Load graph data and model state dict on CPU for compatibility

src/gnn.py

@@ -48,13 +48,13 @@ class GNNClassifier(torch.nn.Module):
 def load_data(version: str = "undirected"):
 
     if version == "undirected":
-        graph_data = torch.load(config.GNN_GRAPH_DATA_PATH)
-        title_to_id = torch.load(config.TITLE_TO_ID_PATH)
-        label_mapping = torch.load(config.LABEL_MAPPING_PATH)
+        graph_data = torch.load(config.GNN_GRAPH_DATA_PATH, map_location=torch.device("cpu"))
+        title_to_id = torch.load(config.TITLE_TO_ID_PATH, map_location=torch.device("cpu"))
+        label_mapping = torch.load(config.LABEL_MAPPING_PATH, map_location=torch.device("cpu"))
     elif version == "no_edge":
-        graph_data = torch.load(config.GNN_GRAPH_DATA_PATH.replace("undirected_gnn", "no_edge_gnn"))
-        title_to_id = torch.load(config.TITLE_TO_ID_PATH.replace("undirected_gnn", "no_edge_gnn"))
-        label_mapping = torch.load(config.LABEL_MAPPING_PATH.replace("undirected_gnn", "no_edge_gnn"))
+        graph_data = torch.load(config.GNN_GRAPH_DATA_PATH.replace("undirected_gnn", "no_edge_gnn"), map_location=torch.device("cpu"))
+        title_to_id = torch.load(config.TITLE_TO_ID_PATH.replace("undirected_gnn", "no_edge_gnn"), map_location=torch.device("cpu"))
+        label_mapping = torch.load(config.LABEL_MAPPING_PATH.replace("undirected_gnn", "no_edge_gnn"), map_location=torch.device("cpu"))
     else:
         raise ValueError(f"Unknown version: {version}")
 
@@ -141,7 +141,7 @@ if __name__ == "__main__":
     graph_data, title_to_id, label_mapping = load_data()
 
     model = GNNClassifier(input_dim=768, hidden_dim=128, layers=2, output_dim=len(label_mapping), dropout_rate=0.5)
-    model.load_state_dict(torch.load(r"C:\Users\pc\Desktop\Projects\Masters\data_mining\semantic_knowledge_graph\demo\models\gnn\gnn_classifier_model.pth"))
+    model.load_state_dict(torch.load(r"C:\Users\pc\Desktop\Projects\Masters\data_mining\semantic_knowledge_graph\demo\models\gnn\gnn_classifier_model.pth"), map_location=torch.device("cpu"))
 
     new_node_content = "Istanbul Türkiye'nin en büyük şehri ve kültürel başkentidir. Tarih boyunca birçok medeniyete ev sahipliği yapmıştır."
     embedder = Embedder(path=r"C:\Users\pc\Desktop\Projects\Masters\data_mining\semantic_knowledge_graph\demo\models\embedding\gte-multilingual-base")