apply black format

Signed-off-by: Ngoc Minh Tran <[email protected]>

Author: minhitbk

art/attacks/evasion/imperceptible_asr/imperceptible_asr_pytorch.py

@@ -451,7 +451,9 @@ 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
@@ -591,7 +593,10 @@ 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

@@ -258,7 +258,11 @@ 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(
@@ -324,7 +328,7 @@ def predict(
         # Aggregate results
         result_outputs = np.zeros(
             shape=(x_preprocessed.shape[0], result_output_sizes.max(), results[0].shape[-1]),
-            dtype=config.ART_NUMPY_DTYPE
+            dtype=config.ART_NUMPY_DTYPE,
         )
 
         for m in range(num_batch):
@@ -517,7 +521,10 @@ 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
@@ -548,7 +555,11 @@ 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

tests/attacks/evasion/test_imperceptible_asr_pytorch.py

@@ -53,7 +53,7 @@ def test_imperceptible_asr_pytorch(art_warning, expected_values, use_amp, device
             [
                 np.array(x1 * 100, dtype=ART_NUMPY_DTYPE),
                 np.array(x2 * 100, dtype=ART_NUMPY_DTYPE),
-                np.array(x3 * 100, dtype=ART_NUMPY_DTYPE)
+                np.array(x3 * 100, dtype=ART_NUMPY_DTYPE),
             ]
         )
 
@@ -71,7 +71,7 @@ def test_imperceptible_asr_pytorch(art_warning, expected_values, use_amp, device
             pretrained_model="librispeech",
             device_type=device_type,
             use_amp=use_amp,
-            preprocessing_defences=audio_filter
+            preprocessing_defences=audio_filter,
         )
 
         # Create attack

tests/estimators/speech_recognition/test_pytorch_deep_speech.py

@@ -58,7 +58,7 @@ def test_pytorch_deep_speech(art_warning, expected_values, use_amp, device_type)
             [
                 np.array(x1 * 100, dtype=ART_NUMPY_DTYPE),
                 np.array(x2 * 100, dtype=ART_NUMPY_DTYPE),
-                np.array(x3 * 100, dtype=ART_NUMPY_DTYPE)
+                np.array(x3 * 100, dtype=ART_NUMPY_DTYPE),
             ]
         )
 

tests/preprocessing/test_l_filter_pytorch.py

@@ -49,7 +49,7 @@ def test_audio_filter(fir_filter, art_warning, expected_values):
             [
                 np.array(x1 * 2, dtype=ART_NUMPY_DTYPE),
                 np.array(x2 * 2, dtype=ART_NUMPY_DTYPE),
-                np.array(x3 * 2, dtype=ART_NUMPY_DTYPE)
+                np.array(x3 * 2, dtype=ART_NUMPY_DTYPE),
             ]
         )