GitLab

Summary: Move testing targets from pytorch3d/tests/TARGETS to pytorch3d/TARGETS.

Reviewed By: shapovalov

Differential Revision: D36186940

fbshipit-source-id: a4c52c4d99351f885e2b0bf870532d530324039b

Summary:
Applies new import merging and sorting from µsort v1.0.

When merging imports, µsort will make a best-effort to move associated
comments to match merged elements, but there are known limitations due to
the diynamic nature of Python and developer tooling. These changes should
not produce any dangerous runtime changes, but may require touch-ups to
satisfy linters and other tooling.

Note that µsort uses case-insensitive, lexicographical sorting, which
results in a different ordering compared to isort. This provides a more
consistent sorting order, matching the case-insensitive order used when
sorting import statements by module name, and ensures that "frog", "FROG",
and "Frog" always sort next to each other.

For details on µsort's sorting and merging semantics, see the user guide:
https://usort.readthedocs.io/en/stable/guide.html#sorting

Reviewed By: bottler

Differential Revision: D35553814

fbshipit-source-id: be49bdb6a4c25264ff8d4db3a601f18736d17be1

Summary: Update all FB license strings to the new format.

Reviewed By: patricklabatut

Differential Revision: D33403538

fbshipit-source-id: 97a4596c5c888f3c54f44456dc07e718a387a02c

Summary:
Implementation of ball query from PointNet++.  This function is similar to KNN (find the neighbors in p2 for all points in p1). These are the key differences:
-  It will return the **first** K neighbors within a specified radius as opposed to the **closest** K neighbors.
- As all the points in p2 do not need to be considered to find the closest K, the algorithm is much faster than KNN when p2 has a large number of points.
- The neighbors are not sorted
- Due to the radius threshold it is not guaranteed that there will be K neighbors even if there are more than K points in p2.
- The padding value for `idx` is -1 instead of 0.

# Note:
- Some of the code is very similar to KNN so it could be possible to modify the KNN forward kernels to support ball query.
- Some users might want to use kNN with ball query - for this we could provide a wrapper function around the current `knn_points` which enables applying the radius threshold afterwards as an alternative. This could be called `ball_query_knn`.

Reviewed By: jcjohnson

Differential Revision: D30261362

fbshipit-source-id: 66b6a7e0114beff7164daf7eba21546ff41ec450