Add check for `target_sizes is None` in `post_process_image_guided_detection` for owlv2 (#31934)

* Add check for target_sizes is None in post_process_image_guided_detection

* Make sure Owlvit and Owlv2 in sync

* Fix incorrect indentation; add check for correct size of target_sizes

src/transformers/models/owlv2/image_processing_owlv2.py

@@ -565,9 +565,9 @@ class Owlv2ImageProcessor(BaseImageProcessor):
         """
         logits, target_boxes = outputs.logits, outputs.target_pred_boxes
 
-        if len(logits) != len(target_sizes):
+        if target_sizes is not None and len(logits) != len(target_sizes):
             raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
-        if target_sizes.shape[1] != 2:
+        if target_sizes is not None and target_sizes.shape[1] != 2:
             raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
 
         probs = torch.max(logits, dim=-1)
@@ -588,9 +588,14 @@ class Owlv2ImageProcessor(BaseImageProcessor):
                     scores[idx][ious > nms_threshold] = 0.0
 
         # Convert from relative [0, 1] to absolute [0, height] coordinates
-        img_h, img_w = target_sizes.unbind(1)
-        scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(target_boxes.device)
-        target_boxes = target_boxes * scale_fct[:, None, :]
+        if target_sizes is not None:
+            if isinstance(target_sizes, List):
+                img_h = torch.tensor([i[0] for i in target_sizes])
+                img_w = torch.tensor([i[1] for i in target_sizes])
+            else:
+                img_h, img_w = target_sizes.unbind(1)
+            scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(target_boxes.device)
+            target_boxes = target_boxes * scale_fct[:, None, :]
 
         # Compute box display alphas based on prediction scores
         results = []

src/transformers/models/owlvit/image_processing_owlvit.py

@@ -556,9 +556,9 @@ class OwlViTImageProcessor(BaseImageProcessor):
         """
         logits, target_boxes = outputs.logits, outputs.target_pred_boxes
 
-        if len(logits) != len(target_sizes):
+        if target_sizes is not None and len(logits) != len(target_sizes):
             raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
-        if target_sizes.shape[1] != 2:
+        if target_sizes is not None and target_sizes.shape[1] != 2:
             raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
 
         probs = torch.max(logits, dim=-1)
@@ -579,9 +579,14 @@ class OwlViTImageProcessor(BaseImageProcessor):
                     scores[idx][ious > nms_threshold] = 0.0
 
         # Convert from relative [0, 1] to absolute [0, height] coordinates
-        img_h, img_w = target_sizes.unbind(1)
-        scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(target_boxes.device)
-        target_boxes = target_boxes * scale_fct[:, None, :]
+        if target_sizes is not None:
+            if isinstance(target_sizes, List):
+                img_h = torch.tensor([i[0] for i in target_sizes])
+                img_w = torch.tensor([i[1] for i in target_sizes])
+            else:
+                img_h, img_w = target_sizes.unbind(1)
+            scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(target_boxes.device)
+            target_boxes = target_boxes * scale_fct[:, None, :]
 
         # Compute box display alphas based on prediction scores
         results = []