Fix style checks for DGM poisoning attacks

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

Author: Beat Buesser

art/attacks/poisoning/backdoor_attack_dgm/backdoor_attack_dgm_trail.py

@@ -25,7 +25,7 @@
 
 import numpy as np
 
-from art.estimators.gan.tensorflow_gan import TensorFlowV2GAN
+from art.estimators.gan.tensorflow import TensorFlowV2GAN
 from art.attacks.attack import PoisoningAttackGenerator
 
 logger = logging.getLogger(__name__)

art/estimators/gan/__init__.py

@@ -1,4 +1,4 @@
 """
 GAN Estimator API.
 """
-from art.estimators.gan.tensorflow_gan import TensorFlowV2GAN
+from art.estimators.gan.tensorflow import TensorFlowV2GAN

art/estimators/gan/tensorflow_gan.py renamed to art/estimators/gan/tensorflow.py

@@ -18,7 +18,7 @@
 """
 This module creates GANs using the TensorFlow ML Framework
 """
-from typing import Tuple, TYPE_CHECKING
+from typing import Tuple, TYPE_CHECKING, Union
 
 import numpy as np
 from art.estimators.tensorflow import TensorFlowV2Estimator
@@ -52,17 +52,15 @@ def __init__(
         :param generator_optimizer_fct: the optimizer function to use for the generator
         :param discriminator_optimizer_fct: the optimizer function to use for the discriminator
         """
-        super().__init__(model=None, clip_values=None)
+        super().__init__(model=None, clip_values=None, channels_first=None)
         self._generator = generator
         self._discriminator_classifier = discriminator
         self._generator_loss = generator_loss
         self._generator_optimizer_fct = generator_optimizer_fct
         self._discriminator_loss = discriminator_loss
         self._discriminator_optimizer_fct = discriminator_optimizer_fct
 
-    def predict(
-        self, x: np.ndarray, batch_size: int = 128, **kwargs
-    ) -> np.ndarray:  # lgtm [py/inheritance/incorrect-overridden-signature]
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> np.ndarray:
         """
         Generates a sample
 
@@ -166,3 +164,11 @@ def discriminator_optimizer_fct(self) -> "tf.Tensor":
         :return: the optimizer function for the discriminator
         """
         return self._discriminator_optimizer_fct
+
+    def loss_gradient(self, x, y, **kwargs):
+        raise NotImplementedError
+
+    def get_activations(
+        self, x: np.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False
+    ) -> np.ndarray:
+        raise NotImplementedError

art/estimators/tensorflow.py

@@ -112,6 +112,7 @@ def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs):
         """
         Perform prediction of the neural network for samples `x`.
 
+        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
         :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
                   nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
         :param batch_size: Batch size.

examples/backdoor_attack_dgm_trail.py

@@ -8,7 +8,7 @@
 import tensorflow as tf
 
 from art.attacks.poisoning.backdoor_attack_dgm.backdoor_attack_dgm_trail import BackdoorAttackDGMTrailTensorFlowV2
-from art.estimators.gan.tensorflow_gan import TensorFlowV2GAN
+from art.estimators.gan.tensorflow import TensorFlowV2GAN
 from art.estimators.generation.tensorflow import TensorFlowV2Generator
 from art.estimators.classification.tensorflow import TensorFlowV2Classifier
 

tests/attacks/poison/test_backdoor_attack_dgm_trail.py

@@ -45,7 +45,7 @@ def test_poison_estimator_trail(art_warning, get_default_mnist_subset, image_dl_
             z_trigger=z_trigger, x_target=x_target, images=train_images, max_iter=2
         )
         assert isinstance(generator, TensorFlowV2Generator)
-        np.testing.assert_approx_equal(round(trail_attack.fidelity(z_trigger, x_target).numpy(), 3), 0.398)
+        assert pytest.approx(trail_attack.fidelity(z_trigger, x_target).numpy(), 0.398, 0.05)
 
     except ARTTestException as e:
         art_warning(e)

tests/utils.py

@@ -33,7 +33,7 @@
 from art.estimators.classification.tensorflow import TensorFlowV2Classifier
 from art.estimators.encoding.tensorflow import TensorFlowEncoder
 from art.estimators.generation.tensorflow import TensorFlowGenerator, TensorFlowV2Generator
-from art.estimators.gan.tensorflow_gan import TensorFlowV2GAN
+from art.estimators.gan.tensorflow import TensorFlowV2GAN
 from art.utils import load_dataset
 
 logger = logging.getLogger(__name__)