implemented inference

Deep-SAD-PyTorch/src/base/torchvision_dataset.py

@@ -14,19 +14,39 @@ class TorchvisionDataset(BaseADDataset):
         shuffle_train=True,
         shuffle_test=False,
         num_workers: int = 0,
-    ) -> (DataLoader, DataLoader):
-        train_loader = DataLoader(
-            dataset=self.train_set,
-            batch_size=batch_size,
-            shuffle=shuffle_train,
-            num_workers=num_workers,
-            drop_last=True,
+    ) -> (DataLoader, DataLoader, DataLoader):
+        train_loader = (
+            DataLoader(
+                dataset=self.train_set,
+                batch_size=batch_size,
+                shuffle=shuffle_train,
+                num_workers=num_workers,
+                drop_last=True,
+            )
+            if self.train_set is not None
+            else None
         )
-        test_loader = DataLoader(
-            dataset=self.test_set,
-            batch_size=batch_size,
-            shuffle=shuffle_test,
-            num_workers=num_workers,
-            drop_last=False,
+        test_loader = (
+            DataLoader(
+                dataset=self.test_set,
+                batch_size=batch_size,
+                shuffle=shuffle_test,
+                num_workers=num_workers,
+                drop_last=False,
+            )
+            if self.test_set is not None
+            else None
         )
-        return train_loader, test_loader
+
+        inference_loader = (
+            DataLoader(
+                dataset=self.inference_set,
+                batch_size=batch_size,
+                shuffle=False,
+                num_workers=num_workers,
+                drop_last=False,
+            )
+            if self.inference_set is not None
+            else None
+        )
+        return train_loader, test_loader, inference_loader