fix style checks

art/estimators/object_detection/pytorch_detection_transformer.py

@@ -23,6 +23,8 @@
 import logging
 from typing import Any, Dict, List, Optional, Tuple, Union, TYPE_CHECKING
 
+import numpy as np
+
 from art.estimators.object_detection.pytorch_object_detector import PyTorchObjectDetector
 
 if TYPE_CHECKING:
@@ -161,7 +163,7 @@ def _translate_labels(self, labels: List[Dict[str, "torch.Tensor"]]) -> List[Any
 
         return labels_translated
 
-    def _translate_predictions(self, predictions: Dict[str, "torch.Tensor"]) -> List[Dict[str, "torch.Tensor"]]:
+    def _translate_predictions(self, predictions: Dict[str, "torch.Tensor"]) -> List[Dict[str, np.ndarray]]:
         """
         Translate object detection predictions from the model format (DETR) to ART format (torchvision) and
         convert tensors to numpy arrays.
@@ -181,7 +183,7 @@ def _translate_predictions(self, predictions: Dict[str, "torch.Tensor"]) -> List
         pred_boxes = predictions["pred_boxes"]
         pred_logits = predictions["pred_logits"]
 
-        predictions_x1y1x2y2 = []
+        predictions_x1y1x2y2: List[Dict[str, np.ndarray]] = []
 
         for pred_box, pred_logit in zip(pred_boxes, pred_logits):
             boxes = rescale_bboxes(pred_box.detach().cpu(), (height, width)).numpy()

art/estimators/object_detection/pytorch_object_detector.py

@@ -120,8 +120,8 @@ def __init__(
         self._attack_losses = attack_losses
 
         # Parameters used for subclasses
-        self.weight_dict = None
-        self.criterion = None
+        self.weight_dict: Optional[Dict[str, float]] = None
+        self.criterion: Optional[torch.nn.Module] = None
 
         if self.clip_values is not None:
             if self.clip_values[0] != 0:
@@ -577,6 +577,6 @@ def compute_loss(  # type: ignore
             )
 
         if isinstance(x, torch.Tensor):
-            return loss
+            return loss  # type: ignore
 
-        return loss.detach().cpu().numpy()
+        return loss.detach().cpu().numpy()  # type: ignore

art/estimators/object_detection/pytorch_yolo.py

@@ -23,6 +23,8 @@
 import logging
 from typing import List, Dict, Optional, Tuple, Union, TYPE_CHECKING
 
+import numpy as np
+
 from art.estimators.object_detection.pytorch_object_detector import PyTorchObjectDetector
 
 if TYPE_CHECKING:
@@ -142,7 +144,7 @@ def _translate_labels(self, labels: List[Dict[str, "torch.Tensor"]]) -> "torch.T
         labels_xcycwh = torch.vstack(labels_xcycwh_list)
         return labels_xcycwh
 
-    def _translate_predictions(self, predictions: "torch.Tensor") -> List[Dict[str, "torch.Tensor"]]:
+    def _translate_predictions(self, predictions: "torch.Tensor") -> List[Dict[str, np.ndarray]]:
         """
         Translate object detection predictions from the model format (YOLO) to ART format (torchvision) and
         convert tensors to numpy arrays.
@@ -159,7 +161,7 @@ def _translate_predictions(self, predictions: "torch.Tensor") -> List[Dict[str,
             height = self.input_shape[0]
             width = self.input_shape[1]
 
-        predictions_x1y1x2y2 = []
+        predictions_x1y1x2y2: List[Dict[str, np.ndarray]] = []
 
         for pred in predictions:
             boxes = torch.vstack(