new torch updates

Author: SherylHYX

torch_geometric_signed_directed/data/directed/DIGRAC_real_data.py

@@ -27,7 +27,10 @@ def __init__(self, name: str, root: str, transform: Optional[Callable] = None, p
         url = ('https://github.com/SherylHYX/pytorch_geometric_signed_directed/raw/main/datasets/'+name+'.npz')
         self.url = url
         super().__init__(root, transform, pre_transform)
-        self.data, self.slices = torch.load(self.processed_paths[0])
+        try:
+            self.data, self.slices = torch.load(self.processed_paths[0], weights_only=False)
+        except TypeError:
+            self.data, self.slices = torch.load(self.processed_paths[0])
 
     @property
     def raw_file_names(self):

torch_geometric_signed_directed/data/directed/Telegram.py

@@ -28,7 +28,10 @@ def __init__(self, root: str, transform: Optional[Callable] = None, pre_transfor
         self.url = (
             'https://github.com/SherylHYX/pytorch_geometric_signed_directed/raw/main/datasets/telegram')
         super().__init__(root, transform, pre_transform)
-        self.data, self.slices = torch.load(self.processed_paths[0])
+        try:
+            self.data, self.slices = torch.load(self.processed_paths[0], weights_only=False)
+        except TypeError:
+            self.data, self.slices = torch.load(self.processed_paths[0])
 
     @property
     def raw_file_names(self):

torch_geometric_signed_directed/data/directed/WikiCS.py

@@ -24,7 +24,10 @@ class WikiCS(InMemoryDataset):
     def __init__(self, root: str, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None):
         self.url = 'https://github.com/pmernyei/wiki-cs-dataset/raw/master/dataset'
         super(WikiCS, self).__init__(root, transform, pre_transform)
-        self.data, self.slices = torch.load(self.processed_paths[0])
+        try:
+            self.data, self.slices = torch.load(self.processed_paths[0], weights_only=False)
+        except TypeError:
+            self.data, self.slices = torch.load(self.processed_paths[0])
 
     @property
     def raw_file_names(self):

torch_geometric_signed_directed/data/directed/WikipediaNetwork.py

@@ -30,7 +30,10 @@ def __init__(self, root: str, name: str, transform: Optional[Callable] = None, p
         self.url = ('https://raw.githubusercontent.com/graphdml-uiuc-jlu/'
                     'geom-gcn/f1fc0d14b3b019c562737240d06ec83b07d16a8f')
         super(WikipediaNetwork, self).__init__(root, transform, pre_transform)
-        self.data, self.slices = torch.load(self.processed_paths[0])
+        try:
+            self.data, self.slices = torch.load(self.processed_paths[0], weights_only=False)
+        except TypeError:
+            self.data, self.slices = torch.load(self.processed_paths[0])
 
     @property
     def raw_dir(self):

torch_geometric_signed_directed/data/directed/citation.py

@@ -28,7 +28,10 @@ def __init__(self, root: str, transform: Optional[Callable] = None, pre_transfor
         self.url = (
             'https://github.com/SherylHYX/pytorch_geometric_signed_directed/raw/main/datasets/cora_ml.npz')
         super().__init__(root, transform, pre_transform)
-        self.data, self.slices = torch.load(self.processed_paths[0])
+        try:
+            self.data, self.slices = torch.load(self.processed_paths[0], weights_only=False)
+        except TypeError:
+            self.data, self.slices = torch.load(self.processed_paths[0])
 
     @property
     def raw_file_names(self):

torch_geometric_signed_directed/data/signed/MSGNN_real_data.py

@@ -29,7 +29,10 @@ def __init__(self, name: str, root: str, transform: Optional[Callable] = None,
         self.url = self._generate_url(name)
         self.sparsify_level = sparsify_level
         super().__init__(root, transform, pre_transform)
-        self.data, self.slices = torch.load(self.processed_paths[0])
+        try:
+            self.data, self.slices = torch.load(self.processed_paths[0], weights_only=False)
+        except TypeError:
+            self.data, self.slices = torch.load(self.processed_paths[0])
 
     def _generate_url(self, name: str) -> Tuple:
         if self.name[:11].lower() == 'fill-pvclcl':

torch_geometric_signed_directed/data/signed/SDGNN_real_data.py

@@ -38,7 +38,10 @@ def __init__(self, name: str,  root: str, transform: Optional[Callable] = None,
         self.root = root
 
         super().__init__(root, transform, pre_transform)
-        self.data, self.slices = torch.load(self.processed_paths[0])
+        try:
+            self.data, self.slices = torch.load(self.processed_paths[0], weights_only=False)
+        except TypeError:
+            self.data, self.slices = torch.load(self.processed_paths[0])
 
     @property
     def raw_dir(self) -> str:

torch_geometric_signed_directed/data/signed/SSSNET_real_data.py

@@ -27,7 +27,10 @@ def __init__(self, name: str, root: str, transform: Optional[Callable] = None, p
         self.name = name
         self.url = self._generate_url(name)
         super().__init__(root, transform, pre_transform)
-        self.data, self.slices = torch.load(self.processed_paths[0])
+        try:
+            self.data, self.slices = torch.load(self.processed_paths[0], weights_only=False)
+        except TypeError:
+            self.data, self.slices = torch.load(self.processed_paths[0])
 
     def _generate_url(self, name: str) -> Tuple:
         if name.lower() == 'sampson':

torch_geometric_signed_directed/utils/directed/get_magnetic_Laplacian.py

@@ -89,5 +89,5 @@ def get_magnetic_Laplacian(edge_index: torch.LongTensor, edge_weight: Optional[t
         L = to_scipy_sparse_matrix(edge_index, edge_weight, num_nodes)
 
         lambda_max = eigsh(L, k=1, which='LM', return_eigenvectors=False)
-        lambda_max = float(lambda_max.real)
+        lambda_max = float(np.asarray(lambda_max).real.item())
         return edge_index, edge_weight.real, edge_weight.imag, lambda_max

torch_geometric_signed_directed/utils/general/get_magnetic_signed_Laplacian.py

@@ -95,5 +95,5 @@ def get_magnetic_signed_Laplacian(edge_index: torch.LongTensor, edge_weight: Opt
         L = to_scipy_sparse_matrix(edge_index, edge_weight, num_nodes)
 
         lambda_max = eigsh(L, k=1, which='LM', return_eigenvectors=False)
-        lambda_max = float(lambda_max.real)
+        lambda_max = float(np.asarray(lambda_max).real.item())
         return edge_index, edge_weight.real, edge_weight.imag, lambda_max