pt updates

nndet/core/boxes/anchors.py

@@ -146,7 +146,7 @@ class AnchorGenerator2D(torch.nn.Module):
             shifts_x = torch.arange(0, size0, dtype=torch.float, device=device) * stride0
             shifts_y = torch.arange(0, size1, dtype=torch.float, device=device) * stride1
             
-            shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
+            shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x, indexing="ij")
             shift_x = shift_x.reshape(-1)
             shift_y = shift_y.reshape(-1)
             shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
@@ -361,7 +361,7 @@ class AnchorGenerator3D(AnchorGenerator2D):
             shifts_y = torch.arange(0, size1, dtype=dtype, device=device) * stride1
             shifts_z = torch.arange(0, size2, dtype=dtype, device=device) * stride2
 
-            shift_x, shift_y, shift_z = torch.meshgrid(shifts_x, shifts_y, shifts_z)
+            shift_x, shift_y, shift_z = torch.meshgrid(shifts_x, shifts_y, shifts_z, indexing="ij")
             shift_x = shift_x.reshape(-1)
             shift_y = shift_y.reshape(-1)
             shift_z = shift_z.reshape(-1)

nndet/core/retina.py

@@ -364,7 +364,7 @@ class BaseRetinaNet(AbstractModel):
             keep_idxs = probs > self.score_thresh
             probs, idx = probs[keep_idxs], idx[keep_idxs]
 
-        anchor_idxs = idx // self.num_foreground_classes
+        anchor_idxs = torch.div(idx, self.num_foreground_classes, rounding_mode="floor")
         labels = idx % self.num_foreground_classes
         boxes = boxes[anchor_idxs]