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lint fixes
Summary: Resolved trailing whitespace warnings. Reviewed By: gkioxari Differential Revision: D21023982 fbshipit-source-id: 14ea2ca372c13cfa987acc260264ca99ce44c461
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@ -41,10 +41,10 @@ class _PointFaceDistance(Function):
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in the corresponding example in the batch
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idxs: LongTensor of shape `(P,)` indicating the closest triangular face
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in the corresponindg example in the batch.
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`dists[p] = d(points[p], tris[idxs[p], 0], tris[idxs[p], 1], tris[idxs[p], 2])`
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where `d(u, v0, v1, v2)` is the distance of point `u` from the trianfular face `(v0, v1, v2)`
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"""
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dists, idxs = _C.point_face_dist_forward(
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points, points_first_idx, tris, tris_first_idx, max_points
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@ -91,7 +91,7 @@ class _FacePointDistance(Function):
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corresponding example in the batch
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idxs: LongTensor of shape `(T,)` indicating the closest point in the
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corresponindg example in the batch.
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`dists[t] = d(points[idxs[t]], tris[t, 0], tris[t, 1], tris[t, 2])`,
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where `d(u, v0, v1, v2)` is the distance of point `u` from the triangular
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face `(v0, v1, v2)`.
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@ -141,7 +141,7 @@ class _PointEdgeDistance(Function):
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corresponding example in the batch
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idxs: LongTensor of shape `(P,)` indicating the closest edge in the
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corresponindg example in the batch.
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`dists[p] = d(points[p], segms[idxs[p], 0], segms[idxs[p], 1])`,
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where `d(u, v0, v1)` is the distance of point `u` from the edge segment
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spanned by `(v0, v1)`.
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@ -191,7 +191,7 @@ class _EdgePointDistance(Function):
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corresponding example in the batch
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idxs: LongTensor of shape `(S,)` indicating the closest point in the
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corresponindg example in the batch.
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`dists[s] = d(points[idxs[s]], edges[s, 0], edges[s, 1])`,
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where `d(u, v0, v1)` is the distance of point `u` from the segment
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spanned by `(v0, v1)`.
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@ -226,7 +226,7 @@ def point_mesh_edge_distance(meshes: Meshes, pcls: Pointclouds):
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to the closest edge segment in mesh and averages across all points in pcl
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`edge_point(mesh, pcl)`: Computes the squared distance of each edge segment in mesh
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to the closest point in pcl and averages across all edges in mesh.
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The above distance functions are applied for all `(mesh, pcl)` pairs in the batch and
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then averaged across the batch.
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@ -293,7 +293,7 @@ def point_mesh_face_distance(meshes: Meshes, pcls: Pointclouds):
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to the closest triangular face in mesh and averages across all points in pcl
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`face_point(mesh, pcl)`: Computes the squared distance of each triangular face in mesh
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to the closest point in pcl and averages across all faces in mesh.
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The above distance functions are applied for all `(mesh, pcl)` pairs in the batch and
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then averaged across the batch.
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@ -125,7 +125,7 @@ def knn_points(
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p1_dists: Tensor of shape (N, P1, K) giving the squared distances to
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the nearest neighbors. This is padded with zeros both where a cloud in p2
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has fewer than K points and where a cloud in p1 has fewer than P1 points.
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p2_nn: Tensor of shape (N, P1, K, D) giving the K nearest neighbors in p2 for
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each point in p1. Concretely, `p2_nn[n, i, k]` gives the k-th nearest neighbor
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for `p1[n, i]`. Returned if `return_nn` is True.
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@ -134,7 +134,7 @@ def knn_points(
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.. code-block::
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p2_nn = knn_gather(p2, p1_idx, lengths2)
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which is a helper function that allows indexing any tensor of shape (N, P2, U) with
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the indices `p1_idx` returned by `knn_points`. The outout is a tensor
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of shape (N, P1, K, U).
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@ -168,7 +168,7 @@ def knn_gather(
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"""
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A helper function for knn that allows indexing a tensor x with the indices `idx`
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returned by `knn_points`.
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For example, if `dists, idx = knn_points(p, x, lengths_p, lengths, K)`
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where p is a tensor of shape (N, L, D) and x a tensor of shape (N, M, D),
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then one can compute the K nearest neighbors of p with `p_nn = knn_gather(x, idx, lengths)`.
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@ -132,7 +132,7 @@ class TestPointMeshDistance(TestCaseMixin, unittest.TestCase):
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edge: FloatTensor of shape (2, 3)
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Returns:
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dist: FloatTensor of shape (1)
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If a, b are the start and end points of the segments, we
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parametrize a point p as
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x(t) = a + t * (b - a)
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@ -165,7 +165,7 @@ class TestPointMeshDistance(TestCaseMixin, unittest.TestCase):
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point: FloatTensor of shape (3)
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tri: FloatTensor of shape (3, 3)
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Returns:
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dist: FloatTensor of shape (1)
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dist: FloatTensor of shape (1)
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"""
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a, b, c = tri.unbind(0)
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cross = torch.cross(b - a, c - a)
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