added new models

SuperResolution/models.py

@@ -169,7 +169,6 @@ def check_performance(
         net (torch.nn.Module): The neural network model to evaluate.
         datafolder (str): Path to the folder containing the data.
         my_loss (torch.nn.Module): The loss function used for evaluation.
-        device (torch.device): The device to perform the computations on (e.g., 'cpu' or 'cuda').
         batchsize (int, optional): The batch size for data loading. Defaults to 50.
 
     Returns:
@@ -259,6 +258,7 @@ def load_model(directory_path: str) -> Tuple[nn.Module, Dict[str, Any]]:
     """
     # Pattern to match the checkpoint files
     pattern = r"model_epoch_counter_(\d+)_data_time_\d+\.pth"
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
     # List to store the checkpoint file names and their indices
     checkpoints = []
@@ -342,9 +342,14 @@ def load_model(directory_path: str) -> Tuple[nn.Module, Dict[str, Any]]:
     path_to_largest_checkpoint_file = os.path.join(
         directory_path, largest_checkpoint_file
     )
+    print(device)
     # Load the model state if restarting
     if os.path.exists(path_to_largest_checkpoint_file):
-        net.load_state_dict(torch.load(path_to_largest_checkpoint_file))
+        net.load_state_dict(
+            torch.load(
+                path_to_largest_checkpoint_file, map_location=torch.device(device)
+            )
+        )
         print(f"loaded model from {path_to_largest_checkpoint_file}")
     else:
         print(