Update imports

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

Author: Beat Buesser

art/__init__.py

@@ -4,11 +4,11 @@
 import logging.config
 
 # Project Imports
-# from art import attacks
+from art import attacks
 from art import defences
-# from art import estimators
-# from art import metrics
-# from art import wrappers
+from art import estimators
+from art import metrics
+from art import wrappers
 
 # Semantic Version
 __version__ = "1.4.0"

art/attacks/evasion/auto_projected_gradient_descent.py

@@ -28,7 +28,7 @@
 from tqdm.auto import trange
 
 from art.config import ART_NUMPY_DTYPE
-from art.attacks import EvasionAttack
+from art.attacks.attack import EvasionAttack
 from art.estimators.estimator import BaseEstimator, LossGradientsMixin
 from art.estimators.classification.classifier import ClassifierMixin
 from art.utils import check_and_transform_label_format, projection, random_sphere, is_probability, get_labels_np_array

art/attacks/inference/__init__.py

@@ -1,3 +1,6 @@
 """
 Module providing inference attacks.
 """
+from art.attacks.inference import attribute_inference
+from art.attacks.inference import membership_inference
+from art.attacks.inference import model_inversion

art/defences/detector/__init__.py

@@ -1,5 +1,5 @@
 """
 Module implementing detector-based defences against adversarial attacks.
 """
-from art.defences.transformer import evasion
-from art.defences.transformer import poisoning
+from art.defences.detector import evasion
+from art.defences.detector import poisoning

art/defences/detector/evasion/__init__.py

@@ -1,10 +1,6 @@
 """
 Module implementing detector-based defences against evasion attacks.
 """
-from art.defences.detector.evasion.detector import (
-    BinaryInputDetector,
-    BinaryActivationDetector,
-)
-from art.defences.detector.evasion.subsetscanning.scanningops import ScanningOps
-from art.defences.detector.evasion.subsetscanning.scanner import Scanner
-from art.defences.detector.evasion.subsetscanning.detector import SubsetScanningDetector
+from art.defences.detector.evasion import subsetscanning
+
+from art.defences.detector.evasion.detector import BinaryInputDetector, BinaryActivationDetector

art/defences/detector/evasion/detector.py

@@ -26,23 +26,25 @@
 
 import numpy as np
 
-from art.estimators.classification.classifier import ClassifierNeuralNetwork
+from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin, LossGradientsMixin
+from art.estimators.classification.classifier import ClassifierMixin, ClassGradientsMixin
 from art.utils import deprecated
 
 if TYPE_CHECKING:
     from art.utils import CLIP_VALUES_TYPE
     from art.data_generators import DataGenerator
+    from art.estimators.classification.classifier import ClassifierNeuralNetwork
 
 logger = logging.getLogger(__name__)
 
 
-class BinaryInputDetector(ClassifierNeuralNetwork):
+class BinaryInputDetector(ClassGradientsMixin, ClassifierMixin, LossGradientsMixin, NeuralNetworkMixin, BaseEstimator):
     """
     Binary detector of adversarial samples coming from evasion attacks. The detector uses an architecture provided by
     the user and trains it on data labeled as clean (label 0) or adversarial (label 1).
     """
 
-    def __init__(self, detector: ClassifierNeuralNetwork) -> None:
+    def __init__(self, detector: "ClassifierNeuralNetwork") -> None:
         """
         Create a `BinaryInputDetector` instance which performs binary classification on input data.
 
@@ -155,14 +157,14 @@ def save(self, filename: str, path: Optional[str] = None) -> None:
         self.detector.save(filename, path)
 
 
-class BinaryActivationDetector(ClassifierNeuralNetwork):
+class BinaryActivationDetector(ClassGradientsMixin, ClassifierMixin, LossGradientsMixin, NeuralNetworkMixin, BaseEstimator):
     """
     Binary detector of adversarial samples coming from evasion attacks. The detector uses an architecture provided by
     the user and is trained on the values of the activations of a classifier at a given layer.
     """
 
     def __init__(
-        self, classifier: ClassifierNeuralNetwork, detector: ClassifierNeuralNetwork, layer: Union[int, str],
+        self, classifier: "ClassifierNeuralNetwork", detector: "ClassifierNeuralNetwork", layer: Union[int, str],
     ) -> None:  # lgtm [py/similar-function]
         """
         Create a `BinaryActivationDetector` instance which performs binary classification on activation information.

art/defences/detector/evasion/subsetscanning/__init__.py

@@ -1,3 +1,6 @@
 """
 This module implements the fast generalized subset scan based detector.
 """
+from art.defences.detector.evasion.subsetscanning.scanningops import ScanningOps
+from art.defences.detector.evasion.subsetscanning.scanner import Scanner
+from art.defences.detector.evasion.subsetscanning.detector import SubsetScanningDetector

art/estimators/estimator.py

@@ -25,15 +25,15 @@
 from tqdm import trange
 
 from art.config import ART_NUMPY_DTYPE
-from art.defences.postprocessor.postprocessor import Postprocessor
-from art.defences.preprocessor.preprocessor import Preprocessor
 from art.utils import Deprecated, deprecated, deprecated_keyword_arg
 
 if TYPE_CHECKING:
     # pylint: disable=R0401
     from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
     from art.data_generators import DataGenerator
     from art.metrics.verification_decisions_trees import Tree
+    from art.defences.postprocessor.postprocessor import Postprocessor
+    from art.defences.preprocessor.preprocessor import Preprocessor
 
 
 class BaseEstimator(ABC):
@@ -54,8 +54,8 @@ def __init__(
         self,
         model=None,
         clip_values: Optional["CLIP_VALUES_TYPE"] = None,
-        preprocessing_defences: Union[Preprocessor, List[Preprocessor], None] = None,
-        postprocessing_defences: Union[Postprocessor, List[Postprocessor], None] = None,
+        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
+        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
         preprocessing: "PREPROCESSING_TYPE" = (0, 1),
     ):
         """
@@ -75,14 +75,14 @@ def __init__(
         self._model = model
         self._clip_values = clip_values
 
-        self.preprocessing_defences: Optional[List[Preprocessor]]
-        if isinstance(preprocessing_defences, Preprocessor):
+        self.preprocessing_defences: Optional[List["Preprocessor"]]
+        if isinstance(preprocessing_defences, "Preprocessor"):
             self.preprocessing_defences = [preprocessing_defences]
         else:
             self.preprocessing_defences = preprocessing_defences
 
-        self.postprocessing_defences: Optional[List[Postprocessor]]
-        if isinstance(postprocessing_defences, Postprocessor):
+        self.postprocessing_defences: Optional[List["Postprocessor"]]
+        if isinstance(postprocessing_defences, "Postprocessor"):
             self.postprocessing_defences = [postprocessing_defences]
         else:
             self.postprocessing_defences = postprocessing_defences
@@ -133,7 +133,7 @@ def _check_params(self) -> None:
 
         if isinstance(self.preprocessing_defences, list):
             for preproc_defence in self.preprocessing_defences:
-                if not isinstance(preproc_defence, Preprocessor):
+                if not isinstance(preproc_defence, "Preprocessor"):
                     raise ValueError(
                         "All preprocessing defences have to be instance of "
                         "art.defences.preprocessor.preprocessor.Preprocessor."
@@ -147,7 +147,7 @@ def _check_params(self) -> None:
             )
         if isinstance(self.postprocessing_defences, list):
             for postproc_defence in self.postprocessing_defences:
-                if not isinstance(postproc_defence, Postprocessor):
+                if not isinstance(postproc_defence, "Postprocessor"):
                     raise ValueError(
                         "All postprocessing defences have to be instance of "
                         "art.defences.postprocessor.postprocessor.Postprocessor."

art/estimators/pytorch.py

@@ -28,7 +28,6 @@
     LossGradientsMixin,
     NeuralNetworkMixin,
 )
-from art.defences.preprocessor.preprocessor import PreprocessorPyTorch
 
 logger = logging.getLogger(__name__)
 
@@ -124,6 +123,7 @@ def _apply_preprocessing_defences(self, x, y, fit: bool = False) -> Tuple[Any, A
         :rtype: Format as expected by the `model`
         """
         import torch
+        from art.defences.preprocessor.preprocessor import PreprocessorPyTorch
 
         if (
             not hasattr(self, "preprocessing_defences")
@@ -184,6 +184,7 @@ def _apply_preprocessing_defences_gradient(self, x, gradients, fit=False):
         :rtype: Format as expected by the `model`
         """
         import torch
+        from art.defences.preprocessor.preprocessor import PreprocessorPyTorch
 
         if (
             not hasattr(self, "preprocessing_defences")

art/estimators/tensorflow.py

@@ -29,7 +29,6 @@
     LossGradientsMixin,
     NeuralNetworkMixin,
 )
-from art.defences.preprocessor.preprocessor import PreprocessorTensorFlowV2
 
 if TYPE_CHECKING:
     import tensorflow as tf
@@ -170,6 +169,7 @@ def _apply_preprocessing_defences(self, x, y, fit: bool = False) -> Tuple[Any, A
         :rtype: Format as expected by the `model`
         """
         import tensorflow as tf  # lgtm [py/repeated-import]
+        from art.defences.preprocessor.preprocessor import PreprocessorTensorFlowV2
 
         if (
             not hasattr(self, "preprocessing_defences")
@@ -229,6 +229,7 @@ def _apply_preprocessing_defences_gradient(self, x, gradients, fit=False):
         :rtype: Format as expected by the `model`
         """
         import tensorflow as tf  # lgtm [py/repeated-import]
+        from art.defences.preprocessor.preprocessor import PreprocessorTensorFlowV2
 
         if (
             not hasattr(self, "preprocessing_defences")