Address black + isort fbsource linter warnings

Summary: Address black + isort fbsource linter warnings from D20558374 (previous diff)

Reviewed By: nikhilaravi

Differential Revision: D20558373

fbshipit-source-id: d3607de4a01fb24c0d5269634563a7914bddf1c8

pytorch3d/loss/__init__.py

@@ -6,4 +6,5 @@ from .mesh_edge_loss import mesh_edge_loss
 from .mesh_laplacian_smoothing import mesh_laplacian_smoothing
 from .mesh_normal_consistency import mesh_normal_consistency
 
+
 __all__ = [k for k in globals().keys() if not k.startswith("_")]

pytorch3d/loss/chamfer.py

@@ -2,13 +2,10 @@
 
 import torch
 import torch.nn.functional as F
-
 from pytorch3d.ops.nearest_neighbor_points import nn_points_idx
 
 
-def _validate_chamfer_reduction_inputs(
-    batch_reduction: str, point_reduction: str
-):
+def _validate_chamfer_reduction_inputs(batch_reduction: str, point_reduction: str):
     """Check the requested reductions are valid.
 
     Args:
@@ -18,17 +15,11 @@ def _validate_chamfer_reduction_inputs(
             points, can be one of ["none", "mean", "sum"].
     """
     if batch_reduction not in ["none", "mean", "sum"]:
-        raise ValueError(
-            'batch_reduction must be one of ["none", "mean", "sum"]'
-        )
+        raise ValueError('batch_reduction must be one of ["none", "mean", "sum"]')
     if point_reduction not in ["none", "mean", "sum"]:
-        raise ValueError(
-            'point_reduction must be one of ["none", "mean", "sum"]'
-        )
+        raise ValueError('point_reduction must be one of ["none", "mean", "sum"]')
     if batch_reduction == "none" and point_reduction == "none":
-        raise ValueError(
-            'batch_reduction and point_reduction cannot both be "none".'
-        )
+        raise ValueError('batch_reduction and point_reduction cannot both be "none".')
 
 
 def chamfer_distance(
@@ -87,10 +78,7 @@ def chamfer_distance(
                     (x.sum((1, 2)) * weights).sum() * 0.0,
                     (x.sum((1, 2)) * weights).sum() * 0.0,
                 )
-            return (
-                (x.sum((1, 2)) * weights) * 0.0,
-                (x.sum((1, 2)) * weights) * 0.0,
-            )
+            return ((x.sum((1, 2)) * weights) * 0.0, (x.sum((1, 2)) * weights) * 0.0)
 
     return_normals = x_normals is not None and y_normals is not None
     cham_norm_x = x.new_zeros(())

pytorch3d/loss/mesh_normal_consistency.py

@@ -2,6 +2,7 @@
 
 
 from itertools import islice
+
 import torch
 
 
@@ -76,10 +77,7 @@ def mesh_normal_consistency(meshes):
     with torch.no_grad():
         edge_idx = face_to_edge.reshape(F * 3)  # (3 * F,) indexes into edges
         vert_idx = (
-            faces_packed.view(1, F, 3)
-            .expand(3, F, 3)
-            .transpose(0, 1)
-            .reshape(3 * F, 3)
+            faces_packed.view(1, F, 3).expand(3, F, 3).transpose(0, 1).reshape(3 * F, 3)
         )
         edge_idx, edge_sort_idx = edge_idx.sort()
         vert_idx = vert_idx[edge_sort_idx]
@@ -132,9 +130,7 @@ def mesh_normal_consistency(meshes):
     loss = 1 - torch.cosine_similarity(n0, n1, dim=1)
 
     verts_packed_to_mesh_idx = verts_packed_to_mesh_idx[vert_idx[:, 0]]
-    verts_packed_to_mesh_idx = verts_packed_to_mesh_idx[
-        vert_edge_pair_idx[:, 0]
-    ]
+    verts_packed_to_mesh_idx = verts_packed_to_mesh_idx[vert_edge_pair_idx[:, 0]]
     num_normals = verts_packed_to_mesh_idx.bincount(minlength=N)
     weights = 1.0 / num_normals[verts_packed_to_mesh_idx].float()