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

Author: Beat Buesser

art/attacks/evasion/imperceptible_asr/imperceptible_asr_pytorch.py

@@ -451,9 +451,7 @@ def _forward_1st_stage(
 
         # Transform data into the model input space
         inputs, targets, input_rates, target_sizes, batch_idx = self.estimator.preprocess_transform_model_input(
-            x=masked_adv_input.to(self.estimator.device),
-            y=original_output,
-            real_lengths=real_lengths,
+            x=masked_adv_input.to(self.estimator.device), y=original_output, real_lengths=real_lengths,
         )
 
         # Compute real input sizes
@@ -593,10 +591,7 @@ class only supports targeted attack.
         return result
 
     def _forward_2nd_stage(
-        self,
-        local_delta_rescale: "torch.Tensor",
-        theta_batch: np.ndarray,
-        original_max_psd_batch: np.ndarray,
+        self, local_delta_rescale: "torch.Tensor", theta_batch: np.ndarray, original_max_psd_batch: np.ndarray,
     ) -> "torch.Tensor":
         """
         The forward pass of the second stage of the attack.

art/estimators/speech_recognition/pytorch_deep_speech.py

@@ -258,11 +258,7 @@ def __init__(
                 enabled = True
 
             self._model, self._optimizer = amp.initialize(
-                models=self._model,
-                optimizers=self._optimizer,
-                enabled=enabled,
-                opt_level=opt_level,
-                loss_scale=1.0,
+                models=self._model, optimizers=self._optimizer, enabled=enabled, opt_level=opt_level, loss_scale=1.0,
             )
 
     def predict(
@@ -521,10 +517,7 @@ def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: in
                 self._optimizer.step()
 
     def preprocess_transform_model_input(
-        self,
-        x: "torch.Tensor",
-        y: np.ndarray,
-        real_lengths: np.ndarray,
+        self, x: "torch.Tensor", y: np.ndarray, real_lengths: np.ndarray,
     ) -> Tuple["torch.Tensor", "torch.Tensor", "torch.Tensor", "torch.Tensor", List]:
         """
         Apply preprocessing and then transform the user input space into the model input space. This function is used
@@ -555,11 +548,7 @@ def preprocess_transform_model_input(
 
         # Transform the input space
         inputs, targets, input_rates, target_sizes, batch_idx = self._transform_model_input(
-            x=x,
-            y=y,
-            compute_gradient=False,
-            tensor_input=True,
-            real_lengths=real_lengths,
+            x=x, y=y, compute_gradient=False, tensor_input=True, real_lengths=real_lengths,
         )
 
         return inputs, targets, input_rates, target_sizes, batch_idx