Merge remote-tracking branch 'origin/main' into dependabot/pip/scikit-learn-1.6.0

beat-buesser · beat-buesser · commit ec977574969b · 2025-05-22T11:49:17.000+02:00
diff --git a/.github/workflows/ci-huggingface.yml b/.github/workflows/ci-huggingface.yml
@@ -23,7 +23,7 @@ on:
 
 jobs:
   test:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-22.04
     strategy:
       fail-fast: false
       matrix:
diff --git a/.github/workflows/ci-keras.yml b/.github/workflows/ci-keras.yml
@@ -23,7 +23,7 @@ on:
 
 jobs:
   test:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-22.04
     strategy:
       fail-fast: false
       matrix:
diff --git a/.github/workflows/ci-legacy.yml b/.github/workflows/ci-legacy.yml
@@ -23,7 +23,7 @@ on:
 
 jobs:
   test:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-22.04
     strategy:
       fail-fast: false
       matrix:
diff --git a/.github/workflows/ci-lingvo.yml b/.github/workflows/ci-lingvo.yml
@@ -23,7 +23,7 @@ on:
 
 jobs:
   test:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-22.04
     strategy:
       fail-fast: false
       matrix:
diff --git a/.github/workflows/ci-mxnet.yml b/.github/workflows/ci-mxnet.yml
@@ -23,7 +23,7 @@ on:
 
 jobs:
   test:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-22.04
     strategy:
       fail-fast: false
       matrix:
diff --git a/.github/workflows/ci-pytorch-object-detectors.yml b/.github/workflows/ci-pytorch-object-detectors.yml
@@ -24,7 +24,7 @@ on:
 jobs:
   test_pytorch_fasterrcnn:
     name: PyTorchObjectDetectors
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-22.04
     strategy:
       fail-fast: false
     steps:
diff --git a/.github/workflows/ci-pytorch.yml b/.github/workflows/ci-pytorch.yml
@@ -23,7 +23,7 @@ on:
 
 jobs:
   test:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-22.04
     strategy:
       fail-fast: false
       matrix:
diff --git a/.github/workflows/ci-scikit-learn.yml b/.github/workflows/ci-scikit-learn.yml
@@ -23,7 +23,7 @@ on:
 
 jobs:
   test:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-22.04
     strategy:
       fail-fast: false
       matrix:
diff --git a/.github/workflows/ci-style-checks.yml b/.github/workflows/ci-style-checks.yml
@@ -39,7 +39,7 @@ jobs:
       - name: Install Dependencies
         run: |
           python -m pip install --upgrade pip setuptools wheel
-          pip install -q pylint==3.2.6 mypy==1.11.1 pycodestyle==2.12.0 black==24.4.2 ruff==0.5.5
+          pip install -r requirements_test.txt
           pip install -q -r <(sed '/^numpy/d;/^tensorflow/d;/^keras/d' requirements_test.txt)
           pip install numpy==1.22.4
           pip install tensorflow==2.13.1
diff --git a/.github/workflows/ci-tensorflow-v1.yml b/.github/workflows/ci-tensorflow-v1.yml
@@ -23,7 +23,7 @@ on:
 
 jobs:
   test:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-22.04
     strategy:
       fail-fast: false
       matrix:
@@ -48,7 +48,7 @@ jobs:
           sudo apt-get update
           sudo apt-get -y -q install ffmpeg libavcodec-extra
           python -m pip install --upgrade pip setuptools wheel
-          pip install -q -r <(sed '/^pandas/d;/^scipy/d;/^matplotlib/d;/^xgboost/d;/^tensorflow/d;/^keras/d;/^jax/d;/^torch/d;/^Pillow/d;/^h5py/d;/^kornia/d;/^scikit-learn/d;/^pytest-mock/d;/^GPy/d;/^lief/d;/^statsmodels/d;/^ultralytics/d;/^ipython/d;/^numba/d;/^pytest/d;/^pylint/d;/^mypy/d;/^pycodestyle/d;/^black/d;/^types-PyYAML/d;/^types-setuptools/d;/^requests/d' requirements_test.txt)
+          pip install -q -r <(sed '/^pandas/d;/^scipy/d;/^matplotlib/d;/^xgboost/d;/^tensorflow/d;/^keras/d;/^jax/d;/^torch/d;/^Pillow/d;/^h5py/d;/^kornia/d;/^scikit-learn/d;/^pytest-mock/d;/^GPy/d;/^lief/d;/^statsmodels/d;/^ultralytics/d;/^ipython/d;/^numba/d;/^pytest/d;/^pylint/d;/^mypy/d;/^pycodestyle/d;/^black/d;/^types-PyYAML/d;/^types-setuptools/d;/^requests/d;/^timm/d' requirements_test.txt)
           pip install pandas==1.3.5
           pip install scipy==1.7.2
           pip install matplotlib==3.5.3
@@ -59,7 +59,7 @@ jobs:
           pip install numpy==1.20
           pip install torch==1.13.1
           pip install torchaudio==0.13.1
-          pip install torchvision==0.14.1+cpu
+          pip install torchvision==0.14.1
           pip install Pillow==9.5.0
           pip install h5py==3.8.0
           pip install kornia==0.6.12
@@ -72,6 +72,7 @@ jobs:
           pip install pytest==7.4.4
           pip install pytest-cov
           pip install requests==2.31.0
+          pip install timm==0.9.12
           pip list
       - name: Run Tests
         run: ./run_tests.sh ${{ matrix.framework }}
diff --git a/.github/workflows/ci-tensorflow-v2.yml b/.github/workflows/ci-tensorflow-v2.yml
@@ -23,7 +23,7 @@ on:
 
 jobs:
   test:
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-22.04
     strategy:
       fail-fast: false
       matrix:
diff --git a/.github/workflows/dockerhub.yml b/.github/workflows/dockerhub.yml
@@ -23,22 +23,22 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Log in to Docker Hub
-        uses: docker/login-action@9780b0c442fbb1117ed29e0efdff1e18412f7567
+        uses: docker/login-action@74a5d142397b4f367a81961eba4e8cd7edddf772
         with:
           username: ${{ secrets.DOCKER_HUB_USERNAME }}
           password: ${{ secrets.DOCKER_HUB_ACCESS_TOKEN }}
 
       - name: Extract metadata (tags, labels) for Docker
         id: meta
-        uses: docker/metadata-action@369eb591f429131d6889c46b94e711f089e6ca96
+        uses: docker/metadata-action@902fa8ec7d6ecbf8d84d538b9b233a880e428804
         with:
           images: adversarialrobustnesstoolbox/releases
           tags: |
-            type=raw,value={{branch}}-1.19.0-{{sha}}
+            type=raw,value={{branch}}-1.19.1-{{sha}}
             type=semver,pattern={{version}}
 
       - name: Build and push Docker image
-        uses: docker/build-push-action@67a2d409c0a876cbe6b11854e3e25193efe4e62d
+        uses: docker/build-push-action@1dc73863535b631f98b2378be8619f83b136f4a0
         with:
           context: .
           push: true
diff --git a/art/__init__.py b/art/__init__.py
@@ -13,7 +13,7 @@
 from art import preprocessing
 
 # Semantic Version
-__version__ = "1.19.0"
+__version__ = "1.19.1"
 
 LOGGING = {
     "version": 1,
diff --git a/art/attacks/evasion/adversarial_patch/adversarial_patch_pytorch.py b/art/attacks/evasion/adversarial_patch/adversarial_patch_pytorch.py
@@ -26,7 +26,7 @@
 import logging
 import math
 from packaging.version import parse
-from typing import Any, TYPE_CHECKING
+from typing import Any, cast, TYPE_CHECKING
 
 import numpy as np
 from tqdm.auto import trange
@@ -41,7 +41,7 @@
 
     import torch
 
-    from art.utils import CLASSIFIER_NEURALNETWORK_TYPE
+    from art.utils import CLASSIFIER_NEURALNETWORK_TYPE, PYTORCH_OBJECT_DETECTOR_TYPE
 
 logger = logging.getLogger(__name__)
 
@@ -72,7 +72,7 @@ class AdversarialPatchPyTorch(EvasionAttack):
 
     def __init__(
         self,
-        estimator: "CLASSIFIER_NEURALNETWORK_TYPE",
+        estimator: "CLASSIFIER_NEURALNETWORK_TYPE | PYTORCH_OBJECT_DETECTOR_TYPE",
         rotation_max: float = 22.5,
         scale_min: float = 0.1,
         scale_max: float = 1.0,
@@ -91,7 +91,7 @@ def __init__(
         """
         Create an instance of the :class:`.AdversarialPatchPyTorch`.
 
-        :param estimator: A trained estimator.
+        :param estimator: A trained PyTorch estimator for classification or object detection.
         :param rotation_max: The maximum rotation applied to random patches. The value is expected to be in the
                range `[0, 180]`.
         :param scale_min: The minimum scaling applied to random patches. The value should be in the range `[0, 1]`,
@@ -183,7 +183,10 @@ def __init__(
             self._optimizer = torch.optim.Adam([self._patch], lr=self.learning_rate)
 
     def _train_step(
-        self, images: "torch.Tensor", target: "torch.Tensor", mask: "torch.Tensor" | None = None
+        self,
+        images: "torch.Tensor",
+        target: "torch.Tensor" | list[dict[str, "torch.Tensor"]],
+        mask: "torch.Tensor" | None = None,
     ) -> "torch.Tensor":
         import torch
 
@@ -227,7 +230,12 @@ def _predictions(
 
         return predictions, target
 
-    def _loss(self, images: "torch.Tensor", target: "torch.Tensor", mask: "torch.Tensor" | None) -> "torch.Tensor":
+    def _loss(
+        self,
+        images: "torch.Tensor",
+        target: "torch.Tensor" | list[dict[str, "torch.Tensor"]],
+        mask: "torch.Tensor" | None,
+    ) -> "torch.Tensor":
         import torch
 
         if isinstance(target, torch.Tensor):
@@ -475,13 +483,17 @@ def _random_overlay(
         return patched_images
 
     def generate(  # type: ignore
-        self, x: np.ndarray, y: np.ndarray | None = None, **kwargs
+        self, x: np.ndarray, y: np.ndarray | list[dict[str, np.ndarray | "torch.Tensor"]] | None = None, **kwargs
     ) -> tuple[np.ndarray, np.ndarray]:
         """
         Generate an adversarial patch and return the patch and its mask in arrays.
 
         :param x: An array with the original input images of shape NCHW or input videos of shape NFCHW.
-        :param y: An array with the original true labels.
+        :param y: True or target labels of format `list[dict[str, Union[np.ndarray, torch.Tensor]]]`, one for each
+                  input image. The fields of the dict are as follows:
+
+                  - boxes [N, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and 0 <= y1 < y2 <= H.
+                  - labels [N]: the labels for each image.
         :param mask: A boolean array of shape equal to the shape of a single samples (1, H, W) or the shape of `x`
                      (N, H, W) without their channel dimensions. Any features for which the mask is True can be the
                      center location of the patch during sampling.
@@ -499,12 +511,19 @@ def generate(  # type: ignore
         if self.patch_location is not None and mask is not None:
             raise ValueError("Masks can only be used if the `patch_location` is `None`.")
 
-        if y is None:  # pragma: no cover
-            logger.info("Setting labels to estimator predictions and running untargeted attack because `y=None`.")
-            y = to_categorical(np.argmax(self.estimator.predict(x=x), axis=1), nb_classes=self.estimator.nb_classes)
-
         if hasattr(self.estimator, "nb_classes"):
-            y = check_and_transform_label_format(labels=y, nb_classes=self.estimator.nb_classes)
+
+            y_array: np.ndarray
+
+            if y is None:  # pragma: no cover
+                logger.info("Setting labels to estimator classification predictions.")
+                y_array = to_categorical(
+                    np.argmax(self.estimator.predict(x=x), axis=1), nb_classes=self.estimator.nb_classes
+                )
+            else:
+                y_array = cast(np.ndarray, y)
+
+            y = check_and_transform_label_format(labels=y_array, nb_classes=self.estimator.nb_classes)
 
             # check if logits or probabilities
             y_pred = self.estimator.predict(x=x[[0]])
@@ -513,6 +532,10 @@ def generate(  # type: ignore
                 self.use_logits = False
             else:
                 self.use_logits = True
+        else:
+            if y is None:  # pragma: no cover
+                logger.info("Setting labels to estimator object detection predictions.")
+                y = self.estimator.predict(x=x)
 
         if isinstance(y, np.ndarray):
             x_tensor = torch.Tensor(x)
diff --git a/art/attacks/evasion/auto_attack.py b/art/attacks/evasion/auto_attack.py
@@ -330,7 +330,7 @@ def __repr__(self) -> str:
             )
             auto_attack_meta = (
                 f"AutoAttack(targeted={self.targeted}, parallel_pool_size={self.parallel_pool_size}, "
-                + "num_attacks={len(self.args)})"
+                + f"num_attacks={len(self.args)})"
             )
             return f"{auto_attack_meta}\nBestAttacks:\n{best_attack_meta}"
 
@@ -342,7 +342,7 @@ def __repr__(self) -> str:
         )
         auto_attack_meta = (
             f"AutoAttack(targeted={self.targeted}, parallel_pool_size={self.parallel_pool_size}, "
-            + "num_attacks={len(self.attacks)})"
+            + f"num_attacks={len(self.attacks)})"
         )
         return f"{auto_attack_meta}\nBestAttacks:\n{best_attack_meta}"
 
diff --git a/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_pytorch.py b/art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_pytorch.py
@@ -497,7 +497,10 @@ def _projection(
         if (suboptimal or norm == 2) and norm != np.inf:  # Simple rescaling
             values_norm = torch.linalg.norm(values_tmp, ord=norm, dim=1, keepdim=True)  # (n_samples, 1)
             values_tmp = values_tmp * values_norm.where(
-                values_norm == 0, torch.minimum(torch.ones(1), torch.tensor(eps).to(values_tmp.device) / values_norm)
+                values_norm == 0,
+                torch.minimum(
+                    torch.ones(1).to(values_tmp.device), torch.tensor(eps).to(values_tmp.device) / values_norm
+                ),
             )
         else:  # Optimal
             if norm == np.inf:  # Easy exact case
diff --git a/art/estimators/certification/derandomized_smoothing/pytorch.py b/art/estimators/certification/derandomized_smoothing/pytorch.py
@@ -162,7 +162,7 @@ def __init__(
                         model, pretrained=load_pretrained, drop_tokens=drop_tokens, device_type=device_type
                     )
                     if replace_last_layer:
-                        model.head = torch.nn.Linear(model.head.in_features, nb_classes)
+                        model.head = torch.nn.Linear(model.head.in_features, nb_classes)  # type: ignore
                     if isinstance(optimizer, type):
                         if optimizer_params is not None:
                             optimizer = optimizer(model.parameters(), **optimizer_params)
@@ -181,9 +181,9 @@ def __init__(
                     model = timm.create_model(
                         pretrained_cfg["architecture"], drop_tokens=drop_tokens, device_type=device_type
                     )
-                    model.load_state_dict(supplied_state_dict)
+                    model.load_state_dict(supplied_state_dict)  # type: ignore
                     if replace_last_layer:
-                        model.head = torch.nn.Linear(model.head.in_features, nb_classes)
+                        model.head = torch.nn.Linear(model.head.in_features, nb_classes)  # type: ignore
 
                     if optimizer is not None:
                         if not isinstance(optimizer, torch.optim.Optimizer):
@@ -193,10 +193,10 @@ def __init__(
                         opt_state_dict = optimizer.state_dict()
                         if isinstance(optimizer, torch.optim.Adam):
                             logging.info("Converting Adam Optimiser")
-                            converted_optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
+                            converted_optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)  # type: ignore
                         elif isinstance(optimizer, torch.optim.SGD):
                             logging.info("Converting SGD Optimiser")
-                            converted_optimizer = torch.optim.SGD(model.parameters(), lr=1e-4)
+                            converted_optimizer = torch.optim.SGD(model.parameters(), lr=1e-4)  # type: ignore
                         else:
                             raise ValueError("Optimiser not supported for conversion")
                         converted_optimizer.load_state_dict(opt_state_dict)
diff --git a/docs/conf.py b/docs/conf.py
@@ -27,7 +27,7 @@
 # The short X.Y version
 version = "1.19"
 # The full version, including alpha/beta/rc tags
-release = "1.19.0"
+release = "1.19.1"
 
 
 # -- General configuration ---------------------------------------------------
diff --git a/docs/modules/defences/detector_evasion.rst b/docs/modules/defences/detector_evasion.rst
@@ -7,7 +7,7 @@ Base Class
 .. autoclass:: EvasionDetector
    :members:
 
-Be Your Own Neighborhood (BEYOND) Detector - PyTorch
+Be Your Own Neighbourhood Detector (BEYOND)- PyTorch
 ----------------------------------------------------
 .. autoclass:: BeyondDetectorPyTorch
    :members:
diff --git a/requirements_test.txt b/requirements_test.txt
