diff --git a/src/lib/models/decode.py b/src/lib/models/decode.py
index 170b216..75baa3d 100644
--- a/src/lib/models/decode.py
+++ b/src/lib/models/decode.py
@@ -118,11 +118,11 @@ def _topk(scores, K=40):
     topk_scores, topk_inds = torch.topk(scores.view(batch, cat, -1), K)
 
     topk_inds = topk_inds % (height * width)
-    topk_ys = (topk_inds / width).int().float()
+    topk_ys = (topk_inds // width).int().float()
     topk_xs = (topk_inds % width).int().float()
 
     topk_score, topk_ind = torch.topk(topk_scores.view(batch, -1), K)
-    topk_clses = (topk_ind / K).int()
+    topk_clses = (topk_ind // K).int()
     topk_inds = _gather_feat(
         topk_inds.view(batch, -1, 1), topk_ind).view(batch, K)
     topk_ys = _gather_feat(topk_ys.view(batch, -1, 1), topk_ind).view(batch, K)
diff --git a/src/lib/models/losses.py b/src/lib/models/losses.py
index 95cdbcc..774a585 100644
--- a/src/lib/models/losses.py
+++ b/src/lib/models/losses.py
@@ -176,7 +176,7 @@ def forward(self, seg_feat, conv_weight, mask, ind, target):
         batch_size = seg_feat.size(0)
         weight = _transpose_and_gather_feat(conv_weight, ind)
         h, w = seg_feat.size(-2), seg_feat.size(-1)
-        x, y = ind % w, ind/w
+        x, y = ind % w, ind//w
         x_range = torch.arange(w).float().to(device=seg_feat.device)
         y_range = torch.arange(h).float().to(device=seg_feat.device)
         y_grid, x_grid = torch.meshgrid([y_range, x_range])