Merge pull request #839 from Trusted-AI/feature/broadcastable-standardization-channels-first

Ensure that mean and standard deviation are broadcastable in standardization preprocessors

Author: beat-buesser

art/estimators/certification/randomized_smoothing/pytorch.py

@@ -63,7 +63,7 @@ def __init__(
         clip_values: Optional["CLIP_VALUES_TYPE"] = None,
         preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
         postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
-        preprocessing: "PREPROCESSING_TYPE" = (0, 1),
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
         device_type: str = "gpu",
         sample_size: int = 32,
         scale: float = 0.1,

art/estimators/certification/randomized_smoothing/tensorflow.py

@@ -62,7 +62,7 @@ def __init__(
         clip_values: Optional["CLIP_VALUES_TYPE"] = None,
         preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
         postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
-        preprocessing: "PREPROCESSING_TYPE" = (0, 1),
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
         sample_size: int = 32,
         scale: float = 0.1,
         alpha: float = 0.001,

art/estimators/classification/GPy.py

@@ -52,7 +52,7 @@ def __init__(
         clip_values: Optional["CLIP_VALUES_TYPE"] = None,
         preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
         postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
-        preprocessing: "PREPROCESSING_TYPE" = (0, 1),
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
     ) -> None:
         """
         Create a `Classifier` instance GPY Gaussian Process classification models.

art/estimators/classification/blackbox.py

@@ -51,7 +51,7 @@ def __init__(
         clip_values: Optional["CLIP_VALUES_TYPE"] = None,
         preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
         postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
-        preprocessing: "PREPROCESSING_TYPE" = (0, 1),
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
     ):
         """
         Create a `Classifier` instance for a black-box model.

art/estimators/classification/catboost.py

@@ -53,7 +53,7 @@ def __init__(
         model: Optional["CatBoostClassifier"] = None,
         preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
         postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
-        preprocessing: "PREPROCESSING_TYPE" = (0, 1),
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
         clip_values: Optional["CLIP_VALUES_TYPE"] = None,
         nb_features: Optional[int] = None,
     ) -> None:

art/estimators/classification/detector_classifier.py

@@ -53,7 +53,7 @@ def __init__(
         detector: ClassifierNeuralNetwork,
         preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
         postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
-        preprocessing: "PREPROCESSING_TYPE" = (0, 1),
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
     ) -> None:
         """
         Initialization for the DetectorClassifier.

art/estimators/classification/ensemble.py

@@ -56,7 +56,7 @@ def __init__(
         clip_values: Optional["CLIP_VALUES_TYPE"] = None,
         preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
         postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
-        preprocessing: "PREPROCESSING_TYPE" = (0, 1),
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
     ) -> None:
         """
         Initialize a :class:`.EnsembleClassifier` object. The data range values and colour channel index have to

art/estimators/classification/keras.py

@@ -80,7 +80,7 @@ def __init__(
         clip_values: Optional["CLIP_VALUES_TYPE"] = None,
         preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
         postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
-        preprocessing: "PREPROCESSING_TYPE" = (0, 1),
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
         input_layer: int = 0,
         output_layer: int = 0,
     ) -> None:

art/estimators/classification/lightgbm.py

@@ -54,7 +54,7 @@ def __init__(
         clip_values: Optional["CLIP_VALUES_TYPE"] = None,
         preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
         postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
-        preprocessing: "PREPROCESSING_TYPE" = (0, 1),
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
     ) -> None:
         """
         Create a `Classifier` instance from a LightGBM model.

art/estimators/classification/mxnet.py

@@ -67,7 +67,7 @@ def __init__(
         clip_values: Optional["CLIP_VALUES_TYPE"] = None,
         preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
         postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
-        preprocessing: "PREPROCESSING_TYPE" = (0, 1),
+        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
     ) -> None:
         """
         Initialize an `MXClassifier` object. Assumes the `model` passed as parameter is a Gluon model.