Detr preprocessor fix (#19007)

* fix in-place preprocessing of inputs

src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py

@@ -555,6 +555,12 @@ class ConditionalDetrFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtrac
             if annotations is not None:
                 annotations = [annotations]
 
+        # Create deep copies to avoid editing inputs in place
+        images = [image for image in images]
+
+        if annotations is not None:
+            annotations = [annotation for annotation in annotations]
+
         # prepare (COCO annotations as a list of Dict -> ConditionalDETR target as a single Dict per image)
         if annotations is not None:
             for idx, (image, target) in enumerate(zip(images, annotations)):
@@ -587,6 +593,8 @@ class ConditionalDetrFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtrac
                 images = [
                     self._normalize(image=image, mean=self.image_mean, std=self.image_std)[0] for image in images
                 ]
+        else:
+            images = [np.array(image) for image in images]
 
         if pad_and_return_pixel_mask:
             # pad images up to largest image in batch and create pixel_mask

src/transformers/models/deformable_detr/feature_extraction_deformable_detr.py

@@ -555,6 +555,12 @@ class DeformableDetrFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtract
             if annotations is not None:
                 annotations = [annotations]
 
+        # Create deep copies to avoid editing inputs in place
+        images = [image for image in images]
+
+        if annotations is not None:
+            annotations = [annotation for annotation in annotations]
+
         # prepare (COCO annotations as a list of Dict -> DETR target as a single Dict per image)
         if annotations is not None:
             for idx, (image, target) in enumerate(zip(images, annotations)):
@@ -587,6 +593,8 @@ class DeformableDetrFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtract
                 images = [
                     self._normalize(image=image, mean=self.image_mean, std=self.image_std)[0] for image in images
                 ]
+        else:
+            images = [np.array(image) for image in images]
 
         if pad_and_return_pixel_mask:
             # pad images up to largest image in batch and create pixel_mask

src/transformers/models/detr/feature_extraction_detr.py

@@ -547,6 +547,12 @@ class DetrFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
             if annotations is not None:
                 annotations = [annotations]
 
+        # Create deep copies to avoid editing inputs in place
+        images = [image for image in images]
+
+        if annotations is not None:
+            annotations = [annotation for annotation in annotations]
+
         # prepare (COCO annotations as a list of Dict -> DETR target as a single Dict per image)
         if annotations is not None:
             for idx, (image, target) in enumerate(zip(images, annotations)):
@@ -579,6 +585,8 @@ class DetrFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
                 images = [
                     self._normalize(image=image, mean=self.image_mean, std=self.image_std)[0] for image in images
                 ]
+        else:
+            images = [np.array(image) for image in images]
 
         if pad_and_return_pixel_mask:
             # pad images up to largest image in batch and create pixel_mask