add more GNN models and fix

configs/default_experiment.yaml

@@ -1,22 +1,22 @@
 output_dir: ./output10
-# resume_output: False
+resume_output: True
 # csv_save: True
 # esd: True
 experiment_templates:
   - name: Local attack_3
-    seed: [0]
+    seed: [1]
     device: cuda
     model:
-      - name: GCN2
+      - name: [GCN_DPR]
         params:
           hidden_channels: 64
     dataset:
       name: [Cora]
       # name: [CoraFull, CS, Physics, Computers, Photo, CoraCitationFull, DBLP, PubMedCitationFull, Reddit] # ogbn-arxiv 
       root: ./datasets
       make_undirected: true
-      params:
-        split: full
+      # params:
+      #   split: full
       # train_ratio: 0.6
       # test_ratio: 0.2
       # val_ratio: 0.2
@@ -27,20 +27,20 @@ experiment_templates:
         #   params:
         #     value: 0.8
 
-    attack:
-      # - scope: local
-      #   name: [LocalDICE]
-      #   type: poison
-      #   epsilon: [0.5]
-      #   nodes: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
-      # - scope: global
-      #   name: FGSM
-      #   type: poison
-      #   epsilon: [0.5]
-      - scope: global
-        name: DICE
-        type: poison
-        epsilon: [0.5]
+    # attack:
+    #   # - scope: local
+    #   #   name: [LocalDICE]
+    #   #   type: poison
+    #   #   epsilon: [0.5]
+    #   #   nodes: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+    #   # - scope: global
+    #   #   name: FGSM
+    #   #   type: poison
+    #   #   epsilon: [0.5]
+    #   - scope: global
+    #     name: DICE
+    #     type: poison
+    #     epsilon: [0.25]
 
       # - scope: local
       #   name: [LocalDICE]
@@ -64,8 +64,8 @@ experiment_templates:
 
 
     training:
-      max_epochs: 100
-      patience: 70
+      max_epochs: 300
+      patience: 290
 
     optimizer:
       name: adam

custom_components/attacks/base_attack.py

@@ -311,41 +311,41 @@ def adj_adversary_for_poisoning(self):
 # from torch_geometric.datasets import Planetoid
 # from torch_geometric.transforms import ToSparseTensor
 # from torch_geometric.utils import dense_to_sparse
-# class DenseAttack(BaseAttack):
-#     @typechecked
-#     def __init__(
-#         self,
-#         adj: Union[SparseTensor, TensorType],
-#         attr: TensorType,
-#         labels: TensorType,
-#         idx_attack: np.ndarray,
-#         model,
-#         device: Union[str, int, torch.device],
-#         make_undirected: bool = True,
-#         loss_type: str = "CE",
-#         **kwargs,
-#     ):
-#         if isinstance(adj, SparseTensor):
-#             adj = adj.to_dense()
-#             # adj = dense_to_sparse(adj)
-#             # cora = Planetoid(root='datasets', name='Cora',transform=ToSparseTensor(remove_edge_index=False))
-#             # data = cora[0]
-#             # row, col, edge_attr = data.adj_t.t().coo()
-#             # edge_index = torch.stack([row, col], dim=0)
-#             # adj = edge_index
-#             # adj = data.adj_t.to_dense()
-#             # ad
-
-#         super().__init__(
-#             adj,
-#             attr,
-#             labels,
-#             idx_attack,
-#             model,
-#             device,
-#             loss_type=loss_type,
-#             make_undirected=make_undirected,
-#             **kwargs,
-#         )
-
-#         self.n = adj.shape[0]
+class DenseAttack(BaseAttack):
+    @typechecked
+    def __init__(
+        self,
+        adj: Union[SparseTensor, TensorType],
+        attr: TensorType,
+        labels: TensorType,
+        idx_attack: np.ndarray,
+        model,
+        device: Union[str, int, torch.device],
+        make_undirected: bool = True,
+        loss_type: str = "CE",
+        **kwargs,
+    ):
+        if isinstance(adj, SparseTensor):
+            adj = adj.to_dense()
+            # adj = dense_to_sparse(adj)
+            # cora = Planetoid(root='datasets', name='Cora',transform=ToSparseTensor(remove_edge_index=False))
+            # data = cora[0]
+            # row, col, edge_attr = data.adj_t.t().coo()
+            # edge_index = torch.stack([row, col], dim=0)
+            # adj = edge_index
+            # adj = data.adj_t.to_dense()
+            # ad
+
+        super().__init__(
+            adj,
+            attr,
+            labels,
+            idx_attack,
+            model,
+            device,
+            loss_type=loss_type,
+            make_undirected=make_undirected,
+            **kwargs,
+        )
+
+        self.n = adj.shape[0]