Update ViT ops.py to `torch.long` (#3508)

Co-authored-by: Laughing-q <1185102784@qq.com>

ultralytics/vit/utils/loss.py

@@ -284,11 +284,11 @@ class RTDETRDetectionLoss(DETRLoss):
         idx_groups = torch.as_tensor([0, *gt_groups[:-1]]).cumsum_(0)
         for i, num_gt in enumerate(gt_groups):
             if num_gt > 0:
-                gt_idx = torch.arange(end=num_gt, dtype=torch.int32) + idx_groups[i]
+                gt_idx = torch.arange(end=num_gt, dtype=torch.long) + idx_groups[i]
                 gt_idx = gt_idx.repeat(dn_num_group)
                 assert len(dn_pos_idx[i]) == len(gt_idx), 'Expected the same length, '
                 f'but got {len(dn_pos_idx[i])} and {len(gt_idx)} respectively.'
                 dn_match_indices.append((dn_pos_idx[i], gt_idx))
             else:
-                dn_match_indices.append((torch.zeros([0], dtype=torch.int32), torch.zeros([0], dtype=torch.int32)))
+                dn_match_indices.append((torch.zeros([0], dtype=torch.long), torch.zeros([0], dtype=torch.long)))
         return dn_match_indices

ultralytics/vit/utils/ops.py

@@ -71,7 +71,7 @@ class HungarianMatcher(nn.Module):
         bs, nq, nc = pred_scores.shape
 
         if sum(gt_groups) == 0:
-            return [(torch.tensor([], dtype=torch.int32), torch.tensor([], dtype=torch.int32)) for _ in range(bs)]
+            return [(torch.tensor([], dtype=torch.long), torch.tensor([], dtype=torch.long)) for _ in range(bs)]
 
         # We flatten to compute the cost matrices in a batch
         # [batch_size * num_queries, num_classes]
@@ -107,7 +107,7 @@ class HungarianMatcher(nn.Module):
         indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(gt_groups, -1))]
         gt_groups = torch.as_tensor([0, *gt_groups[:-1]]).cumsum_(0)
         # (idx for queries, idx for gt)
-        return [(torch.tensor(i, dtype=torch.int32), torch.tensor(j, dtype=torch.int32) + gt_groups[k])
+        return [(torch.tensor(i, dtype=torch.long), torch.tensor(j, dtype=torch.long) + gt_groups[k])
                 for k, (i, j) in enumerate(indices)]
 
     def _cost_mask(self, bs, num_gts, masks=None, gt_mask=None):