21 Commits

Author SHA1 Message Date
Thomas Polasek
055ab3a2e3 Convert directory fbcode/vision to use the Ruff Formatter
Summary:
Converts the directory specified to use the Ruff formatter in pyfmt

ruff_dog

If this diff causes merge conflicts when rebasing, please run
`hg status -n -0 --change . -I '**/*.{py,pyi}' | xargs -0 arc pyfmt`
on your diff, and amend any changes before rebasing onto latest.
That should help reduce or eliminate any merge conflicts.

allow-large-files

Reviewed By: bottler

Differential Revision: D66472063

fbshipit-source-id: 35841cb397e4f8e066e2159550d2f56b403b1bef
2024-11-26 02:38:20 -08:00
Jeremy Reizenstein
34f648ede0 move targets
Summary: Move testing targets from pytorch3d/tests/TARGETS to pytorch3d/TARGETS.

Reviewed By: shapovalov

Differential Revision: D36186940

fbshipit-source-id: a4c52c4d99351f885e2b0bf870532d530324039b
2022-05-25 06:16:03 -07:00
John Reese
bef959c755 formatting changes from black 22.3.0
Summary:
Applies the black-fbsource codemod with the new build of pyfmt.

paintitblack

Reviewed By: lisroach

Differential Revision: D36324783

fbshipit-source-id: 280c09e88257e5e569ab729691165d8dedd767bc
2022-05-11 19:55:56 -07:00
Tim Hatch
34bbb3ad32 apply import merging for fbcode/vision/fair (2 of 2)
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
2022-04-13 06:51:33 -07:00
Jeremy Reizenstein
9eeb456e82 Update license for company name
Summary: Update all FB license strings to the new format.

Reviewed By: patricklabatut

Differential Revision: D33403538

fbshipit-source-id: 97a4596c5c888f3c54f44456dc07e718a387a02c
2022-01-04 11:43:38 -08:00
Patrick Labatut
af93f34834 License lint codebase
Summary: License lint codebase

Reviewed By: theschnitz

Differential Revision: D29001799

fbshipit-source-id: 5c59869911785b0181b1663bbf430bc8b7fb2909
2021-06-22 03:45:27 -07:00
Jeremy Reizenstein
0ca839cc32 avoid running tests twice
Summary: Avoid test files explicitly importing TestCase objects from each other, because doing so causes the tests to be discovered twice by unittest discover. This means moving a few static functions out of their classes. I noticed this while trying to fix failures from yesterday.

Reviewed By: nikhilaravi

Differential Revision: D28194679

fbshipit-source-id: ac6e6585603bd4ef9c098cdd56891d94f8923ba6
2021-05-07 05:04:08 -07:00
Jeremy Reizenstein
c18ee9d40a lint fixes
Summary: Lint after recent changes.

Reviewed By: nikhilaravi

Differential Revision: D27682328

fbshipit-source-id: 285d159010d886e4e97902995adbdff875fd3c19
2021-04-12 19:10:18 -07:00
Rong Rong (AI Infra)
1216b5765a Extract finding directories for test data
Summary: Make common functions for finding directories where test data is found, instead of lots of tests using their own `__file__`  while trying to get ./tests/data and the tutorials data.

Reviewed By: nikhilaravi

Differential Revision: D27633701

fbshipit-source-id: 1467bb6018cea16eba3cab097d713116d51071e9
2021-04-08 20:03:04 -07:00
Georgia Gkioxari
327bd2b976 extend sample_points_from_meshes with texture
Summary:
Enhanced `sample_points_from_meshes` with texture sampling

* This new feature is used to return textures corresponding to the sampled points in `sample_points_from_meshes`

Reviewed By: nikhilaravi

Differential Revision: D24031525

fbshipit-source-id: 8e5d8f784cc38aa391aa8e84e54423bd9fad7ad1
2020-10-06 09:17:58 -07:00
Nikhila Ravi
0eca74fa5f lint fixes
Summary:
Ran the linter.
TODO: need to update the linter as per D21353065.

Reviewed By: bottler

Differential Revision: D21362270

fbshipit-source-id: ad0e781de0a29f565ad25c43bc94a19b1828c020
2020-05-04 09:56:44 -07:00
Nikhila Ravi
c3d636dc8c Cuda updates
Summary:
Updates to:
- enable cuda kernel launches on any GPU (not just the default)
- cuda and contiguous checks for all kernels
- checks to ensure all tensors are on the same device
- error reporting in the cuda kernels
- cuda tests now run on a random device not just the default

Reviewed By: jcjohnson, gkioxari

Differential Revision: D21215280

fbshipit-source-id: 1bedc9fe6c35e9e920bdc4d78ed12865b1005519
2020-04-24 09:11:04 -07:00
Patrick Labatut
d57daa6f85 Address black + isort fbsource linter warnings
Summary: Address black + isort fbsource linter warnings from D20558374 (previous diff)

Reviewed By: nikhilaravi

Differential Revision: D20558373

fbshipit-source-id: d3607de4a01fb24c0d5269634563a7914bddf1c8
2020-03-29 14:51:02 -07:00
Jeremy Reizenstein
595aca27ea use assertClose
Summary: use assertClose in some tests, which enforces shape equality. Fixes some small problems, including graph_conv on an empty graph.

Reviewed By: nikhilaravi

Differential Revision: D20556912

fbshipit-source-id: 60a61eafe3c03ce0f6c9c1a842685708fb10ac5b
2020-03-23 11:36:38 -07:00
Georgia Gkioxari
03f441e7ca run lint
Summary: Run `/dev/linter.sh` to fix linting

Reviewed By: nikhilaravi

Differential Revision: D20584037

fbshipit-source-id: 69e45b33d22e3d54b6d37c3c35580bb3e9dc50a5
2020-03-21 17:58:15 -07:00
Georgia Gkioxari
6c48ff6ad9 replace view with reshape, check for nans
Summary: Replace view with reshape, add check for nans before mesh sampling

Reviewed By: nikhilaravi

Differential Revision: D20548456

fbshipit-source-id: c4e1b88e033ecb8f0f3a8f3a33a04ce13a5b5043
2020-03-19 19:31:41 -07:00
Patrick Labatut
3c71ab64cc Remove shebang line when not strictly required
Summary: The shebang line `#!<path to interpreter>` is only required for Python scripts, so remove it on source files for class or function definitions. Additionally explicitly mark as executable the actual Python scripts in the codebase.

Reviewed By: nikhilaravi

Differential Revision: D20095778

fbshipit-source-id: d312599fba485e978a243292f88a180d71e1b55a
2020-03-12 10:39:44 -07:00
Georgia Gkioxari
a3baa367e3 face areas backward
Summary:
Added backward for mesh face areas & normals. Exposed it as a layer. Replaced the computation with the new op in Meshes and in Sample Points.

Current issue: Circular imports. I moved the import of the op in meshes inside the function scope.

Reviewed By: jcjohnson

Differential Revision: D19920082

fbshipit-source-id: d213226d5e1d19a0c8452f4d32771d07e8b91c0a
2020-02-20 11:11:33 -08:00
Georgia Gkioxari
60f3c4e7d2 cpp support for packed to padded
Summary:
Cpu implementation for packed to padded and added gradients
```
Benchmark                                     Avg Time(μs)      Peak Time(μs) Iterations
--------------------------------------------------------------------------------
PACKED_TO_PADDED_2_100_300_1_cpu                    138             221           3625
PACKED_TO_PADDED_2_100_300_1_cuda:0                 184             261           2716
PACKED_TO_PADDED_2_100_300_16_cpu                   555             726            901
PACKED_TO_PADDED_2_100_300_16_cuda:0                179             260           2794
PACKED_TO_PADDED_2_100_3000_1_cpu                   396             519           1262
PACKED_TO_PADDED_2_100_3000_1_cuda:0                181             274           2764
PACKED_TO_PADDED_2_100_3000_16_cpu                 4517            5003            111
PACKED_TO_PADDED_2_100_3000_16_cuda:0               224             397           2235
PACKED_TO_PADDED_2_1000_300_1_cpu                   138             212           3616
PACKED_TO_PADDED_2_1000_300_1_cuda:0                180             282           2775
PACKED_TO_PADDED_2_1000_300_16_cpu                  565             711            885
PACKED_TO_PADDED_2_1000_300_16_cuda:0               179             264           2797
PACKED_TO_PADDED_2_1000_3000_1_cpu                  389             494           1287
PACKED_TO_PADDED_2_1000_3000_1_cuda:0               180             271           2777
PACKED_TO_PADDED_2_1000_3000_16_cpu                4522            5170            111
PACKED_TO_PADDED_2_1000_3000_16_cuda:0              216             286           2313
PACKED_TO_PADDED_10_100_300_1_cpu                   251             345           1995
PACKED_TO_PADDED_10_100_300_1_cuda:0                178             262           2806
PACKED_TO_PADDED_10_100_300_16_cpu                 2354            2750            213
PACKED_TO_PADDED_10_100_300_16_cuda:0               178             291           2814
PACKED_TO_PADDED_10_100_3000_1_cpu                 1519            1786            330
PACKED_TO_PADDED_10_100_3000_1_cuda:0               179             237           2791
PACKED_TO_PADDED_10_100_3000_16_cpu               24705           25879             21
PACKED_TO_PADDED_10_100_3000_16_cuda:0              228             316           2191
PACKED_TO_PADDED_10_1000_300_1_cpu                  261             432           1919
PACKED_TO_PADDED_10_1000_300_1_cuda:0               181             261           2756
PACKED_TO_PADDED_10_1000_300_16_cpu                2349            2770            213
PACKED_TO_PADDED_10_1000_300_16_cuda:0              180             256           2782
PACKED_TO_PADDED_10_1000_3000_1_cpu                1613            1929            310
PACKED_TO_PADDED_10_1000_3000_1_cuda:0              183             253           2739
PACKED_TO_PADDED_10_1000_3000_16_cpu              22041           23653             23
PACKED_TO_PADDED_10_1000_3000_16_cuda:0             220             343           2270
PACKED_TO_PADDED_32_100_300_1_cpu                   555             750            901
PACKED_TO_PADDED_32_100_300_1_cuda:0                188             282           2661
PACKED_TO_PADDED_32_100_300_16_cpu                 7550            8131             67
PACKED_TO_PADDED_32_100_300_16_cuda:0               181             272           2770
PACKED_TO_PADDED_32_100_3000_1_cpu                 4574            6327            110
PACKED_TO_PADDED_32_100_3000_1_cuda:0               173             254           2884
PACKED_TO_PADDED_32_100_3000_16_cpu               70366           72563              8
PACKED_TO_PADDED_32_100_3000_16_cuda:0              349             654           1433
PACKED_TO_PADDED_32_1000_300_1_cpu                  612             728            818
PACKED_TO_PADDED_32_1000_300_1_cuda:0               189             295           2647
PACKED_TO_PADDED_32_1000_300_16_cpu                7699            8254             65
PACKED_TO_PADDED_32_1000_300_16_cuda:0              189             311           2646
PACKED_TO_PADDED_32_1000_3000_1_cpu                5105            5261             98
PACKED_TO_PADDED_32_1000_3000_1_cuda:0              191             260           2625
PACKED_TO_PADDED_32_1000_3000_16_cpu              87073           92708              6
PACKED_TO_PADDED_32_1000_3000_16_cuda:0             344             425           1455
--------------------------------------------------------------------------------

Benchmark                                           Avg Time(μs)      Peak Time(μs) Iterations
--------------------------------------------------------------------------------
PACKED_TO_PADDED_TORCH_2_100_300_1_cpu                    492             627           1016
PACKED_TO_PADDED_TORCH_2_100_300_1_cuda:0                 768             975            652
PACKED_TO_PADDED_TORCH_2_100_300_16_cpu                   659             804            760
PACKED_TO_PADDED_TORCH_2_100_300_16_cuda:0                781             918            641
PACKED_TO_PADDED_TORCH_2_100_3000_1_cpu                   624             734            802
PACKED_TO_PADDED_TORCH_2_100_3000_1_cuda:0                778             929            643
PACKED_TO_PADDED_TORCH_2_100_3000_16_cpu                 2609            2850            192
PACKED_TO_PADDED_TORCH_2_100_3000_16_cuda:0               758             901            660
PACKED_TO_PADDED_TORCH_2_1000_300_1_cpu                   467             612           1072
PACKED_TO_PADDED_TORCH_2_1000_300_1_cuda:0                772             905            648
PACKED_TO_PADDED_TORCH_2_1000_300_16_cpu                  689             839            726
PACKED_TO_PADDED_TORCH_2_1000_300_16_cuda:0               789            1143            635
PACKED_TO_PADDED_TORCH_2_1000_3000_1_cpu                  629             735            795
PACKED_TO_PADDED_TORCH_2_1000_3000_1_cuda:0               812             916            616
PACKED_TO_PADDED_TORCH_2_1000_3000_16_cpu                2716            3117            185
PACKED_TO_PADDED_TORCH_2_1000_3000_16_cuda:0              844            1288            593
PACKED_TO_PADDED_TORCH_10_100_300_1_cpu                  2387            2557            210
PACKED_TO_PADDED_TORCH_10_100_300_1_cuda:0               4112            4993            122
PACKED_TO_PADDED_TORCH_10_100_300_16_cpu                 3385            4254            148
PACKED_TO_PADDED_TORCH_10_100_300_16_cuda:0              3959            4902            127
PACKED_TO_PADDED_TORCH_10_100_3000_1_cpu                 2918            3105            172
PACKED_TO_PADDED_TORCH_10_100_3000_1_cuda:0              4054            4450            124
PACKED_TO_PADDED_TORCH_10_100_3000_16_cpu               12748           13623             40
PACKED_TO_PADDED_TORCH_10_100_3000_16_cuda:0             4023            4395            125
PACKED_TO_PADDED_TORCH_10_1000_300_1_cpu                 2258            2492            222
PACKED_TO_PADDED_TORCH_10_1000_300_1_cuda:0              3997            4312            126
PACKED_TO_PADDED_TORCH_10_1000_300_16_cpu                3404            3597            147
PACKED_TO_PADDED_TORCH_10_1000_300_16_cuda:0             3877            4227            129
PACKED_TO_PADDED_TORCH_10_1000_3000_1_cpu                2789            3054            180
PACKED_TO_PADDED_TORCH_10_1000_3000_1_cuda:0             3821            4402            131
PACKED_TO_PADDED_TORCH_10_1000_3000_16_cpu              11967           12963             42
PACKED_TO_PADDED_TORCH_10_1000_3000_16_cuda:0            3729            4290            135
PACKED_TO_PADDED_TORCH_32_100_300_1_cpu                  6933            8152             73
PACKED_TO_PADDED_TORCH_32_100_300_1_cuda:0              11856           12287             43
PACKED_TO_PADDED_TORCH_32_100_300_16_cpu                 9895           11205             51
PACKED_TO_PADDED_TORCH_32_100_300_16_cuda:0             12354           13596             41
PACKED_TO_PADDED_TORCH_32_100_3000_1_cpu                 9516           10128             53
PACKED_TO_PADDED_TORCH_32_100_3000_1_cuda:0             12917           13597             39
PACKED_TO_PADDED_TORCH_32_100_3000_16_cpu               41209           43783             13
PACKED_TO_PADDED_TORCH_32_100_3000_16_cuda:0            12210           13288             41
PACKED_TO_PADDED_TORCH_32_1000_300_1_cpu                 7179            7689             70
PACKED_TO_PADDED_TORCH_32_1000_300_1_cuda:0             11896           12381             43
PACKED_TO_PADDED_TORCH_32_1000_300_16_cpu               10127           15494             50
PACKED_TO_PADDED_TORCH_32_1000_300_16_cuda:0            12034           12817             42
PACKED_TO_PADDED_TORCH_32_1000_3000_1_cpu                8743           10251             58
PACKED_TO_PADDED_TORCH_32_1000_3000_1_cuda:0            12023           12908             42
PACKED_TO_PADDED_TORCH_32_1000_3000_16_cpu              39071           41777             13
PACKED_TO_PADDED_TORCH_32_1000_3000_16_cuda:0           11999           13690             42
--------------------------------------------------------------------------------
```

Reviewed By: bottler, nikhilaravi, jcjohnson

Differential Revision: D19870575

fbshipit-source-id: 23a2477b73373c411899633386c87ab034c3702a
2020-02-19 10:48:54 -08:00
Georgia Gkioxari
29cd181a83 CPU implem for face areas normals
Summary:
Added cpu implementation for face areas normals. Moved test and bm to separate functions.

```
Benchmark                                   Avg Time(μs)      Peak Time(μs) Iterations
--------------------------------------------------------------------------------
FACE_AREAS_NORMALS_2_100_300_False                196             268           2550
FACE_AREAS_NORMALS_2_100_300_True                 106             179           4733
FACE_AREAS_NORMALS_2_100_3000_False              1447            1630            346
FACE_AREAS_NORMALS_2_100_3000_True                107             178           4674
FACE_AREAS_NORMALS_2_1000_300_False               201             309           2486
FACE_AREAS_NORMALS_2_1000_300_True                107             186           4673
FACE_AREAS_NORMALS_2_1000_3000_False             1451            1636            345
FACE_AREAS_NORMALS_2_1000_3000_True               107             186           4655
FACE_AREAS_NORMALS_10_100_300_False               767             918            653
FACE_AREAS_NORMALS_10_100_300_True                106             167           4712
FACE_AREAS_NORMALS_10_100_3000_False             7036            7754             72
FACE_AREAS_NORMALS_10_100_3000_True               113             164           4445
FACE_AREAS_NORMALS_10_1000_300_False              748             947            669
FACE_AREAS_NORMALS_10_1000_300_True               108             169           4638
FACE_AREAS_NORMALS_10_1000_3000_False            7069            7783             71
FACE_AREAS_NORMALS_10_1000_3000_True              108             172           4646
FACE_AREAS_NORMALS_32_100_300_False              2286            2496            219
FACE_AREAS_NORMALS_32_100_300_True                108             180           4631
FACE_AREAS_NORMALS_32_100_3000_False            23184           24369             22
FACE_AREAS_NORMALS_32_100_3000_True               159             213           3147
FACE_AREAS_NORMALS_32_1000_300_False             2414            2645            208
FACE_AREAS_NORMALS_32_1000_300_True               112             197           4480
FACE_AREAS_NORMALS_32_1000_3000_False           21687           22964             24
FACE_AREAS_NORMALS_32_1000_3000_True              141             211           3540
--------------------------------------------------------------------------------

Benchmark                                         Avg Time(μs)      Peak Time(μs) Iterations
--------------------------------------------------------------------------------
FACE_AREAS_NORMALS_TORCH_2_100_300_False               5465            5782             92
FACE_AREAS_NORMALS_TORCH_2_100_300_True                1198            1351            418
FACE_AREAS_NORMALS_TORCH_2_100_3000_False             48228           48869             11
FACE_AREAS_NORMALS_TORCH_2_100_3000_True               1186            1304            422
FACE_AREAS_NORMALS_TORCH_2_1000_300_False              5556            6097             90
FACE_AREAS_NORMALS_TORCH_2_1000_300_True               1200            1328            417
FACE_AREAS_NORMALS_TORCH_2_1000_3000_False            48683           50016             11
FACE_AREAS_NORMALS_TORCH_2_1000_3000_True              1185            1306            422
FACE_AREAS_NORMALS_TORCH_10_100_300_False             24215           25097             21
FACE_AREAS_NORMALS_TORCH_10_100_300_True               1150            1314            435
FACE_AREAS_NORMALS_TORCH_10_100_3000_False           232605          234952              3
FACE_AREAS_NORMALS_TORCH_10_100_3000_True              1193            1314            420
FACE_AREAS_NORMALS_TORCH_10_1000_300_False            24912           25343             21
FACE_AREAS_NORMALS_TORCH_10_1000_300_True              1216            1330            412
FACE_AREAS_NORMALS_TORCH_10_1000_3000_False          239907          241253              3
FACE_AREAS_NORMALS_TORCH_10_1000_3000_True             1226            1333            408
FACE_AREAS_NORMALS_TORCH_32_100_300_False             73991           75776              7
FACE_AREAS_NORMALS_TORCH_32_100_300_True               1193            1339            420
FACE_AREAS_NORMALS_TORCH_32_100_3000_False           728932          728932              1
FACE_AREAS_NORMALS_TORCH_32_100_3000_True              1186            1359            422
FACE_AREAS_NORMALS_TORCH_32_1000_300_False            76385           79129              7
FACE_AREAS_NORMALS_TORCH_32_1000_300_True              1165            1310            430
FACE_AREAS_NORMALS_TORCH_32_1000_3000_False          753276          753276              1
FACE_AREAS_NORMALS_TORCH_32_1000_3000_True             1205            1340            415
--------------------------------------------------------------------------------
```

Reviewed By: bottler, jcjohnson

Differential Revision: D19864385

fbshipit-source-id: 3a87ae41a8e3ab5560febcb94961798f2e09dfb8
2020-02-13 11:42:48 -08:00
facebook-github-bot
dbf06b504b Initial commit
fbshipit-source-id: ad58e416e3ceeca85fae0583308968d04e78fe0d
2020-01-23 11:53:46 -08:00