Merge branch 'dev_1.8.0' into update-component-yaml

Author: beat-buesser

.github/workflows/ci-style-checks.yml

@@ -51,7 +51,7 @@ jobs:
         run: mypy art
       - name: pytest-flake8
         if: ${{ always() }}
-        run: pytest --flake8 -v -m flake8
+        run: pytest --flake8 -v -m flake8 --ignore=contrib
       - name: black
         if: ${{ always() }}
         run: |

art/__init__.py

@@ -12,7 +12,7 @@
 from art import preprocessing
 
 # Semantic Version
-__version__ = "1.7.1"
+__version__ = "1.8.0.dev0"
 
 # pylint: disable=C0103
 

art/attacks/inference/attribute_inference/meminf_based.py

@@ -96,11 +96,11 @@ def infer(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.n
             if y.shape[0] != x.shape[0]:
                 raise ValueError("Number of rows in x and y do not match")
 
-        # assumes single index
+        # single index
         if isinstance(self.attack_feature, int):
             first = True
             for value in values:
-                v_full = np.full((x.shape[0], 1), value).astype(np.float32)
+                v_full = np.full((x.shape[0], 1), value).astype(x.dtype)
                 x_value = np.concatenate((x[:, : self.attack_feature], v_full), axis=1)
                 x_value = np.concatenate((x_value, x[:, self.attack_feature :]), axis=1)
 
@@ -112,7 +112,7 @@ def infer(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.n
                     probabilities = np.hstack((probabilities, predicted))
 
             # needs to be of type float so we can later replace back the actual values
-            value_indexes = np.argmax(probabilities, axis=1).astype(np.float32)
+            value_indexes = np.argmax(probabilities, axis=1).astype(x.dtype)
             pred_values = np.zeros_like(value_indexes)
             for index, value in enumerate(values):
                 pred_values[value_indexes == index] = value
@@ -134,7 +134,7 @@ def infer(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.n
                 else:
                     probabilities = np.hstack((probabilities, predicted))
                 first = False
-            value_indexes = np.argmax(probabilities, axis=1).astype(np.float32)
+            value_indexes = np.argmax(probabilities, axis=1).astype(x.dtype)
             pred_values = np.zeros_like(probabilities)
             for index, value in enumerate(values):
                 curr_value = np.zeros(len(values))

art/attacks/inference/attribute_inference/white_box_decision_tree.py

@@ -98,7 +98,7 @@ def infer(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.n
 
         for i, value in enumerate(values):
             # prepare data with the given value in the attacked feature
-            v_full = np.full((n_samples, 1), value)
+            v_full = np.full((n_samples, 1), value).astype(x.dtype)
             x_value = np.concatenate((x[:, : self.attack_feature], v_full), axis=1)
             x_value = np.concatenate((x_value, x[:, self.attack_feature :]), axis=1)
 

art/attacks/inference/attribute_inference/white_box_lifestyle_decision_tree.py

@@ -96,7 +96,7 @@ def infer(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.n
 
         for i, value in enumerate(values):
             # prepare data with the given value in the attacked feature
-            v_full = np.full((n_samples, 1), value)
+            v_full = np.full((n_samples, 1), value).astype(x.dtype)
             x_value = np.concatenate((x[:, : self.attack_feature], v_full), axis=1)
             x_value = np.concatenate((x_value, x[:, self.attack_feature :]), axis=1)
 
@@ -117,7 +117,7 @@ def infer(self, x: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> np.n
     def _calculate_phi(self, x, values, n_samples):
         phi = []
         for value in values:
-            v_full = np.full((n_samples, 1), value)
+            v_full = np.full((n_samples, 1), value).astype(x.dtype)
             x_value = np.concatenate((x[:, : self.attack_feature], v_full), axis=1)
             x_value = np.concatenate((x_value, x[:, self.attack_feature :]), axis=1)
             nodes_value = {}

art/estimators/speech_recognition/tensorflow_lingvo.py

@@ -95,7 +95,8 @@ class TensorFlowLingvoASR(SpeechRecognizerMixin, TensorFlowV2Estimator):
         },
         "decoder": {
             "uri": (
-                "https://raw.githubusercontent.com/hesseltuinhof/lingvo/qin_patched_decoder/lingvo/tasks/asr/decoder.py"
+                "https://raw.githubusercontent.com/Trusted-AI/adversarial-robustness-toolbox/"
+                "4dabf5fcfb55502316ad48abbdc1a26033db1da5/contrib/lingvo-patched-decoder.py"
             ),
             "basename": "decoder_patched.py",
         },