spelling and flake

Summary: mostly recent lintish things

Reviewed By: nikhilaravi

Differential Revision: D21089003

fbshipit-source-id: 028733c1d875268f1879e4481da475b7100ba0b6

pytorch3d/csrc/utils/geometry_utils.cuh

@@ -43,7 +43,7 @@ EdgeFunctionForward(const float2& p, const float2& v0, const float2& v1) {
 //     grad_edge: Upstream gradient for output from edge function.
 //
 // Returns:
-//     tuple of  gradients for each of the input points:
+//     tuple of gradients for each of the input points:
 //     (float2 d_edge_dp, float2 d_edge_dv0, float2 d_edge_dv1)
 //
 __device__ inline thrust::tuple<float2, float2, float2> EdgeFunctionBackward(

pytorch3d/csrc/utils/geometry_utils.h

@@ -56,7 +56,7 @@ T EdgeFunctionForward(const vec2<T>& p, const vec2<T>& v0, const vec2<T>& v1) {
 //     grad_edge: Upstream gradient for output from edge function.
 //
 // Returns:
-//     tuple of  gradients for each of the input points:
+//     tuple of gradients for each of the input points:
 //     (vec2<T> d_edge_dp, vec2<T> d_edge_dv0, vec2<T> d_edge_dv1)
 //
 template <typename T>

pytorch3d/loss/point_mesh_distance.py

@@ -40,10 +40,12 @@ class _PointFaceDistance(Function):
                 euclidean distance of `p`-th point to the closest triangular face
                 in the corresponding example in the batch
             idxs: LongTensor of shape `(P,)` indicating the closest triangular face
-                in the corresponindg example in the batch.
+                in the corresponding 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[p]` is
+            `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 triangular
+            face `(v0, v1, v2)`
 
         """
         dists, idxs = _C.point_face_dist_forward(
@@ -90,7 +92,7 @@ class _FacePointDistance(Function):
                 euclidean distance of `t`-th trianguar face to the closest point in the
                 corresponding example in the batch
             idxs: LongTensor of shape `(T,)` indicating the closest point in the
-                corresponindg example in the batch.
+                corresponding 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
@@ -140,7 +142,7 @@ class _PointEdgeDistance(Function):
                 euclidean distance of `p`-th point to the closest edge in the
                 corresponding example in the batch
             idxs: LongTensor of shape `(P,)` indicating the closest edge in the
-                corresponindg example in the batch.
+                corresponding 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
@@ -190,7 +192,7 @@ class _EdgePointDistance(Function):
                 euclidean distance of `s`-th edge to the closest point in the
                 corresponding example in the batch
             idxs: LongTensor of shape `(S,)` indicating the closest point in the
-                corresponindg example in the batch.
+                corresponding 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
@@ -227,8 +229,8 @@ def point_mesh_edge_distance(meshes: Meshes, pcls: Pointclouds):
     `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.
+    The above distance functions are applied for all `(mesh, pcl)` pairs in the batch
+    and then averaged across the batch.
 
     Args:
         meshes: A Meshes data structure containing N meshes
@@ -239,7 +241,7 @@ def point_mesh_edge_distance(meshes: Meshes, pcls: Pointclouds):
             between all `(mesh, pcl)` in a batch averaged across the batch.
     """
     if len(meshes) != len(pcls):
-        raise ValueError("meshes and pointclouds be equal sized batches")
+        raise ValueError("meshes and pointclouds must be equal sized batches")
     N = len(meshes)
 
     # packed representation for pointclouds
@@ -259,7 +261,7 @@ def point_mesh_edge_distance(meshes: Meshes, pcls: Pointclouds):
         points, points_first_idx, segms, segms_first_idx, max_points
     )
 
-    # weigh each example by the inverse of number of points in the example
+    # weight each example by the inverse of number of points in the example
     point_to_cloud_idx = pcls.packed_to_cloud_idx()  # (sum(P_i), )
     num_points_per_cloud = pcls.num_points_per_cloud()  # (N,)
     weights_p = num_points_per_cloud.gather(0, point_to_cloud_idx)
@@ -272,7 +274,7 @@ def point_mesh_edge_distance(meshes: Meshes, pcls: Pointclouds):
         points, points_first_idx, segms, segms_first_idx, max_segms
     )
 
-    # weigh each example by the inverse of number of edges in the example
+    # weight each example by the inverse of number of edges in the example
     segm_to_mesh_idx = meshes.edges_packed_to_mesh_idx()  # (sum(S_n),)
     num_segms_per_mesh = meshes.num_edges_per_mesh()  # (N,)
     weights_s = num_segms_per_mesh.gather(0, segm_to_mesh_idx)
@@ -291,11 +293,11 @@ def point_mesh_face_distance(meshes: Meshes, pcls: Pointclouds):
 
     `point_face(mesh, pcl)`: Computes the squared distance of each point p in pcl
         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.
+    `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.
+    The above distance functions are applied for all `(mesh, pcl)` pairs in the batch
+    and then averaged across the batch.
 
     Args:
         meshes: A Meshes data structure containing N meshes
@@ -327,7 +329,7 @@ def point_mesh_face_distance(meshes: Meshes, pcls: Pointclouds):
         points, points_first_idx, tris, tris_first_idx, max_points
     )
 
-    # weigh each example by the inverse of number of points in the example
+    # weight each example by the inverse of number of points in the example
     point_to_cloud_idx = pcls.packed_to_cloud_idx()  # (sum(P_i),)
     num_points_per_cloud = pcls.num_points_per_cloud()  # (N,)
     weights_p = num_points_per_cloud.gather(0, point_to_cloud_idx)
@@ -340,7 +342,7 @@ def point_mesh_face_distance(meshes: Meshes, pcls: Pointclouds):
         points, points_first_idx, tris, tris_first_idx, max_tris
     )
 
-    # weigh each example by the inverse of number of faces in the example
+    # weight each example by the inverse of number of faces in the example
     tri_to_mesh_idx = meshes.faces_packed_to_mesh_idx()  # (sum(T_n),)
     num_tris_per_mesh = meshes.num_faces_per_mesh()  # (N, )
     weights_t = num_tris_per_mesh.gather(0, tri_to_mesh_idx)

pytorch3d/ops/perspective_n_points.py

@@ -3,7 +3,7 @@
 """
 This file contains Efficient PnP algorithm for Perspective-n-Points problem.
 It finds a camera position (defined by rotation `R` and translation `T`) that
-minimises re-projection error between the given 3D points `x` and
+minimizes re-projection error between the given 3D points `x` and
 the corresponding uncalibrated 2D points `y`.
 """
 

pytorch3d/structures/pointclouds.py

@@ -1020,7 +1020,8 @@ class Pointclouds(object):
                     box[..., 0, :] gives the min x, y & z.
                     box[..., 1, :] gives the max x, y & z.
         Returns:
-            idx: BoolTensor of length sum(P_i) indicating whether the packed points are within the input box.
+            idx: BoolTensor of length sum(P_i) indicating whether the packed points are
+                within the input box.
         """
         if box.dim() > 3 or box.dim() < 2:
             raise ValueError("Input box must be of shape (2, 3) or (N, 2, 3).")

tests/test_points_alignment.py

@@ -88,7 +88,7 @@ class TestICP(TestCaseMixin, unittest.TestCase):
     def test_init_transformation(self, batch_size=10):
         """
         First runs a full ICP on a random problem. Then takes a given point
-        in the history of  ICP iteration transformations, initializes
+        in the history of ICP iteration transformations, initializes
         a second run of ICP with this transformation and checks whether
         both runs ended with the same solution.
         """