Add additional tests for DPatch

Signed-off-by: Beat Buesser <[email protected]>

Author: Beat Buesser

art/attacks/evasion/dpatch.py

@@ -112,7 +112,7 @@ def generate(
                 raise ValueError("The target_label has to be a 1-dimensional array.")
             self.target_label = target_label.tolist()
         else:
-            if not len(target_label == x.shape[0]) or not isinstance(target_label, list):
+            if not len(target_label) == x.shape[0] or not isinstance(target_label, list):
                 raise ValueError("The target_label as list of integers needs to of length number of images in `x`.")
             self.target_label = target_label
 
@@ -134,7 +134,7 @@ def generate(
 
                 target_dict = dict()
                 target_dict["boxes"] = np.asarray([[i_x_1, i_y_1, i_x_2, i_y_2]])
-                target_dict["labels"] = np.asarray([target_label[i_image],])
+                target_dict["labels"] = np.asarray([self.target_label[i_image],])
                 target_dict["scores"] = np.asarray([1.0,])
 
                 patch_target.append(target_dict)
@@ -150,7 +150,7 @@ def generate(
                     x=patched_images[i_batch_start:i_batch_end], y=patch_target[i_batch_start:i_batch_end],
                 )
 
-                for i_image in range(self.batch_size):
+                for i_image in range(patched_images.shape[0]):
 
                     i_x_1 = transforms[i_batch_start + i_image]["i_x_1"]
                     i_x_2 = transforms[i_batch_start + i_image]["i_x_2"]

tests/attacks/evasion/test_dpatch.py

@@ -130,6 +130,39 @@ def test_augment_images_with_patch(random_location, image_format, fix_get_mnist_
     np.testing.assert_array_equal(patched_images[1, 2, :, 0], patched_images_column)
 
 
+def test_exceptions(get_default_mnist_subset, image_dl_estimator):
+    class ObjectDetector(BaseEstimator, LossGradientsMixin, ObjectDetectorMixin):
+
+        clip_values = (0, 1)
+        channels_first = False
+
+        def fit(self):
+            pass
+
+        def loss_gradient(self, x, y, **kwargs):
+            pass
+
+        def predict(self, x, **kwargs):
+            pass
+
+    estimator = ObjectDetector()
+
+    (x_train_mnist, y_train_mnist), (_, _) = get_default_mnist_subset
+
+    attack = DPatch(estimator=estimator, patch_shape=(4, 4, 1), learning_rate=5.0, max_iter=5, batch_size=16,)
+
+    with pytest.raises(ValueError, match="The DPatch attack does not use target labels."):
+        attack.generate(x=x_train_mnist, y=y_train_mnist)
+
+    with pytest.raises(
+        ValueError, match="The target_label as list of integers needs to of length number of images in" " `x`."
+    ):
+        attack.generate(x=x_train_mnist, y=None, target_label=[1, 2, 3])
+
+    with pytest.raises(ValueError, match="The target_label has to be a 1-dimensional array."):
+        attack.generate(x=x_train_mnist, y=None, target_label=np.asarray([[1, 2, 3], [4, 5, 6]]))
+
+
 def test_classifier_type_check_fail():
     backend_test_classifier_type_check_fail(DPatch, [BaseEstimator, LossGradientsMixin, ObjectDetectorMixin])