Fix: Pass meshgrid() indexing argument to avoid pytorch warning

detectron2/modeling/anchor_generator.py

@@ -49,7 +49,7 @@ def _create_grid_offsets(
         target_device_tensor,
     )
 
-    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)
     return shift_x, shift_y

detectron2/modeling/backbone/swin.py

@@ -116,7 +116,7 @@ def __init__(
         # 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

projects/DensePose/densepose/modeling/losses/cycle_pix2shape.py

@@ -24,7 +24,7 @@ def _create_pixel_dist_matrix(grid_size: int) -> torch.Tensor:
     # row = i // grid_size
     # col = i % grid_size
     pix_coords = (
-        torch.stack(torch.meshgrid(rows, cols), -1).reshape((grid_size * grid_size, 2)).float()
+        torch.stack(torch.meshgrid(rows, cols, indexing="ij"), -1).reshape((grid_size * grid_size, 2)).float()
     )
     return squared_euclidean_distance_matrix(pix_coords, pix_coords)
 
@@ -130,7 +130,7 @@ def forward(
                 )
                 # pixel distances [M, M]
                 pixel_dists = self.pixel_dists.to(pixel_embeddings.device)[
-                    torch.meshgrid(pixel_indices_flattened, pixel_indices_flattened)
+                    torch.meshgrid(pixel_indices_flattened, pixel_indices_flattened, indexing="ij")
                 ]
                 # pixel embeddings [M, D]
                 pixel_embeddings_sampled = normalize_embeddings(

projects/DensePose/densepose/modeling/losses/cycle_shape2shape.py

@@ -91,7 +91,7 @@ def _get_embeddings_and_geodists_for_mesh(
         geodists = mesh.geodists
         if indices is not None:
             embeddings = embeddings[indices]
-            geodists = geodists[torch.meshgrid(indices, indices)]
+            geodists = geodists[torch.meshgrid(indices, indices, indexing="ij")]
         return embeddings, geodists
 
     def _forward_one_pair(

projects/DensePose/tests/test_video_keyframe_dataset.py

@@ -19,7 +19,7 @@
 
 # copied from torchvision test/test_io.py
 def _create_video_frames(num_frames, height, width):
-    y, x = torch.meshgrid(torch.linspace(-2, 2, height), torch.linspace(-2, 2, width))
+    y, x = torch.meshgrid(torch.linspace(-2, 2, height), torch.linspace(-2, 2, width), indexing="ij")
     data = []
     for i in range(num_frames):
         xc = float(i) / num_frames

projects/Panoptic-DeepLab/panoptic_deeplab/post_processing.py

@@ -60,6 +60,7 @@ def group_pixels(center_points, offsets):
     y_coord, x_coord = torch.meshgrid(
         torch.arange(height, dtype=offsets.dtype, device=offsets.device),
         torch.arange(width, dtype=offsets.dtype, device=offsets.device),
+        indexing="ij",
     )
     coord = torch.cat((y_coord.unsqueeze(0), x_coord.unsqueeze(0)), dim=0)
 

projects/TensorMask/tensormask/arch.py

@@ -247,7 +247,7 @@ def grid_anchors_with_unit_lengths_and_indexes(self, grid_sizes):
             shifts_y = torch.arange(
                 0, grid_height * stride, step=stride, dtype=torch.float32, device=device
             )
-            shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
+            shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x, indexing="ij")
             shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=2)
             # Stack anchors in shapes of (HWA, 4)
             cur_anchor = (shifts[:, :, None, :] + base_anchors.view(1, 1, -1, 4)).view(-1, 4)
@@ -261,7 +261,7 @@ def grid_anchors_with_unit_lengths_and_indexes(self, grid_sizes):
             shifts_h = torch.arange(0, grid_height, dtype=torch.int64, device=device)
             shifts_w = torch.arange(0, grid_width, dtype=torch.int64, device=device)
             shifts_a = torch.arange(0, base_anchors.shape[0], dtype=torch.int64, device=device)
-            grids = torch.meshgrid(shifts_l, shifts_i, shifts_h, shifts_w, shifts_a)
+            grids = torch.meshgrid(shifts_l, shifts_i, shifts_h, shifts_w, shifts_a, indexing="ij")
 
             indexes.append(torch.stack(grids, dim=5).view(-1, 5))
 

tests/layers/test_mask_ops.py

@@ -44,7 +44,7 @@ def rasterize_polygons_with_grid_sample(full_image_bit_mask, box, mask_size, thr
 
     mask_x = (mask_x - 0.5) / (img_w - 1) * 2 + -1
     mask_y = (mask_y - 0.5) / (img_h - 1) * 2 + -1
-    gy, gx = torch.meshgrid(torch.from_numpy(mask_y), torch.from_numpy(mask_x))
+    gy, gx = torch.meshgrid(torch.from_numpy(mask_y), torch.from_numpy(mask_x), indexing="ij")
     ind = torch.stack([gx, gy], dim=-1).to(dtype=torch.float32)
 
     full_image_bit_mask = torch.from_numpy(full_image_bit_mask)