Merge pull request #33 from ispras/fix_split

fix_train_test_split. Add masks in batch

experiments/attack_defense_test.py

@@ -25,7 +25,8 @@ def test_attack_defense():
 
     # full_name = ("multiple-graphs", "TUDataset", 'MUTAG')
     # full_name = ("single-graph", "custom", 'karate')
-    full_name = ("single-graph", "Planetoid", 'Cora')
+    # full_name = ("single-graph", "Planetoid", 'Cora')
+    full_name = ("single-graph", "Amazon", 'Photo')
     # full_name = ("single-graph", "Planetoid", 'CiteSeer')
     # full_name = ("multiple-graphs", "TUDataset", 'PROTEINS')
 
@@ -235,8 +236,8 @@ def test_attack_defense():
 
     # gnn_model_manager.set_poison_attacker(poison_attack_config=random_poison_attack_config)
     # gnn_model_manager.set_poison_defender(poison_defense_config=gnnguard_poison_defense_config)
-    gnn_model_manager.set_evasion_attacker(evasion_attack_config=netattackgroup_evasion_attack_config)
-    # gnn_model_manager.set_evasion_defender(evasion_defense_config=at_evasion_defense_config)
+    # gnn_model_manager.set_evasion_attacker(evasion_attack_config=netattackgroup_evasion_attack_config)
+    gnn_model_manager.set_evasion_defender(evasion_defense_config=at_evasion_defense_config)
 
     warnings.warn("Start training")
     dataset.train_test_split()
@@ -266,7 +267,8 @@ def test_attack_defense():
 
 def test_meta():
     from attacks.metattack import meta_gradient_attack
-    my_device = device('cpu')
+    # my_device = device('cpu')
+    my_device = device('cuda' if torch.cuda.is_available() else 'cpu')
     full_name = ("single-graph", "Planetoid", 'Cora')
 
     dataset, data, results_dataset_path = DatasetManager.get_by_full_name(
@@ -705,7 +707,8 @@ def test_adv_training():
     from defense.evasion_defense import AdvTraining
 
     my_device = device('cpu')
-    full_name = ("single-graph", "Planetoid", 'Cora')
+    # full_name = ("single-graph", "Planetoid", 'Cora')
+    full_name = ("single-graph", "Amazon", 'Photo')
 
     dataset, data, results_dataset_path = DatasetManager.get_by_full_name(
         full_name=full_name,

src/base/datasets_processing.py

@@ -676,6 +676,9 @@ def train_test_split(self, percent_train_class: float = 0.8, percent_test_class:
         self.train_mask = train_mask
         self.test_mask = test_mask
         self.val_mask = val_mask
+        self.dataset.data.train_mask = train_mask
+        self.dataset.data.test_mask = test_mask
+        self.dataset.data.val_mask = val_mask
 
     def save_train_test_mask(self, path):
         """ Save current train/test mask to a given path (together with the model). """

src/models_builder/gnn_constructor.py

@@ -427,7 +427,7 @@ def forward(self, *args, **kwargs):
                         tensor_storage[layer_ind] = torch.clone(x)
                 layer_ind = curr_layer_ind
                 x_copy = torch.clone(x)
-                connection_tensor = torch.Tensor()
+                connection_tensor = torch.empty(0, device=x_copy.device)
                 for key, value in self.conn_dict.items():
                     if key[1] == curr_layer_ind:
                         if key[1] - key[0] == 1: