Merge branch 'dev_1.17.0' into hf_notebook_dev

art/attacks/evasion/__init__.py

@@ -18,6 +18,7 @@
     from art.attacks.evasion.brendel_bethge import BrendelBethgeAttack
 
 from art.attacks.evasion.boundary import BoundaryAttack
+from art.attacks.evasion.composite_adversarial_attack import CompositeAdversarialAttackPyTorch
 from art.attacks.evasion.carlini import CarliniL2Method, CarliniLInfMethod, CarliniL0Method
 from art.attacks.evasion.decision_tree_attack import DecisionTreeAttack
 from art.attacks.evasion.deepfool import DeepFool

art/attacks/extraction/knockoff_nets.py

@@ -155,7 +155,7 @@ def _random_extraction(self, x: np.ndarray, thieved_classifier: "CLASSIFIER_TYPE
             y=fake_labels,
             batch_size=self.batch_size_fit,
             nb_epochs=self.nb_epochs,
-            verbose=0,
+            verbose=False,
         )
 
         return thieved_classifier
@@ -243,7 +243,7 @@ def _adaptive_extraction(
                 y=fake_label,
                 batch_size=self.batch_size_fit,
                 nb_epochs=1,
-                verbose=0,
+                verbose=False,
             )
 
             # Test new labels

art/attacks/poisoning/sleeper_agent_attack.py

@@ -360,7 +360,7 @@ def _create_model(
             for layer in model_pt.model.children():
                 if hasattr(layer, "reset_parameters"):
                     layer.reset_parameters()  # type: ignore
-            model_pt.fit(x_train, y_train, batch_size=batch_size, nb_epochs=epochs, verbose=1)
+            model_pt.fit(x_train, y_train, batch_size=batch_size, nb_epochs=epochs, verbose=True)
             predictions = model_pt.predict(x_test)
             accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
             logger.info("Accuracy of retrained model : %s", accuracy * 100.0)
@@ -370,7 +370,7 @@ def _create_model(
 
             self.substitute_classifier.model.trainable = True
             model_tf = self.substitute_classifier.clone_for_refitting()
-            model_tf.fit(x_train, y_train, batch_size=batch_size, nb_epochs=epochs, verbose=0)
+            model_tf.fit(x_train, y_train, batch_size=batch_size, nb_epochs=epochs, verbose=False)
             predictions = model_tf.predict(x_test)
             accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test)
             logger.info("Accuracy of retrained model : %s", accuracy * 100.0)

art/defences/detector/poison/activation_defence.py

@@ -695,7 +695,9 @@ def _get_activations(self, x_train: Optional[np.ndarray] = None) -> np.ndarray:
 
         # wrong way to get activations activations = self.classifier.predict(self.x_train)
         if isinstance(activations, np.ndarray):
-            nodes_last_layer = np.shape(activations)[1]
+            # flatten activations across batch
+            activations = np.reshape(activations, (activations.shape[0], -1))
+            nodes_last_layer = activations.shape[1]
         else:
             raise ValueError("activations is None or tensor.")
 

art/defences/detector/poison/spectral_signature_defense.py

@@ -121,6 +121,8 @@ def detect_poison(self, **kwargs) -> Tuple[dict, List[int]]:
             raise ValueError("Wrong type detected.")
 
         if features_x_poisoned is not None:
+            # flatten activations across batch
+            features_x_poisoned = np.reshape(features_x_poisoned, (features_x_poisoned.shape[0], -1))
             features_split = segment_by_class(features_x_poisoned, self.y_train, self.classifier.nb_classes)
         else:
             raise ValueError("Activation are `None`.")

art/defences/trainer/adversarial_trainer.py

@@ -188,7 +188,9 @@ def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwarg
                     x_batch[adv_ids] = x_adv
 
                 # Fit batch
-                self._classifier.fit(x_batch, y_batch, nb_epochs=1, batch_size=x_batch.shape[0], verbose=0, **kwargs)
+                self._classifier.fit(
+                    x_batch, y_batch, nb_epochs=1, batch_size=x_batch.shape[0], verbose=False, **kwargs
+                )
                 attack_id = (attack_id + 1) % len(self.attacks)
 
     def fit(  # pylint: disable=W0221
@@ -260,7 +262,9 @@ def fit(  # pylint: disable=W0221
                     x_batch[adv_ids] = x_adv
 
                 # Fit batch
-                self._classifier.fit(x_batch, y_batch, nb_epochs=1, batch_size=x_batch.shape[0], verbose=0, **kwargs)
+                self._classifier.fit(
+                    x_batch, y_batch, nb_epochs=1, batch_size=x_batch.shape[0], verbose=False, **kwargs
+                )
                 attack_id = (attack_id + 1) % len(self.attacks)
 
     def predict(self, x: np.ndarray, **kwargs) -> np.ndarray:

art/defences/trainer/dp_instahide_trainer.py

@@ -155,7 +155,7 @@ def fit(  # pylint: disable=W0221
                 x_aug = self._generate_noise(x_aug)
 
                 # fit batch
-                self._classifier.fit(x_aug, y_aug, nb_epochs=1, batch_size=x_aug.shape[0], verbose=0, **kwargs)
+                self._classifier.fit(x_aug, y_aug, nb_epochs=1, batch_size=x_aug.shape[0], verbose=False, **kwargs)
 
                 # get metrics
                 loss = self._classifier.compute_loss(x_aug, y_aug, reduction="mean")
@@ -234,7 +234,7 @@ def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwarg
                 x_aug = self._generate_noise(x_aug)
 
                 # fit batch
-                self._classifier.fit(x_aug, y_aug, nb_epochs=1, batch_size=x_aug.shape[0], verbose=0, **kwargs)
+                self._classifier.fit(x_aug, y_aug, nb_epochs=1, batch_size=x_aug.shape[0], verbose=False, **kwargs)
 
                 # get metrics
                 loss = self._classifier.compute_loss(x_aug, y_aug, reduction="mean")

art/estimators/certification/derandomized_smoothing/pytorch.py

@@ -438,7 +438,7 @@ def fit(  # pylint: disable=W0221
         training_mode: bool = True,
         drop_last: bool = False,
         scheduler: Optional[Any] = None,
-        verbose: Optional[Union[bool, int]] = None,
+        verbose: bool = False,
         update_batchnorm: bool = True,
         batchnorm_update_epochs: int = 1,
         transform: Optional["torchvision.transforms.transforms.Compose"] = None,
@@ -457,7 +457,7 @@ def fit(  # pylint: disable=W0221
                           the batch size. If ``False`` and the size of dataset is not divisible by the batch size, then
                           the last batch will be smaller. (default: ``False``)
         :param scheduler: Learning rate scheduler to run at the start of every epoch.
-        :param verbose: if to display training progress bars
+        :param verbose: Display training progress bar.
         :param update_batchnorm: ViT specific argument.
                                  If to run the training data through the model to update any batch norm statistics prior
                                  to training. Useful on small datasets when using pre-trained ViTs.
@@ -469,8 +469,6 @@ def fit(  # pylint: disable=W0221
         """
         import torch
 
-        display_pb = self.process_verbose(verbose)
-
         # Set model mode
         self._model.train(mode=training_mode)
 
@@ -501,7 +499,7 @@ def fit(  # pylint: disable=W0221
             epoch_loss = []
             epoch_batch_sizes = []
 
-            pbar = tqdm(range(num_batch), disable=not display_pb)
+            pbar = tqdm(range(num_batch), disable=not verbose)
 
             # Train for one epoch
             for m in pbar:
@@ -547,7 +545,7 @@ def fit(  # pylint: disable=W0221
                 epoch_loss.append(loss.cpu().detach().numpy())
                 epoch_batch_sizes.append(len(i_batch))
 
-                if display_pb:
+                if verbose:
                     pbar.set_description(
                         f"Loss {np.average(epoch_loss, weights=epoch_batch_sizes):.3f} "
                         f"Acc {np.average(epoch_acc, weights=epoch_batch_sizes):.3f} "

art/estimators/certification/derandomized_smoothing/tensorflow.py

@@ -160,7 +160,7 @@ def fit(  # pylint: disable=W0221
         y: np.ndarray,
         batch_size: int = 128,
         nb_epochs: int = 10,
-        verbose: Optional[Union[bool, int]] = None,
+        verbose: bool = False,
         **kwargs,
     ) -> None:
         """
@@ -171,15 +171,13 @@ def fit(  # pylint: disable=W0221
                   shape (nb_samples,).
         :param batch_size: Size of batches.
         :param nb_epochs: Number of epochs to use for training.
-        :param verbose: If to display training progress bars
+        :param verbose: Display training progress bar.
         :param kwargs: Dictionary of framework-specific arguments. This parameter currently only supports
                        "scheduler" which is an optional function that will be called at the end of every
                        epoch to adjust the learning rate.
         """
         import tensorflow as tf
 
-        display_pb = self.process_verbose(verbose)
-
         if self._train_step is None:  # pragma: no cover
             if self._loss_object is None:  # pragma: no cover
                 raise TypeError(
@@ -222,7 +220,7 @@ def train_step(model, images, labels):
             epoch_loss = []
             epoch_batch_sizes = []
 
-            pbar = tqdm(range(num_batch), disable=not display_pb)
+            pbar = tqdm(range(num_batch), disable=not verbose)
 
             ind = np.arange(len(x_preprocessed))
             for m in pbar:
@@ -239,7 +237,7 @@ def train_step(model, images, labels):
                 else:
                     train_step(self.model, images, labels)
 
-                if display_pb:
+                if verbose:
                     if self._train_step is None:
                         pbar.set_description(
                             f"Loss {np.average(epoch_loss, weights=epoch_batch_sizes):.3f} "

art/estimators/certification/randomized_smoothing/macer/pytorch.py

@@ -75,7 +75,6 @@ def __init__(
         gamma: float = 8.0,
         lmbda: float = 12.0,
         gaussian_samples: int = 16,
-        verbose: bool = False,
     ) -> None:
         """
         Create a MACER classifier.
@@ -105,7 +104,6 @@ def __init__(
         :param gamma: The hinge factor.
         :param lmbda: The trade-off factor.
         :param gaussian_samples: The number of gaussian samples per input.
-        :param verbose: Show progress bars.
         """
         super().__init__(
             model=model,
@@ -122,7 +120,6 @@ def __init__(
             sample_size=sample_size,
             scale=scale,
             alpha=alpha,
-            verbose=verbose,
         )
         self.beta = beta
         self.gamma = gamma
@@ -138,7 +135,7 @@ def fit(  # pylint: disable=W0221
         training_mode: bool = True,
         drop_last: bool = False,
         scheduler: Optional["torch.optim.lr_scheduler._LRScheduler"] = None,
-        verbose: Optional[Union[bool, int]] = None,
+        verbose: bool = False,
         **kwargs,
     ) -> None:
         """
@@ -154,17 +151,14 @@ def fit(  # pylint: disable=W0221
                           the batch size. If ``False`` and the size of dataset is not divisible by the batch size, then
                           the last batch will be smaller. (default: ``False``)
         :param scheduler: Learning rate scheduler to run at the start of every epoch.
-        :param verbose: (Optional) Display the progress bar, if not supplied will revert to the verbose level when
-                        class was initialised.
+        :param verbose: Display the training progress bar.
         :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
                and providing it takes no effect.
         """
         import torch
         import torch.nn.functional as F
         from torch.utils.data import TensorDataset, DataLoader
 
-        display_pb = self.process_verbose(verbose)
-
         # Set model mode
         self._model.train(mode=training_mode)
 
@@ -190,7 +184,7 @@ class was initialised.
         )
 
         # Start training
-        for _ in trange(nb_epochs, disable=not display_pb):
+        for _ in trange(nb_epochs, disable=not verbose):
             for x_batch, y_batch in dataloader:
                 # Move inputs to GPU
                 x_batch = x_batch.to(self.device)

art/estimators/certification/randomized_smoothing/macer/tensorflow.py

@@ -75,7 +75,6 @@ def __init__(
         gamma: float = 8.0,
         lmbda: float = 12.0,
         gaussian_samples: int = 16,
-        verbose: bool = False,
     ) -> None:
         """
         Create a MACER classifier.
@@ -108,7 +107,6 @@ def __init__(
         :param gamma: The hinge factor.
         :param lmbda: The trade-off factor.
         :param gaussian_samples: The number of gaussian samples per input.
-        :param verbose: Show progress bars.
         """
         super().__init__(
             model=model,
@@ -125,21 +123,14 @@ def __init__(
             sample_size=sample_size,
             scale=scale,
             alpha=alpha,
-            verbose=verbose,
         )
         self.beta = beta
         self.gamma = gamma
         self.lmbda = lmbda
         self.gaussian_samples = gaussian_samples
 
     def fit(
-        self,
-        x: np.ndarray,
-        y: np.ndarray,
-        batch_size: int = 128,
-        nb_epochs: int = 10,
-        verbose: Optional[Union[bool, int]] = None,
-        **kwargs
+        self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 10, verbose: bool = False, **kwargs
     ) -> None:
         """
         Fit the classifier on the training set `(x, y)`.
@@ -149,16 +140,13 @@ def fit(
                   shape (nb_samples,).
         :param batch_size: Size of batches.
         :param nb_epochs: Number of epochs to use for training.
-        :param verbose: (Optional) Display the progress bar, if not supplied will revert to the verbose level when
-                        class was initialised.
+        :param verbose: Display the training progress bar.
         :param kwargs: Dictionary of framework-specific arguments. This parameter currently only supports
                        "scheduler" which is an optional function that will be called at the end of every
                        epoch to adjust the learning rate.
         """
         import tensorflow as tf
 
-        display_pb = self.process_verbose(verbose)
-
         if self._train_step is None:  # pragma: no cover
             if self._optimizer is None:  # pragma: no cover
                 raise ValueError(
@@ -225,7 +213,7 @@ def train_step(model, images, labels):
 
         train_ds = tf.data.Dataset.from_tensor_slices((x_preprocessed, y_preprocessed)).shuffle(10000).batch(batch_size)
 
-        for epoch in trange(nb_epochs, disable=not display_pb):
+        for epoch in trange(nb_epochs, disable=not verbose):
             for images, labels in train_ds:
                 # Tile samples for Gaussian augmentation
                 input_size = len(images)