Apply ruff==0.9.0 formatting (#18624)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

ultralytics/models/sam/model.py

@@ -106,7 +106,7 @@ class SAM(Model):
             ...     print(f"Detected {len(r.masks)} masks")
         """
         overrides = dict(conf=0.25, task="segment", mode="predict", imgsz=1024)
-        kwargs = {**overrides, **kwargs}
+        kwargs = overrides | kwargs
         prompts = dict(bboxes=bboxes, points=points, labels=labels)
         return super().predict(source, stream, prompts=prompts, **kwargs)
 

ultralytics/models/sam/modules/utils.py

@@ -54,7 +54,7 @@ def select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num
             (t for t in cond_frame_outputs if t not in selected_outputs),
             key=lambda x: abs(x - frame_idx),
         )[:num_remain]
-        selected_outputs.update((t, cond_frame_outputs[t]) for t in inds_remain)
+        selected_outputs |= ((t, cond_frame_outputs[t]) for t in inds_remain)
         unselected_outputs = {t: v for t, v in cond_frame_outputs.items() if t not in selected_outputs}
 
     return selected_outputs, unselected_outputs

ultralytics/models/utils/loss.py

@@ -244,7 +244,7 @@ class DETRLoss(nn.Module):
             gt_scores[idx] = bbox_iou(pred_bboxes.detach(), gt_bboxes, xywh=True).squeeze(-1)
 
         loss = {}
-        loss.update(self._get_loss_class(pred_scores, targets, gt_scores, len(gt_bboxes), postfix))
+        loss |= self._get_loss_class(pred_scores, targets, gt_scores, len(gt_bboxes), postfix)
         loss.update(self._get_loss_bbox(pred_bboxes, gt_bboxes, postfix))
         # if masks is not None and gt_mask is not None:
         #     loss.update(self._get_loss_mask(masks, gt_mask, match_indices, postfix))