update: set `indexing` parameter with `ij`

timm/models/swin_transformer.py

@@ -133,7 +133,7 @@ def window_reverse(windows, window_size: int, H: int, W: int):
 
 def get_relative_position_index(win_h, win_w):
     # get pair-wise relative position index for each token inside the window
-    coords = torch.stack(torch.meshgrid([torch.arange(win_h), torch.arange(win_w)]))  # 2, Wh, Ww
+    coords = torch.stack(torch.meshgrid([torch.arange(win_h), torch.arange(win_w)], indexing='ij'))  # 2, Wh, Ww
     coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
     relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
     relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2

timm/models/swin_transformer_v2.py

@@ -160,9 +160,15 @@ class WindowAttention(nn.Module):
         # get relative_coords_table
         relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
         relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
-        relative_coords_table = torch.stack(torch.meshgrid([
+        relative_coords_table = torch.stack(
+            torch.meshgrid(
+                [
                     relative_coords_h,
-            relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0)  # 1, 2*Wh-1, 2*Ww-1, 2
+                    relative_coords_w
+                ],
+                indexing='ij',
+            )
+        ).permute(1, 2, 0).contiguous().unsqueeze(0)  # 1, 2*Wh-1, 2*Ww-1, 2
         if pretrained_window_size[0] > 0:
             relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
             relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
@@ -178,7 +184,7 @@ class WindowAttention(nn.Module):
         # get pair-wise relative position index for each token inside the window
         coords_h = torch.arange(self.window_size[0])
         coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij'))  # 2, Wh, Ww
         coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
         relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
         relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2

timm/models/swin_transformer_v2_cr.py

@@ -194,9 +194,16 @@ class WindowMultiHeadAttention(nn.Module):
     def _make_pair_wise_relative_positions(self) -> None:
         """Method initializes the pair-wise relative positions to compute the positional biases."""
         device = self.logit_scale.device
-        coordinates = torch.stack(torch.meshgrid([
+        coordinates = torch.stack(
+            torch.meshgrid(
+                [
                     torch.arange(self.window_size[0], device=device),
-            torch.arange(self.window_size[1], device=device)]), dim=0).flatten(1)
+                    torch.arange(self.window_size[1], device=device),
+                ],
+                indexing='ij',
+            ),
+            dim=0,
+        ).flatten(1)
         relative_coordinates = coordinates[:, :, None] - coordinates[:, None, :]
         relative_coordinates = relative_coordinates.permute(1, 2, 0).reshape(-1, 2).float()
         relative_coordinates_log = torch.sign(relative_coordinates) * torch.log(