lint fixes

Summary: Resolved trailing whitespace warnings.

Reviewed By: gkioxari

Differential Revision: D21023982

fbshipit-source-id: 14ea2ca372c13cfa987acc260264ca99ce44c461

pytorch3d/loss/point_mesh_distance.py

@@ -41,10 +41,10 @@ class _PointFaceDistance(Function):
                 in the corresponding example in the batch
             idxs: LongTensor of shape `(P,)` indicating the closest triangular face
                 in the corresponindg example in the batch.
-                
+
             `dists[p] = d(points[p], tris[idxs[p], 0], tris[idxs[p], 1], tris[idxs[p], 2])`
             where `d(u, v0, v1, v2)` is the distance of point `u` from the trianfular face `(v0, v1, v2)`
-            
+
         """
         dists, idxs = _C.point_face_dist_forward(
             points, points_first_idx, tris, tris_first_idx, max_points
@@ -91,7 +91,7 @@ class _FacePointDistance(Function):
                 corresponding example in the batch
             idxs: LongTensor of shape `(T,)` indicating the closest point in the
                 corresponindg example in the batch.
-                
+
             `dists[t] = d(points[idxs[t]], tris[t, 0], tris[t, 1], tris[t, 2])`,
             where `d(u, v0, v1, v2)` is the distance of point `u` from the triangular
             face `(v0, v1, v2)`.
@@ -141,7 +141,7 @@ class _PointEdgeDistance(Function):
                 corresponding example in the batch
             idxs: LongTensor of shape `(P,)` indicating the closest edge in the
                 corresponindg example in the batch.
-                
+
             `dists[p] = d(points[p], segms[idxs[p], 0], segms[idxs[p], 1])`,
             where `d(u, v0, v1)` is the distance of point `u` from the edge segment
             spanned by `(v0, v1)`.
@@ -191,7 +191,7 @@ class _EdgePointDistance(Function):
                 corresponding example in the batch
             idxs: LongTensor of shape `(S,)` indicating the closest point in the
                 corresponindg example in the batch.
-                
+
             `dists[s] = d(points[idxs[s]], edges[s, 0], edges[s, 1])`,
             where `d(u, v0, v1)` is the distance of point `u` from the segment
             spanned by `(v0, v1)`.
@@ -226,7 +226,7 @@ def point_mesh_edge_distance(meshes: Meshes, pcls: Pointclouds):
         to the closest edge segment in mesh and averages across all points in pcl
     `edge_point(mesh, pcl)`: Computes the squared distance of each edge segment in mesh
         to the closest point in pcl and averages across all edges in mesh.
-    
+
     The above distance functions are applied for all `(mesh, pcl)` pairs in the batch and
     then averaged across the batch.
 
@@ -293,7 +293,7 @@ def point_mesh_face_distance(meshes: Meshes, pcls: Pointclouds):
         to the closest triangular face in mesh and averages across all points in pcl
     `face_point(mesh, pcl)`: Computes the squared distance of each triangular face in mesh
         to the closest point in pcl and averages across all faces in mesh.
-    
+
     The above distance functions are applied for all `(mesh, pcl)` pairs in the batch and
     then averaged across the batch.
 

pytorch3d/ops/knn.py

@@ -125,7 +125,7 @@ def knn_points(
         p1_dists: Tensor of shape (N, P1, K) giving the squared distances to
             the nearest neighbors. This is padded with zeros both where a cloud in p2
             has fewer than K points and where a cloud in p1 has fewer than P1 points.
-        
+
         p2_nn: Tensor of shape (N, P1, K, D) giving the K nearest neighbors in p2 for
             each point in p1. Concretely, `p2_nn[n, i, k]` gives the k-th nearest neighbor
             for `p1[n, i]`. Returned if `return_nn` is True.
@@ -134,7 +134,7 @@ def knn_points(
             .. code-block::
 
                 p2_nn = knn_gather(p2, p1_idx, lengths2)
-            
+
             which is a helper function that allows indexing any tensor of shape (N, P2, U) with
             the indices `p1_idx` returned by `knn_points`. The outout is a tensor
             of shape (N, P1, K, U).
@@ -168,7 +168,7 @@ def knn_gather(
     """
     A helper function for knn that allows indexing a tensor x with the indices `idx`
     returned by `knn_points`.
-    
+
     For example, if `dists, idx = knn_points(p, x, lengths_p, lengths, K)`
     where p is a tensor of shape (N, L, D) and x a tensor of shape (N, M, D),
     then one can compute the K nearest neighbors of p with `p_nn = knn_gather(x, idx, lengths)`.

tests/test_point_mesh_distance.py

@@ -132,7 +132,7 @@ class TestPointMeshDistance(TestCaseMixin, unittest.TestCase):
             edge: FloatTensor of shape (2, 3)
         Returns:
             dist: FloatTensor of shape (1)
-        
+
         If a, b are the start and end points of the segments, we
         parametrize a point p as
             x(t) = a + t * (b - a)
@@ -165,7 +165,7 @@ class TestPointMeshDistance(TestCaseMixin, unittest.TestCase):
             point: FloatTensor of shape (3)
             tri: FloatTensor of shape (3, 3)
         Returns:
-            dist: FloatTensor of shape (1) 
+            dist: FloatTensor of shape (1)
         """
         a, b, c = tri.unbind(0)
         cross = torch.cross(b - a, c - a)