fix recent lint

Summary: lint clean again

Reviewed By: patricklabatut

Differential Revision: D20868775

fbshipit-source-id: ade4301c1012c5c6943186432465215701d635a9

pytorch3d/ops/points_alignment.py

@@ -1,12 +1,12 @@
 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 import warnings
-from typing import List, Optional, Tuple, Union
-import torch
+from typing import List, Tuple, Union
 
-from pytorch3d.structures.pointclouds import Pointclouds
-from pytorch3d.structures import utils as strutil
+import torch
 from pytorch3d.ops import utils as oputil
+from pytorch3d.structures import utils as strutil
+from pytorch3d.structures.pointclouds import Pointclouds
 
 
 def corresponding_points_alignment(
@@ -77,9 +77,7 @@ def corresponding_points_alignment(
             weights = strutil.list_to_padded(weights)[..., 0]
 
         if Xt.shape[:2] != weights.shape:
-            raise ValueError(
-                "weights should have the same first two dimensions as X."
-            )
+            raise ValueError("weights should have the same first two dimensions as X.")
 
     b, n, dim = Xt.shape
 
@@ -120,9 +118,7 @@ def corresponding_points_alignment(
     U, S, V = torch.svd(XYcov)
 
     # identity matrix used for fixing reflections
-    E = torch.eye(dim, dtype=XYcov.dtype, device=XYcov.device)[None].repeat(
-        b, 1, 1
-    )
+    E = torch.eye(dim, dtype=XYcov.dtype, device=XYcov.device)[None].repeat(b, 1, 1)
 
     if not allow_reflection:
         # reflection test:

pytorch3d/ops/utils.py

@@ -27,7 +27,7 @@ def wmean(
         * if `weights` is None => `mean(x, dim)`,
         * otherwise => `sum(x*w, dim) / max{sum(w, dim), eps}`.
     """
-    args = dict(dim=dim, keepdim=keepdim)
+    args = {"dim": dim, "keepdim": keepdim}
 
     if weight is None:
         return x.mean(**args)
@@ -38,7 +38,6 @@ def wmean(
     ):
         raise ValueError("wmean: weights are not compatible with the tensor")
 
-    return (
-        (x * weight[..., None]).sum(**args)
-        / weight[..., None].sum(**args).clamp(eps)
+    return (x * weight[..., None]).sum(**args) / weight[..., None].sum(**args).clamp(
+        eps
     )