: {:.3f}s".format(self.inference_time))
+        logger.info("Finished inference.")
+
+        return np.array(scores)
+
     def test(self, dataset: BaseADDataset, net: BaseNet):
         logger = logging.getLogger()
 
         # Get test data loader
-        _, test_loader = dataset.loaders(
+        _, test_loader, _ = dataset.loaders(
             batch_size=self.batch_size, num_workers=self.n_jobs_dataloader
         )