suppress errors in vision/fair/pytorch3d

Differential Revision: D31496551

fbshipit-source-id: 705fd88f319875db3f7938a2946c48a51ea225f5

Author: Pyre Bot Jr

pytorch3d/ops/marching_cubes.py

@@ -117,7 +117,6 @@ def marching_cubes_naive(
     volume_data_batch = volume_data_batch.detach().cpu()
     batched_verts, batched_faces = [], []
     D, H, W = volume_data_batch.shape[1:]
-    # pyre-ignore [16]
     volume_size_xyz = volume_data_batch.new_tensor([W, H, D])[None]
 
     if return_local_coords:

pytorch3d/ops/points_to_volumes.py

@@ -435,7 +435,6 @@ def _add_points_features_to_volume_densities_features_python(
 
     if volume_features is None:
         # initialize features if not passed in
-        # pyre-fixme[16]: `Tensor` has no attribute `new_zeros`.
         volume_features_flatten = volume_densities.new_zeros(ba, feature_dim, n_voxels)
     else:
         # otherwise just flatten
@@ -478,7 +477,6 @@ def _check_points_to_volumes_inputs(
     mask: Optional[torch.Tensor] = None,
 ):
 
-    # pyre-fixme[16]: `Tuple` has no attribute `values`.
     max_grid_size = grid_sizes.max(dim=0).values
     if torch.prod(max_grid_size) > volume_densities.shape[1]:
         raise ValueError(

pytorch3d/ops/sample_farthest_points.py

@@ -134,7 +134,6 @@ def sample_farthest_points_naive(
         # Initialize closest distances to inf, shape: (P,)
         # This will be updated at each iteration to track the closest distance of the
         # remaining points to any of the selected points
-        # pyre-fixme[16]: `torch.Tensor` has no attribute new_full.
         closest_dists = points.new_full(
             (lengths[n],), float("inf"), dtype=torch.float32
         )

pytorch3d/renderer/blending.py

@@ -67,7 +67,7 @@ def hard_rgb_blend(
     if isinstance(background_color_, torch.Tensor):
         background_color = background_color_.to(device)
     else:
-        background_color = colors.new_tensor(background_color_)  # pyre-fixme[16]
+        background_color = colors.new_tensor(background_color_)
 
     # Find out how much background_color needs to be expanded to be used for masked_scatter.
     num_background_pixels = is_background.sum()
@@ -217,7 +217,6 @@ def softmax_rgb_blend(
         znear = znear[:, None, None, None]
 
     z_inv = (zfar - fragments.zbuf) / (zfar - znear) * mask
-    # pyre-fixme[16]: `Tuple` has no attribute `values`.
     z_inv_max = torch.max(z_inv, dim=-1).values[..., None].clamp(min=eps)
     weights_num = prob_map * torch.exp((z_inv - z_inv_max) / blend_params.gamma)
 

pytorch3d/renderer/implicit/raysampling.py

@@ -298,7 +298,7 @@ def _xy_to_ray_bundle(
             .reshape(batch_size, n_rays_per_image * 2, 2),
             torch.cat(
                 (
-                    xy_grid.new_ones(batch_size, n_rays_per_image, 1),  # pyre-ignore
+                    xy_grid.new_ones(batch_size, n_rays_per_image, 1),
                     2.0 * xy_grid.new_ones(batch_size, n_rays_per_image, 1),
                 ),
                 dim=1,

pytorch3d/renderer/mesh/rasterize_meshes.py

@@ -475,7 +475,6 @@ def rasterize_meshes_python(  # noqa: C901
     )
 
     # Calculate all face bounding boxes.
-    # pyre-fixme[16]: `Tuple` has no attribute `values`.
     x_mins = torch.min(faces_verts[:, :, 0], dim=1, keepdim=True).values
     x_maxs = torch.max(faces_verts[:, :, 0], dim=1, keepdim=True).values
     y_mins = torch.min(faces_verts[:, :, 1], dim=1, keepdim=True).values

pytorch3d/renderer/mesh/textures.py

@@ -1134,7 +1134,6 @@ def join_scene(self) -> "TexturesUV":
             )
         merging_plan = pack_unique_rectangles(heights_and_widths)
         C = maps[0].shape[-1]
-        # pyre-fixme[16]: `Tensor` has no attribute `new_zeros`.
         single_map = maps[0].new_zeros((*merging_plan.total_size, C))
         verts_uvs = self.verts_uvs_list()
         verts_uvs_merged = []

pytorch3d/structures/utils.py

@@ -52,7 +52,6 @@ def list_to_padded(
 
     # replace empty 1D tensors with empty tensors with a correct number of dimensions
     x = [
-        # pyre-fixme[16]: `Tensor` has no attribute `new_zeros`.
         (y.new_zeros([0] * element_ndim) if (y.ndim == 1 and y.nelement() == 0) else y)
         for y in x
     ]

pytorch3d/transforms/rotation_conversions.py

@@ -142,7 +142,6 @@ def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
 
     # We floor here at 0.1 but the exact level is not important; if q_abs is small,
     # the candidate won't be picked.
-    # pyre-ignore [16]: `torch.Tensor` has no attribute `new_tensor`.
     quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(q_abs.new_tensor(0.1)))
 
     # if not for numerical problems, quat_candidates[i] should be same (up to a sign),

pytorch3d/vis/plotly_vis.py

@@ -791,7 +791,7 @@ def _add_ray_bundle_trace(
     # make ray line endpoints
     min_max_ray_depth = torch.stack(
         [
-            ray_bundle_flat.lengths.min(dim=1).values,  # pyre-ignore[16]
+            ray_bundle_flat.lengths.min(dim=1).values,
             ray_bundle_flat.lengths.max(dim=1).values,
         ],
         dim=-1,