Georgia Gkioxari
f2b229c1d1
pytorch3d compatibility
...
Summary: Making meshrcnn compatible with new PyTorch3D features/API changes.
Reviewed By: nikhilaravi
Differential Revision: D21149516
fbshipit-source-id: 1c7b8c1c1f5a2abe7d379fee10ded5d2db21515a
2020-04-20 23:04:24 -07:00
Jeremy Reizenstein
6207c359b1
spelling and flake
...
Summary: mostly recent lintish things
Reviewed By: nikhilaravi
Differential Revision: D21089003
fbshipit-source-id: 028733c1d875268f1879e4481da475b7100ba0b6
2020-04-17 10:50:22 -07:00
Jeremy Reizenstein
f25af96959
vert_align for Pointclouds object
...
Reviewed By: gkioxari
Differential Revision: D21088730
fbshipit-source-id: f8c125ac8c8009d45712ae63237ca64acf1faf45
2020-04-17 10:39:43 -07:00
Roman Shapovalov
04d8bf6a43
Efficient PnP.
...
Summary:
Efficient PnP algorithm to fit 2D to 3D correspondences under perspective assumption.
Benchmarked both variants of nullspace and pick one; SVD takes 7 times longer in the 100K points case.
Reviewed By: davnov134, gkioxari
Differential Revision: D20095754
fbshipit-source-id: 2b4519729630e6373820880272f674829eaed073
2020-04-17 07:44:16 -07:00
David Novotny
365945b1fd
Pointcloud normals estimation.
...
Summary: Estimates normals of a point cloud.
Reviewed By: gkioxari
Differential Revision: D20860182
fbshipit-source-id: 652ec2743fa645e02c01ffa37c2971bf27b89cef
2020-04-16 18:36:19 -07:00
David Novotny
8abbe22ffb
ICP - point-to-point version
...
Summary:
The iterative closest point algorithm - point-to-point version.
Output of `bm_iterative_closest_point`:
Argument key: `batch_size dim n_points_X n_points_Y use_pointclouds`
```
Benchmark Avg Time(μs) Peak Time(μs) Iterations
--------------------------------------------------------------------------------
IterativeClosestPoint_1_3_100_100_False 107569 111323 5
IterativeClosestPoint_1_3_100_1000_False 118972 122306 5
IterativeClosestPoint_1_3_1000_100_False 108576 110978 5
IterativeClosestPoint_1_3_1000_1000_False 331836 333515 2
IterativeClosestPoint_1_20_100_100_False 134387 137842 4
IterativeClosestPoint_1_20_100_1000_False 149218 153405 4
IterativeClosestPoint_1_20_1000_100_False 414248 416595 2
IterativeClosestPoint_1_20_1000_1000_False 374318 374662 2
IterativeClosestPoint_10_3_100_100_False 539852 539852 1
IterativeClosestPoint_10_3_100_1000_False 752784 752784 1
IterativeClosestPoint_10_3_1000_100_False 1070700 1070700 1
IterativeClosestPoint_10_3_1000_1000_False 1164020 1164020 1
IterativeClosestPoint_10_20_100_100_False 374548 377337 2
IterativeClosestPoint_10_20_100_1000_False 472764 476685 2
IterativeClosestPoint_10_20_1000_100_False 1457175 1457175 1
IterativeClosestPoint_10_20_1000_1000_False 2195820 2195820 1
IterativeClosestPoint_1_3_100_100_True 110084 115824 5
IterativeClosestPoint_1_3_100_1000_True 142728 147696 4
IterativeClosestPoint_1_3_1000_100_True 212966 213966 3
IterativeClosestPoint_1_3_1000_1000_True 369130 375114 2
IterativeClosestPoint_10_3_100_100_True 354615 355179 2
IterativeClosestPoint_10_3_100_1000_True 451815 452704 2
IterativeClosestPoint_10_3_1000_100_True 511833 511833 1
IterativeClosestPoint_10_3_1000_1000_True 798453 798453 1
--------------------------------------------------------------------------------
```
Reviewed By: shapovalov, gkioxari
Differential Revision: D19909952
fbshipit-source-id: f77fadc88fb7c53999909d594114b182ee2a3def
2020-04-16 14:02:16 -07:00
Nikhila Ravi
b530b0af32
lint fixes
...
Summary: Resolved trailing whitespace warnings.
Reviewed By: gkioxari
Differential Revision: D21023982
fbshipit-source-id: 14ea2ca372c13cfa987acc260264ca99ce44c461
2020-04-15 21:58:59 -07:00
Nikhila Ravi
3794f6753f
remove nearest_neighbors
...
Summary: knn is more general and faster than the nearest_neighbor code, so remove the latter.
Reviewed By: gkioxari
Differential Revision: D20816424
fbshipit-source-id: 75d6c44d17180752d0c9859814bbdf7892558158
2020-04-15 20:51:41 -07:00
Georgia Gkioxari
b2b0c5a442
knn autograd
...
Summary:
Adds knn backward to return `grad_pts1` and `grad_pts2`. Adds `knn_gather` to return the nearest neighbors in pts2.
The BM tests include backward pass and are ran on an M40.
```
Benchmark Avg Time(μs) Peak Time(μs) Iterations
--------------------------------------------------------------------------------
KNN_SQUARE_32_256_128_3_24_cpu 39558 43485 13
KNN_SQUARE_32_256_128_3_24_cuda:0 1080 1404 463
KNN_SQUARE_32_256_512_3_24_cpu 81950 85781 7
KNN_SQUARE_32_256_512_3_24_cuda:0 1519 1641 330
--------------------------------------------------------------------------------
Benchmark Avg Time(μs) Peak Time(μs) Iterations
--------------------------------------------------------------------------------
KNN_RAGGED_32_256_128_3_24_cpu 13798 14650 37
KNN_RAGGED_32_256_128_3_24_cuda:0 1576 1713 318
KNN_RAGGED_32_256_512_3_24_cpu 31255 32210 16
KNN_RAGGED_32_256_512_3_24_cuda:0 2024 2162 248
--------------------------------------------------------------------------------
```
Reviewed By: jcjohnson
Differential Revision: D20945556
fbshipit-source-id: a16f616029c6b5f8c2afceb5f2bc12c5c20d2f3c
2020-04-14 17:22:56 -07:00
Jeremy Reizenstein
01b5f7b228
heterogenous KNN
...
Summary: Interface and working implementation of ragged KNN. Benchmarks (which aren't ragged) haven't slowed. New benchmark shows that ragged is faster than non-ragged of the same shape.
Reviewed By: jcjohnson
Differential Revision: D20696507
fbshipit-source-id: 21b80f71343a3475c8d3ee0ce2680f92f0fae4de
2020-04-07 01:47:37 -07:00
Jeremy Reizenstein
b87058c62a
fix recent lint
...
Summary: lint clean again
Reviewed By: patricklabatut
Differential Revision: D20868775
fbshipit-source-id: ade4301c1012c5c6943186432465215701d635a9
2020-04-06 06:41:00 -07:00
Roman Shapovalov
e37085d999
Weighted Umeyama.
...
Summary:
1. Introduced weights to Umeyama implementation. This will be needed for weighted ePnP but is useful on its own.
2. Refactored to use the same code for the Pointclouds mask and passed weights.
3. Added test cases with random weights.
4. Fixed a bug in tests that calls the function with 0 points (fails randomly in Pytorch 1.3, will be fixed in the next release: https://github.com/pytorch/pytorch/issues/31421 ).
Reviewed By: gkioxari
Differential Revision: D20070293
fbshipit-source-id: e9f549507ef6dcaa0688a0f17342e6d7a9a4336c
2020-04-03 02:59:11 -07:00
David Novotny
e5b1d6d3a3
Umeyama
...
Summary:
Umeyama estimates a rigid motion between two sets of corresponding points.
Benchmark output for `bm_points_alignment`
```
Arguments key: [<allow_reflection>_<batch_size>_<dim>_<estimate_scale>_<n_points>_<use_pointclouds>]
Benchmark Avg Time(μs) Peak Time(μs) Iterations
--------------------------------------------------------------------------------
CorrespodingPointsAlignment_True_1_3_True_100_False 7382 9833 68
CorrespodingPointsAlignment_True_1_3_True_10000_False 8183 10500 62
CorrespodingPointsAlignment_True_1_3_False_100_False 7301 9263 69
CorrespodingPointsAlignment_True_1_3_False_10000_False 7945 9746 64
CorrespodingPointsAlignment_True_1_20_True_100_False 13706 41623 37
CorrespodingPointsAlignment_True_1_20_True_10000_False 11044 33766 46
CorrespodingPointsAlignment_True_1_20_False_100_False 9908 28791 51
CorrespodingPointsAlignment_True_1_20_False_10000_False 9523 18680 53
CorrespodingPointsAlignment_True_10_3_True_100_False 29585 32026 17
CorrespodingPointsAlignment_True_10_3_True_10000_False 29626 36324 18
CorrespodingPointsAlignment_True_10_3_False_100_False 26013 29253 20
CorrespodingPointsAlignment_True_10_3_False_10000_False 25000 33820 20
CorrespodingPointsAlignment_True_10_20_True_100_False 40955 41592 13
CorrespodingPointsAlignment_True_10_20_True_10000_False 42087 42393 12
CorrespodingPointsAlignment_True_10_20_False_100_False 39863 40381 13
CorrespodingPointsAlignment_True_10_20_False_10000_False 40813 41699 13
CorrespodingPointsAlignment_True_100_3_True_100_False 183146 194745 3
CorrespodingPointsAlignment_True_100_3_True_10000_False 213789 231466 3
CorrespodingPointsAlignment_True_100_3_False_100_False 177805 180796 3
CorrespodingPointsAlignment_True_100_3_False_10000_False 184963 185695 3
CorrespodingPointsAlignment_True_100_20_True_100_False 347181 347325 2
CorrespodingPointsAlignment_True_100_20_True_10000_False 363259 363613 2
CorrespodingPointsAlignment_True_100_20_False_100_False 351769 352496 2
CorrespodingPointsAlignment_True_100_20_False_10000_False 375629 379818 2
CorrespodingPointsAlignment_False_1_3_True_100_False 11155 13770 45
CorrespodingPointsAlignment_False_1_3_True_10000_False 10743 13938 47
CorrespodingPointsAlignment_False_1_3_False_100_False 9578 11511 53
CorrespodingPointsAlignment_False_1_3_False_10000_False 9549 11984 53
CorrespodingPointsAlignment_False_1_20_True_100_False 13809 14183 37
CorrespodingPointsAlignment_False_1_20_True_10000_False 14084 15082 36
CorrespodingPointsAlignment_False_1_20_False_100_False 12765 14177 40
CorrespodingPointsAlignment_False_1_20_False_10000_False 12811 13096 40
CorrespodingPointsAlignment_False_10_3_True_100_False 28823 39384 18
CorrespodingPointsAlignment_False_10_3_True_10000_False 27135 27525 19
CorrespodingPointsAlignment_False_10_3_False_100_False 26236 28980 20
CorrespodingPointsAlignment_False_10_3_False_10000_False 42324 45123 12
CorrespodingPointsAlignment_False_10_20_True_100_False 723902 723902 1
CorrespodingPointsAlignment_False_10_20_True_10000_False 220007 252886 3
CorrespodingPointsAlignment_False_10_20_False_100_False 55593 71636 9
CorrespodingPointsAlignment_False_10_20_False_10000_False 44419 71861 12
CorrespodingPointsAlignment_False_100_3_True_100_False 184768 185199 3
CorrespodingPointsAlignment_False_100_3_True_10000_False 198657 213868 3
CorrespodingPointsAlignment_False_100_3_False_100_False 224598 309645 3
CorrespodingPointsAlignment_False_100_3_False_10000_False 197863 202002 3
CorrespodingPointsAlignment_False_100_20_True_100_False 293484 309459 2
CorrespodingPointsAlignment_False_100_20_True_10000_False 327253 366644 2
CorrespodingPointsAlignment_False_100_20_False_100_False 420793 422194 2
CorrespodingPointsAlignment_False_100_20_False_10000_False 462634 485542 2
CorrespodingPointsAlignment_True_1_3_True_100_True 7664 9909 66
CorrespodingPointsAlignment_True_1_3_True_10000_True 7190 8366 70
CorrespodingPointsAlignment_True_1_3_False_100_True 6549 8316 77
CorrespodingPointsAlignment_True_1_3_False_10000_True 6534 7710 77
CorrespodingPointsAlignment_True_10_3_True_100_True 29052 32940 18
CorrespodingPointsAlignment_True_10_3_True_10000_True 30526 33453 17
CorrespodingPointsAlignment_True_10_3_False_100_True 28708 32993 18
CorrespodingPointsAlignment_True_10_3_False_10000_True 30630 35973 17
CorrespodingPointsAlignment_True_100_3_True_100_True 264909 320820 3
CorrespodingPointsAlignment_True_100_3_True_10000_True 310902 322604 2
CorrespodingPointsAlignment_True_100_3_False_100_True 246832 250634 3
CorrespodingPointsAlignment_True_100_3_False_10000_True 276006 289061 2
CorrespodingPointsAlignment_False_1_3_True_100_True 11421 13757 44
CorrespodingPointsAlignment_False_1_3_True_10000_True 11199 12532 45
CorrespodingPointsAlignment_False_1_3_False_100_True 11474 15841 44
CorrespodingPointsAlignment_False_1_3_False_10000_True 10384 13188 49
CorrespodingPointsAlignment_False_10_3_True_100_True 36599 47340 14
CorrespodingPointsAlignment_False_10_3_True_10000_True 40702 50754 13
CorrespodingPointsAlignment_False_10_3_False_100_True 41277 52149 13
CorrespodingPointsAlignment_False_10_3_False_10000_True 34286 37091 15
CorrespodingPointsAlignment_False_100_3_True_100_True 254991 258578 2
CorrespodingPointsAlignment_False_100_3_True_10000_True 257999 261285 2
CorrespodingPointsAlignment_False_100_3_False_100_True 247511 248693 3
CorrespodingPointsAlignment_False_100_3_False_10000_True 251807 263865 3
```
Reviewed By: gkioxari
Differential Revision: D19808389
fbshipit-source-id: 83305a58627d2fc5dcaf3c3015132d8148f28c29
2020-04-02 14:46:51 -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
37c5c8e0b6
Linter, deprecated type()
...
Summary: Run linter after recent changes. Fix long comment in knn.h which clang-format has reflowed badly. Add crude test that code doesn't call deprecated `.type()` or `.data()`.
Reviewed By: nikhilaravi
Differential Revision: D20692935
fbshipit-source-id: 28ce0308adae79a870cb41a810b7cf8744f41ab8
2020-03-29 14:02:58 -07:00
Justin Johnson
870290df34
Implement K-Nearest Neighbors
...
Summary:
Implements K-Nearest Neighbors with C++ and CUDA versions.
KNN in CUDA is highly nontrivial. I've implemented a few different versions of the kernel, and we heuristically dispatch to different kernels based on the problem size. Some of the kernels rely on template specialization on either D or K, so we use template metaprogramming to compile specialized versions for ranges of D and K.
These kernels are up to 3x faster than our existing 1-nearest-neighbor kernels, so we should also consider swapping out `nn_points_idx` to use these kernels in the backend.
I've been working mostly on the CUDA kernels, and haven't converged on the correct Python API.
I still want to benchmark against FAISS to see how far away we are from their performance.
Reviewed By: bottler
Differential Revision: D19729286
fbshipit-source-id: 608ffbb7030c21fe4008f330522f4890f0c3c21a
2020-03-26 13:40:26 -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
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
Jeremy Reizenstein
e491efb81f
lint things
...
Summary:
Lint related fixes: Improve internal/OSS consistency. Fix the fight between black and certain pyre-ignore markers by moving them to the line before.
Use clang-format-8 automatically if present. Small number of pyre fixes.
arc doesn't run pyre at the moment, so I put back the explicit call to pyre. I don't know if there's an option somewhere to change this.
Reviewed By: nikhilaravi
Differential Revision: D19780518
fbshipit-source-id: ef1c243392322fa074130f6cff2dd8a6f7738a7f
2020-02-21 05:05:06 -08:00
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
facebook-github-bot
dbf06b504b
Initial commit
...
fbshipit-source-id: ad58e416e3ceeca85fae0583308968d04e78fe0d
2020-01-23 11:53:46 -08:00
