Merge pull request #2207 from Trusted-AI/dev_1.15.0

Update to ART 1.15.0

.github/workflows/ci-pytorch-object-detectors.yml

@@ -50,6 +50,8 @@ jobs:
         run: pytest --cov-report=xml --cov=art --cov-append -q -vv tests/estimators/object_detection/test_pytorch_object_detector.py --framework=pytorch --durations=0
       - name: Run Test Action - test_pytorch_faster_rcnn
         run: pytest --cov-report=xml --cov=art --cov-append -q -vv tests/estimators/object_detection/test_pytorch_faster_rcnn.py --framework=pytorch --durations=0
+      - name: Run Test Action - test_pytorch_detection_transformer
+        run: pytest --cov-report=xml --cov=art --cov-append -q -vv tests/estimators/object_detection/test_pytorch_detection_transformer.py --framework=pytorch --durations=0
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v3
         with:

art/attacks/attack.py

@@ -339,10 +339,10 @@ def __init__(self):
     @abc.abstractmethod
     def poison(
         self,
-        x: np.ndarray,
+        x: Union[np.ndarray, List[np.ndarray]],
         y: List[Dict[str, np.ndarray]],
         **kwargs,
-    ) -> Tuple[np.ndarray, List[Dict[str, np.ndarray]]]:
+    ) -> Tuple[Union[np.ndarray, List[np.ndarray]], List[Dict[str, np.ndarray]]]:
         """
         Generate poisoning examples and return them as an array. This method should be overridden by all concrete
         poisoning attack implementations.

art/attacks/evasion/adversarial_patch/adversarial_patch_pytorch.py

@@ -575,9 +575,9 @@ def __getitem__(self, idx):
                     img = torch.from_numpy(self.x[idx])
 
                     target = {}
-                    target["boxes"] = torch.from_numpy(y[idx]["boxes"])
-                    target["labels"] = torch.from_numpy(y[idx]["labels"])
-                    target["scores"] = torch.from_numpy(y[idx]["scores"])
+                    target["boxes"] = torch.from_numpy(self.y[idx]["boxes"])
+                    target["labels"] = torch.from_numpy(self.y[idx]["labels"])
+                    target["scores"] = torch.from_numpy(self.y[idx]["scores"])
                     mask_i = torch.from_numpy(self.mask[idx])
 
                     return img, target, mask_i
@@ -602,19 +602,33 @@ def __getitem__(self, idx):
                     if isinstance(target, torch.Tensor):
                         target = target.to(self.estimator.device)
                     else:
-                        target["boxes"] = target["boxes"].to(self.estimator.device)
-                        target["labels"] = target["labels"].to(self.estimator.device)
-                        target["scores"] = target["scores"].to(self.estimator.device)
+                        targets = []
+                        for idx in range(target["boxes"].shape[0]):
+                            targets.append(
+                                {
+                                    "boxes": target["boxes"][idx].to(self.estimator.device),
+                                    "labels": target["labels"][idx].to(self.estimator.device),
+                                    "scores": target["scores"][idx].to(self.estimator.device),
+                                }
+                            )
+                        target = targets
                     _ = self._train_step(images=images, target=target, mask=None)
             else:
                 for images, target, mask_i in data_loader:
                     images = images.to(self.estimator.device)
                     if isinstance(target, torch.Tensor):
                         target = target.to(self.estimator.device)
                     else:
-                        target["boxes"] = target["boxes"].to(self.estimator.device)
-                        target["labels"] = target["labels"].to(self.estimator.device)
-                        target["scores"] = target["scores"].to(self.estimator.device)
+                        targets = []
+                        for idx in range(target["boxes"].shape[0]):
+                            targets.append(
+                                {
+                                    "boxes": target["boxes"][idx].to(self.estimator.device),
+                                    "labels": target["labels"][idx].to(self.estimator.device),
+                                    "scores": target["scores"][idx].to(self.estimator.device),
+                                }
+                            )
+                        target = targets
                     mask_i = mask_i.to(self.estimator.device)
                     _ = self._train_step(images=images, target=target, mask=mask_i)
 

art/attacks/evasion/auto_conjugate_gradient.py

@@ -224,7 +224,8 @@ def __call__(self, y_true: tf.Tensor, y_pred: tf.Tensor, *args, **kwargs) -> tf.
                     nb_classes=estimator.nb_classes,
                     input_shape=estimator.input_shape,
                     loss_object=_loss_object_tf,
-                    train_step=estimator._train_step,
+                    optimizer=estimator.optimizer,
+                    train_step=estimator.train_step,
                     channels_first=estimator.channels_first,
                     clip_values=estimator.clip_values,
                     preprocessing_defences=estimator.preprocessing_defences,

art/attacks/evasion/auto_projected_gradient_descent.py

@@ -203,7 +203,8 @@ def __call__(self, y_true: tf.Tensor, y_pred: tf.Tensor, *args, **kwargs) -> tf.
                     nb_classes=estimator.nb_classes,
                     input_shape=estimator.input_shape,
                     loss_object=_loss_object_tf,
-                    train_step=estimator._train_step,
+                    optimizer=estimator.optimizer,
+                    train_step=estimator.train_step,
                     channels_first=estimator.channels_first,
                     clip_values=estimator.clip_values,
                     preprocessing_defences=estimator.preprocessing_defences,

art/attacks/evasion/brendel_bethge.py

@@ -2055,7 +2055,8 @@ def logits_difference(y_true, y_pred):
                 nb_classes=estimator.nb_classes,
                 input_shape=estimator.input_shape,
                 loss_object=self._loss_object,
-                train_step=estimator._train_step,
+                optimizer=estimator.optimizer,
+                train_step=estimator.train_step,
                 channels_first=estimator.channels_first,
                 clip_values=estimator.clip_values,
                 preprocessing_defences=estimator.preprocessing_defences,

art/attacks/evasion/projected_gradient_descent/projected_gradient_descent_pytorch.py

@@ -269,7 +269,7 @@ def _generate_batch(
         inputs = x.to(self.estimator.device)
         targets = targets.to(self.estimator.device)
         adv_x = torch.clone(inputs)
-        momentum = torch.zeros(inputs.shape)
+        momentum = torch.zeros(inputs.shape).to(self.estimator.device)
 
         if mask is not None:
             mask = mask.to(self.estimator.device)

art/attacks/evasion/sign_opt.py

@@ -306,14 +306,14 @@ def _fine_grained_binary_search_local(
         lbd = initial_lbd
         # For targeted: we want to expand(x1.01) boundary away from targeted dataset
         # For untargeted, we want to slim(x0.99) the boundary toward the original dataset
-        if (not self._is_label(x_0 + lbd * theta, target) and self.targeted) or (
-            self._is_label(x_0 + lbd * theta, y_0) and not self.targeted
+        if (self.targeted and not self._is_label(x_0 + lbd * theta, target)) or (
+            not self.targeted and self._is_label(x_0 + lbd * theta, y_0)
         ):
             lbd_lo = lbd
             lbd_hi = lbd * 1.01
             nquery += 1
-            while (not self._is_label(x_0 + lbd_hi * theta, target) and self.targeted) or (
-                self._is_label(x_0 + lbd_hi * theta, y_0) and not self.targeted
+            while (self.targeted and not self._is_label(x_0 + lbd_hi * theta, target)) or (
+                not self.targeted and self._is_label(x_0 + lbd_hi * theta, y_0)
             ):
                 lbd_hi = lbd_hi * 1.01
                 nquery += 1
@@ -323,17 +323,17 @@ def _fine_grained_binary_search_local(
             lbd_hi = lbd
             lbd_lo = lbd * 0.99
             nquery += 1
-            while (self._is_label(x_0 + lbd_lo * theta, target) and self.targeted) or (
-                not self._is_label(x_0 + lbd_lo * theta, y_0) and not self.targeted
+            while (self.targeted and self._is_label(x_0 + lbd_lo * theta, target)) or (
+                not self.targeted and not self._is_label(x_0 + lbd_lo * theta, y_0)
             ):
                 lbd_lo = lbd_lo * 0.99
                 nquery += 1
 
         while (lbd_hi - lbd_lo) > tol:
             lbd_mid = (lbd_lo + lbd_hi) / 2.0
             nquery += 1
-            if (self._is_label(x_0 + lbd_mid * theta, target) and self.targeted) or (
-                not self._is_label(x_0 + lbd_mid * theta, y_0) and not self.targeted
+            if (self.targeted and self._is_label(x_0 + lbd_mid * theta, target)) or (
+                not self.targeted and not self._is_label(x_0 + lbd_mid * theta, y_0)
             ):
                 lbd_hi = lbd_mid
             else:

art/attacks/poisoning/bad_det/bad_det_gma.py

@@ -23,7 +23,7 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
 
 import logging
-from typing import Dict, List, Tuple
+from typing import Dict, List, Tuple, Union
 
 import numpy as np
 from tqdm.auto import tqdm
@@ -77,36 +77,39 @@ def __init__(
 
     def poison(  # pylint: disable=W0221
         self,
-        x: np.ndarray,
+        x: Union[np.ndarray, List[np.ndarray]],
         y: List[Dict[str, np.ndarray]],
         **kwargs,
-    ) -> Tuple[np.ndarray, List[Dict[str, np.ndarray]]]:
+    ) -> Tuple[Union[np.ndarray, List[np.ndarray]], List[Dict[str, np.ndarray]]]:
         """
         Generate poisoning examples by inserting the backdoor onto the input `x` and changing the classification
         for labels `y`.
 
-        :param x: Sample images of shape `NCHW` or `NHWC`.
+        :param x: Sample images of shape `NCHW` or `NHWC` or a list of sample images of any size.
         :param y: True labels of type `List[Dict[np.ndarray]]`, one dictionary per input image. The keys and values
                   of the dictionary are:
 
                   - 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.
-                  - scores [N]: the scores or each prediction.
         :return: An tuple holding the `(poisoning_examples, poisoning_labels)`.
         """
-        x_ndim = len(x.shape)
+        if isinstance(x, np.ndarray):
+            x_ndim = len(x.shape)
+        else:
+            x_ndim = len(x[0].shape) + 1
 
         if x_ndim != 4:
             raise ValueError("Unrecognized input dimension. BadDet GMA can only be applied to image data.")
 
-        if self.channels_first:
-            # NCHW --> NHWC
-            x = np.transpose(x, (0, 2, 3, 1))
-
-        x_poison = x.copy()
-        y_poison: List[Dict[str, np.ndarray]] = []
+        # copy images
+        x_poison: Union[np.ndarray, List[np.ndarray]]
+        if isinstance(x, np.ndarray):
+            x_poison = x.copy()
+        else:
+            x_poison = [x_i.copy() for x_i in x]
 
         # copy labels
+        y_poison: List[Dict[str, np.ndarray]] = []
         for y_i in y:
             target_dict = {k: v.copy() for k, v in y_i.items()}
             y_poison.append(target_dict)
@@ -120,18 +123,22 @@ def poison(  # pylint: disable=W0221
             image = x_poison[i]
             labels = y_poison[i]["labels"]
 
+            if self.channels_first:
+                image = np.transpose(image, (1, 2, 0))
+
             # insert backdoor into the image
             # add an additional dimension to create a batch of size 1
             poisoned_input, _ = self.backdoor.poison(image[np.newaxis], labels)
-            x_poison[i] = poisoned_input[0]
+            image = poisoned_input[0]
+
+            # replace the original image with the poisoned image
+            if self.channels_first:
+                image = np.transpose(image, (2, 0, 1))
+            x_poison[i] = image
 
             # change all labels to the target label
             y_poison[i]["labels"] = np.full(labels.shape, self.class_target)
 
-        if self.channels_first:
-            # NHWC --> NCHW
-            x_poison = np.transpose(x_poison, (0, 3, 1, 2))
-
         return x_poison, y_poison
 
     def _check_params(self) -> None:

art/attacks/poisoning/bad_det/bad_det_oda.py

@@ -23,7 +23,7 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
 
 import logging
-from typing import Dict, List, Tuple
+from typing import Dict, List, Tuple, Union
 
 import numpy as np
 from tqdm.auto import tqdm
@@ -77,36 +77,39 @@ def __init__(
 
     def poison(  # pylint: disable=W0221
         self,
-        x: np.ndarray,
+        x: Union[np.ndarray, List[np.ndarray]],
         y: List[Dict[str, np.ndarray]],
         **kwargs,
-    ) -> Tuple[np.ndarray, List[Dict[str, np.ndarray]]]:
+    ) -> Tuple[Union[np.ndarray, List[np.ndarray]], List[Dict[str, np.ndarray]]]:
         """
         Generate poisoning examples by inserting the backdoor onto the input `x` and changing the classification
         for labels `y`.
 
-        :param x: Sample images of shape `NCHW` or `NHWC`.
+        :param x: Sample images of shape `NCHW` or `NHWC` or a list of sample images of any size.
         :param y: True labels of type `List[Dict[np.ndarray]]`, one dictionary per input image. The keys and values
                   of the dictionary are:
 
                   - 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.
-                  - scores [N]: the scores or each prediction.
         :return: An tuple holding the `(poisoning_examples, poisoning_labels)`.
         """
-        x_ndim = len(x.shape)
+        if isinstance(x, np.ndarray):
+            x_ndim = len(x.shape)
+        else:
+            x_ndim = len(x[0].shape) + 1
 
         if x_ndim != 4:
             raise ValueError("Unrecognized input dimension. BadDet ODA can only be applied to image data.")
 
-        if self.channels_first:
-            # NCHW --> NHWC
-            x = np.transpose(x, (0, 2, 3, 1))
-
-        x_poison = x.copy()
-        y_poison: List[Dict[str, np.ndarray]] = []
+        # copy images
+        x_poison: Union[np.ndarray, List[np.ndarray]]
+        if isinstance(x, np.ndarray):
+            x_poison = x.copy()
+        else:
+            x_poison = [x_i.copy() for x_i in x]
 
         # copy labels and find indices of the source class
+        y_poison: List[Dict[str, np.ndarray]] = []
         source_indices = []
         for i, y_i in enumerate(y):
             target_dict = {k: v.copy() for k, v in y_i.items()}
@@ -121,10 +124,12 @@ def poison(  # pylint: disable=W0221
 
         for i in tqdm(selected_indices, desc="BadDet ODA iteration", disable=not self.verbose):
             image = x_poison[i]
-
             boxes = y_poison[i]["boxes"]
             labels = y_poison[i]["labels"]
 
+            if self.channels_first:
+                image = np.transpose(image, (1, 2, 0))
+
             keep_indices = []
 
             for j, (box, label) in enumerate(zip(boxes, labels)):
@@ -140,13 +145,14 @@ def poison(  # pylint: disable=W0221
                 else:
                     keep_indices.append(j)
 
+            # replace the original image with the poisoned image
+            if self.channels_first:
+                image = np.transpose(image, (2, 0, 1))
+            x_poison[i] = image
+
             # remove labels for poisoned bounding boxes
             y_poison[i] = {k: v[keep_indices] for k, v in y_poison[i].items()}
 
-        if self.channels_first:
-            # NHWC --> NCHW
-            x_poison = np.transpose(x_poison, (0, 3, 1, 2))
-
         return x_poison, y_poison
 
     def _check_params(self) -> None: