Adding import statement for new typing

Signed-off-by: Nicholas Audric Adriel <[email protected]>

Author: nicholasadriel

art/attacks/poisoning/one_pixel_shortcut_attack.py

@@ -22,6 +22,8 @@
 
 import numpy as np
 
+from __future__ import annotations
+
 from art.attacks.attack import PoisoningAttackBlackBox
 
 
@@ -32,7 +34,11 @@ class OnePixelShortcutAttack(PoisoningAttackBlackBox):
     for each class by maximizing a mean-minus-variance objective over that class's
     images. The found pixel coordinate and color are applied to all images of the class
     (labels remain unchanged). Reference: Wu et al. (ICLR 2023).
+<<<<<<< HEAD
     
+=======
+
+>>>>>>> 4461f324a (Update the one-pixel shortcut attack and its unit tests based on review)
     | Paper link: https://arxiv.org/abs/2205.12141
     """
 

tests/attacks/poison/test_one_pixel_shortcut_attack.py

@@ -21,7 +21,7 @@
 import numpy as np
 import pytest
 from unittest.mock import patch
-
+from __future__ import annotations
 from art.attacks.poisoning.one_pixel_shortcut_attack import OnePixelShortcutAttack
 from tests.utils import ARTTestException
 
@@ -113,12 +113,12 @@ def test_one_pixel_effect_with_pytorchclassifier():
             input_shape=(1, 2, 2),
             nb_classes=2,
         )
-        classifier_clean.fit(X, y, nb_epochs=10, batch_size=4, verbose=0)
-        preds_clean = classifier_clean.predict(X)
+        classifier_clean.fit(x, y, nb_epochs=10, batch_size=4, verbose=0)
+        preds_clean = classifier_clean.predict(x)
         acc_clean = np.mean(preds_clean.argmax(axis=1) == y)
 
         ops_attack = OnePixelShortcutAttack()
-        x_poison, y_poison = ops_attack.poison(X.copy(), y.copy())
+        x_poison, y_poison = ops_attack.poison(x.copy(), y.copy())
 
         model_poisoned = nn.Sequential(nn.Flatten(), nn.Linear(4, 2))
         classifier_poisoned = PyTorchClassifier(