Fix graph loading and boundary mask

neural_lam/build_rectangular_graph.py

@@ -30,7 +30,7 @@ def main(input_args=None):
         help="Path to the configuration for neural-lam",
     )
     parser.add_argument(
-        "--name",
+        "--graph_name",
         type=str,
         help="Name to save graph as (default: multiscale)",
     )
@@ -74,8 +74,8 @@ def main(input_args=None):
         args.config_path is not None
     ), "Specify your config with --config_path"
     assert (
-        args.name is not None
-    ), "Specify the name to save graph as with --name"
+        args.graph_name is not None
+    ), "Specify the name to save graph as with --graph_name"
 
     _, datastore = load_config_and_datastore(config_path=args.config_path)
 
@@ -124,7 +124,9 @@ def main(input_args=None):
         print(f"{name}: {subgraph}")
 
     # Save graph
-    graph_dir_path = os.path.join(datastore.root_path, "graphs", args.name)
+    graph_dir_path = os.path.join(
+        datastore.root_path, "graphs", args.graph_name
+    )
     os.makedirs(graph_dir_path, exist_ok=True)
     for component, graph in graph_comp.items():
         # This seems like a bit of a hack, maybe better if saving in wmg

neural_lam/models/ar_model.py

@@ -70,12 +70,12 @@ def __init__(
         static_features_torch = torch.tensor(arr_static, dtype=torch.float32)
         self.register_buffer(
             "grid_static_features",
-            static_features_torch[self.boundary_mask.to(torch.bool)],
+            static_features_torch[self.boundary_mask[:, 0].to(torch.bool)],
             persistent=False,
         )
         self.register_buffer(
             "boundary_static_features",
-            static_features_torch[self.interior_mask.to(torch.bool)],
+            static_features_torch[self.interior_mask[:, 0].to(torch.bool)],
             persistent=False,
         )
 

neural_lam/models/base_graph_model.py

@@ -19,7 +19,7 @@ def __init__(self, args, config: NeuralLAMConfig, datastore: BaseDatastore):
         super().__init__(args, config=config, datastore=datastore)
 
         # Load graph with static features
-        graph_dir_path = datastore.root_path / "graphs" / args.graph
+        graph_dir_path = datastore.root_path / "graphs" / args.graph_name
         self.hierarchical, graph_ldict = utils.load_graph(
             graph_dir_path=graph_dir_path
         )

neural_lam/plot_graph.py

@@ -21,7 +21,7 @@ def main():
         help="Path to the configuration for neural-lam",
     )
     parser.add_argument(
-        "--name",
+        "--graph_name",
         type=str,
         default="multiscale",
         help="Name of saved graph to plot (default: multiscale)",
@@ -46,7 +46,9 @@ def main():
     _, datastore = load_config_and_datastore(config_path=args.config_path)
 
     # Load graph data
-    graph_dir_path = os.path.join(datastore.root_path, "graphs", args.name)
+    graph_dir_path = os.path.join(
+        datastore.root_path, "graphs", args.graph_name
+    )
     hierarchical, graph_ldict = utils.load_graph(graph_dir_path=graph_dir_path)
     (g2m_edge_index, m2g_edge_index, m2m_edge_index,) = (
         graph_ldict["g2m_edge_index"],

neural_lam/train_model.py

@@ -78,7 +78,7 @@ def main(input_args=None):
 
     # Model architecture
     parser.add_argument(
-        "--graph",
+        "--graph_name",
         type=str,
         default="multiscale",
         help="Graph to load and use in graph-based model "

neural_lam/weather_dataset.py

@@ -116,9 +116,9 @@ def __init__(
 
         # Load border/interior mask for splitting
         border_mask_float = torch.tensor(
-            self.datastore.boundary_mask, dtype=torch.float32
+            self.datastore.boundary_mask.to_numpy(), dtype=torch.float32
         )
-        self.border_mask = border_mask_float.to(torch.bool)[:, 0]
+        self.border_mask = border_mask_float.to(torch.bool)
         self.interior_mask = torch.logical_not(self.border_mask)
 
     def __len__(self):