diff --git a/fastreid/modeling/losses/circle_loss.py b/fastreid/modeling/losses/circle_loss.py
index c379852..c95aac2 100644
--- a/fastreid/modeling/losses/circle_loss.py
+++ b/fastreid/modeling/losses/circle_loss.py
@@ -29,12 +29,12 @@ def circle_loss(
     dist_mat = torch.matmul(all_embedding, all_embedding.t())
 
     N = dist_mat.size(0)
-    is_pos = targets.view(N, 1).expand(N, N).eq(all_targets.view(N, 1).expand(N, N).t()).float()
+    is_pos = all_targets.view(N, 1).expand(N, N).eq(all_targets.view(N, 1).expand(N, N).t()).float()
 
     # Compute the mask which ignores the relevance score of the query to itself
     is_pos = is_pos - torch.eye(N, N, device=is_pos.device)
 
-    is_neg = targets.view(N, 1).expand(N, N).ne(all_targets.view(N, 1).expand(N, N).t())
+    is_neg = all_targets.view(N, 1).expand(N, N).ne(all_targets.view(N, 1).expand(N, N).t())
 
     s_p = dist_mat * is_pos
     s_n = dist_mat * is_neg