Fix label persistence during the training phase, other minor changes

Author: enriquetomasmb

nebula/addons/attacks/dataset/labelflipping.py

@@ -8,9 +8,9 @@
 """
 
 import copy
+import logging
 import random
-
-import torch
+import numpy as np
 
 from nebula.addons.attacks.dataset.datasetattack import DatasetAttack
 
@@ -87,30 +87,41 @@ def labelFlipping(
             - In targeted mode, labels that match `target_label` are directly changed to `target_changed_label`.
         """
         new_dataset = copy.deepcopy(dataset)
+        if not isinstance(new_dataset.targets, np.ndarray):
+            new_dataset.targets = np.array(new_dataset.targets)
+        else:
+            new_dataset.targets = new_dataset.targets.copy()
 
-        targets = torch.tensor(new_dataset.targets) if isinstance(new_dataset.targets, list) else new_dataset.targets
+        # logging.info(f"[{self.__class__.__name__}] First 20 labels before flipping: {new_dataset.targets[:20]}")
+        # logging.info(f"[{self.__class__.__name__}] First 20 indices before flipping: {indices[:20]}")
 
-        num_indices = len(indices)
-        class_list = list(set(targets.tolist()))
         if not targeted:
+            num_indices = len(indices)
             num_flipped = int(poisoned_percent * num_indices)
-            if num_indices == 0:
-                return new_dataset
-            if num_flipped > num_indices:
-                return new_dataset
-            flipped_indice = random.sample(indices, num_flipped)
-
-            for i in flipped_indice:
-                t = targets[i]
-                flipped = torch.tensor(random.sample(class_list, 1)[0])
-                while t == flipped:
-                    flipped = torch.tensor(random.sample(class_list, 1)[0])
-                targets[i] = flipped
+            if num_indices == 0 or num_flipped > num_indices:
+                return
+            flipped_indices = random.sample(indices, num_flipped)
+            class_list = list(set(new_dataset.targets.tolist()))
+            for i in flipped_indices:
+                current_label = new_dataset.targets[i]
+                new_label = random.choice(class_list)
+                while new_label == current_label:
+                    new_label = random.choice(class_list)
+                new_dataset.targets[i] = new_label
         else:
             for i in indices:
-                if int(targets[i]) == int(target_label):
-                    targets[i] = torch.tensor(target_changed_label)
-        new_dataset.targets = targets
+                if int(new_dataset.targets[i]) == target_label:
+                    new_dataset.targets[i] = target_changed_label
+
+            if target_label in new_dataset.targets:
+                logging.info(f"[{self.__class__.__name__}] Target label {target_label} still present after flipping.")
+            else:
+                logging.info(
+                    f"[{self.__class__.__name__}] Target label {target_label} successfully flipped to {target_changed_label}."
+                )
+
+        # logging.info(f"[{self.__class__.__name__}] First 20 labels after flipping: {new_dataset.targets[:20]}")
+
         return new_dataset
 
     def get_malicious_dataset(self):

nebula/core/datasets/cifar10/cifar10.py

@@ -26,7 +26,7 @@ def __getitem__(self, idx):
 
         # CIFAR10 from torchvision returns a tuple (image, target)
         if isinstance(data, tuple):
-            img, target = data
+            img, _ = data
         else:
             img = data
 

nebula/core/datasets/cifar100/cifar100.py

@@ -26,7 +26,7 @@ def __getitem__(self, idx):
 
         # CIFAR100 from torchvision returns a tuple (image, target)
         if isinstance(data, tuple):
-            img, target = data
+            img, _ = data
         else:
             img = data
 

nebula/core/datasets/emnist/emnist.py

@@ -26,7 +26,7 @@ def __getitem__(self, idx):
 
         # EMNIST from torchvision returns a tuple (image, target)
         if isinstance(data, tuple):
-            img, target = data
+            img, _ = data
         else:
             img = data
 

nebula/core/datasets/fashionmnist/fashionmnist.py

@@ -22,7 +22,7 @@ def __getitem__(self, idx):
 
         # FashionMNIST from torchvision returns a tuple (image, target)
         if isinstance(data, tuple):
-            img, target = data
+            img, _ = data
         else:
             img = data
 

nebula/core/datasets/mnist/mnist.py

@@ -22,7 +22,7 @@ def __getitem__(self, idx):
 
         # MNIST from torchvision returns a tuple (image, target)
         if isinstance(data, tuple):
-            img, target = data
+            img, _ = data
         else:
             img = data
 

nebula/core/datasets/nebuladataset.py

@@ -90,7 +90,8 @@ def __len__(self):
 
     def __getitem__(self, idx):
         data = self.data[idx]
-        target = self.targets[idx]
+        # Persist the modified targets (if any) during the training process
+        target = self.targets[idx] if hasattr(self, "targets") and self.targets is not None else None
         return data, target
 
     def set_data(self, data, targets, data_opt=None, targets_opt=None):
@@ -269,9 +270,7 @@ def load_partition(self):
             self.test_set = self.handler(test_partition_file, "test", config=self.config)
             self.test_indices = list(range(len(self.test_set)))
 
-            self.local_test_set = self.handler(
-                test_partition_file, "local_test", config=self.config, empty=True
-            )
+            self.local_test_set = self.handler(test_partition_file, "local_test", config=self.config, empty=True)
             self.local_test_set.set_data(self.test_set.data, self.test_set.targets)
             self.local_test_indices = self.set_local_test_indices()
 

nebula/frontend/templates/deployment.html

@@ -588,7 +588,7 @@ <h5 id="selection-interval-title" class="step-title" style="display: none;">Targ
                         <h5 id="start-attack-title" class="step-title" style="display: none;">Starting round</h5>
                         <div class="form-check form-check-inline" id="start-attack-container" style="display: none;">
                             <input type="number" class="form-control" id="start-attack"
-                                placeholder="Starting round" min="1" value="1"
+                                placeholder="Starting round" min="0" value="1"
                                 style="display: inline; width: 80%">
                         </div>
                         <h5 id="stop-attack-title" class="step-title" style="display: none;">Stopping round</h5>