Keypoints 0.0 are confusing...

Keypoints 0.0 are confusing ../transformers/models/detr/image_processing_detr.py which are fixed (#26250)

* Keypoints 0.0 is fixed

* fixed keypoints for image_processing_yolos

* fixed keypoints for image_processing_deta

* fixed keypoints for image_processing_deformable_detr

* fixed keypoints for image_processing_conditional_detr

* fixed styles

* Removed Comments

* Removed comment form conditional detr too

* Removed Extra code

* make fix-copes

* Fixed code quality

* keypoints changes

src/transformers/models/conditional_detr/image_processing_conditional_detr.py

@@ -332,10 +332,13 @@ def prepare_coco_detection_annotation(
 
     if annotations and "keypoints" in annotations[0]:
         keypoints = [obj["keypoints"] for obj in annotations]
+        # Converting the filtered keypoints list to a numpy array
         keypoints = np.asarray(keypoints, dtype=np.float32)
+        # Apply the keep mask here to filter the relevant annotations
+        keypoints = keypoints[keep]
         num_keypoints = keypoints.shape[0]
         keypoints = keypoints.reshape((-1, 3)) if num_keypoints else keypoints
-        new_target["keypoints"] = keypoints[keep]
+        new_target["keypoints"] = keypoints
 
     if return_segmentation_masks:
         segmentation_masks = [obj["segmentation"] for obj in annotations]

src/transformers/models/deformable_detr/image_processing_deformable_detr.py

@@ -331,10 +331,13 @@ def prepare_coco_detection_annotation(
 
     if annotations and "keypoints" in annotations[0]:
         keypoints = [obj["keypoints"] for obj in annotations]
+        # Converting the filtered keypoints list to a numpy array
         keypoints = np.asarray(keypoints, dtype=np.float32)
+        # Apply the keep mask here to filter the relevant annotations
+        keypoints = keypoints[keep]
         num_keypoints = keypoints.shape[0]
         keypoints = keypoints.reshape((-1, 3)) if num_keypoints else keypoints
-        new_target["keypoints"] = keypoints[keep]
+        new_target["keypoints"] = keypoints
 
     if return_segmentation_masks:
         segmentation_masks = [obj["segmentation"] for obj in annotations]

src/transformers/models/deta/image_processing_deta.py

@@ -323,10 +323,13 @@ def prepare_coco_detection_annotation(
 
     if annotations and "keypoints" in annotations[0]:
         keypoints = [obj["keypoints"] for obj in annotations]
+        # Converting the filtered keypoints list to a numpy array
         keypoints = np.asarray(keypoints, dtype=np.float32)
+        # Apply the keep mask here to filter the relevant annotations
+        keypoints = keypoints[keep]
         num_keypoints = keypoints.shape[0]
         keypoints = keypoints.reshape((-1, 3)) if num_keypoints else keypoints
-        new_target["keypoints"] = keypoints[keep]
+        new_target["keypoints"] = keypoints
 
     if return_segmentation_masks:
         segmentation_masks = [obj["segmentation"] for obj in annotations]

src/transformers/models/detr/image_processing_detr.py

@@ -326,10 +326,13 @@ def prepare_coco_detection_annotation(
 
     if annotations and "keypoints" in annotations[0]:
         keypoints = [obj["keypoints"] for obj in annotations]
+        # Converting the filtered keypoints list to a numpy array
         keypoints = np.asarray(keypoints, dtype=np.float32)
+        # Apply the keep mask here to filter the relevant annotations
+        keypoints = keypoints[keep]
         num_keypoints = keypoints.shape[0]
         keypoints = keypoints.reshape((-1, 3)) if num_keypoints else keypoints
-        new_target["keypoints"] = keypoints[keep]
+        new_target["keypoints"] = keypoints
 
     if return_segmentation_masks:
         segmentation_masks = [obj["segmentation"] for obj in annotations]

src/transformers/models/yolos/image_processing_yolos.py

@@ -329,10 +329,13 @@ def prepare_coco_detection_annotation(
 
     if annotations and "keypoints" in annotations[0]:
         keypoints = [obj["keypoints"] for obj in annotations]
+        # Converting the filtered keypoints list to a numpy array
         keypoints = np.asarray(keypoints, dtype=np.float32)
+        # Apply the keep mask here to filter the relevant annotations
+        keypoints = keypoints[keep]
         num_keypoints = keypoints.shape[0]
         keypoints = keypoints.reshape((-1, 3)) if num_keypoints else keypoints
-        new_target["keypoints"] = keypoints[keep]
+        new_target["keypoints"] = keypoints
 
     if return_segmentation_masks:
         segmentation_masks = [obj["segmentation"] for obj in annotations]