diff --git a/pytorch3d/ops/cubify.py b/pytorch3d/ops/cubify.py
index 6de5decb..cd82ec97 100644
--- a/pytorch3d/ops/cubify.py
+++ b/pytorch3d/ops/cubify.py
@@ -18,9 +18,9 @@ def unravel_index(idx, dims) -> torch.Tensor:
     if len(dims) != 4:
         raise ValueError("Expects a 4-element list.")
     N, H, W, D = dims
-    n = torch.div(idx, H * W * D)
-    h = torch.div(idx - n * H * W * D, W * D)
-    w = torch.div(idx - n * H * W * D - h * W * D, D)
+    n = idx // (H * W * D)
+    h = (idx - n * H * W * D) // (W * D)
+    w = (idx - n * H * W * D - h * W * D) // D
     d = idx - n * H * W * D - h * W * D - w * D
     return torch.stack((n, h, w, d), dim=1)
 
diff --git a/pytorch3d/structures/meshes.py b/pytorch3d/structures/meshes.py
index 036d26de..a72eb5dc 100644
--- a/pytorch3d/structures/meshes.py
+++ b/pytorch3d/structures/meshes.py
@@ -1031,7 +1031,7 @@ class Meshes(object):
         unique_mask[1:] = sorted_hash[1:] != sorted_hash[:-1]
         unique_idx = sort_idx[unique_mask]
 
-        self._edges_packed = torch.stack([u / V, u % V], dim=1)
+        self._edges_packed = torch.stack([u // V, u % V], dim=1)
         self._edges_packed_to_mesh_idx = edge_to_mesh[unique_idx]
 
         face_to_edge = torch.arange(3 * F).view(3, F).t()
diff --git a/tests/test_rasterizer.py b/tests/test_rasterizer.py
index b65147a5..da1c95e4 100644
--- a/tests/test_rasterizer.py
+++ b/tests/test_rasterizer.py
@@ -24,11 +24,8 @@ DEBUG = False  # Set DEBUG to true to save outputs from the tests.
 def convert_image_to_binary_mask(filename):
     with Image.open(filename) as raw_image:
         image = torch.from_numpy(np.array(raw_image))
-    min = image.min()
-    max = image.max()
-    image_norm = (image - min) / (max - min)
-    image_norm[image_norm > 0] == 1.0
-    image_norm = image_norm.to(torch.int64)
+    mx = image.max()
+    image_norm = (image == mx).to(torch.int64)
     return image_norm