423A35C7/pytorch3d
1
0
Fork 0
mirror of https://github.com/facebookresearch/pytorch3d.git synced 2025-08-02 03:42:50 +08:00
Code Issues Packages Projects Releases Wiki Activity

spelling and flake

Browse Source 
Summary: mostly recent lintish things

Reviewed By: nikhilaravi

Differential Revision: D21089003

fbshipit-source-id: 028733c1d875268f1879e4481da475b7100ba0b6
This commit is contained in:
Jeremy Reizenstein 2020-04-17 10:38:47 -07:00 committed by Facebook GitHub Bot
parent 9397cd872d
commit 6207c359b1
6 changed files with 25 additions and 22 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
pytorch3d/csrc/utils/geometry_utils.cuh
View File

@ -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(

2
pytorch3d/csrc/utils/geometry_utils.h
View File

@ -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>

36
pytorch3d/loss/point_mesh_distance.py
View File

@ -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)

2
pytorch3d/ops/perspective_n_points.py
View File

@ -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`.
"""

3
pytorch3d/structures/pointclouds.py
View File

@ -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).")

2
tests/test_points_alignment.py
View File

@ -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.
"""