- 07 Apr, 2020 1 commit
-
-
Jeremy Reizenstein authored
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
-
- 06 Apr, 2020 2 commits
-
-
Jeremy Reizenstein authored
Summary: The conda build process generates some files of its own, which we don't want to catch in our test for copyright notices. Reviewed By: nikhilaravi, patricklabatut Differential Revision: D20868566 fbshipit-source-id: 76a786a3eb9a674d59e630cc06f346e8b82258a4
-
Jeremy Reizenstein authored
Summary: lint clean again Reviewed By: patricklabatut Differential Revision: D20868775 fbshipit-source-id: ade4301c1012c5c6943186432465215701d635a9
-
- 05 Apr, 2020 1 commit
-
-
David Novotny authored
Summary: Allows to initialize a Transform3D object with a batch of user-defined transformation matrices: ``` t = Transform3D(matrix=torch.randn(2, 4, 4)) ``` Reviewed By: nikhilaravi Differential Revision: D20693475 fbshipit-source-id: dccc49b2ca4c19a034844c63463953ba8f52c1bc
-
- 03 Apr, 2020 1 commit
-
-
Roman Shapovalov authored
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
-
- 02 Apr, 2020 1 commit
-
-
David Novotny authored
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
-
- 01 Apr, 2020 1 commit
-
-
Patrick Labatut authored
Summary: Similar to D20392526, PLY files without vertices or faces should be allowed: - a PLY with only vertices can represent a point cloud - a PLY without any vertex or face is just empty - a PLY with faces referencing inexistent vertices has invalid data Reviewed By: gkioxari Differential Revision: D20400330 fbshipit-source-id: 35a5f072603fd221f382c7faad5f37c3e0b49bb1
-
- 30 Mar, 2020 2 commits
-
-
Jeremy Reizenstein authored
Summary: rename join_meshes to join_meshes_as_batch. Reviewed By: nikhilaravi Differential Revision: D20671293 fbshipit-source-id: e84d6a67d6c1ec28fb5e52d4607db8e92561a4cd
-
Jeremy Reizenstein authored
Summary: Flowing of compositing comments Reviewed By: nikhilaravi Differential Revision: D20556707 fbshipit-source-id: 4abdc85e4f65abd41c4a890b6895bc5e95b4576b
-
- 29 Mar, 2020 2 commits
-
-
Patrick Labatut authored
Summary: Address black + isort fbsource linter warnings from D20558374 (previous diff) Reviewed By: nikhilaravi Differential Revision: D20558373 fbshipit-source-id: d3607de4a01fb24c0d5269634563a7914bddf1c8
-
Jeremy Reizenstein authored
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
-
- 28 Mar, 2020 1 commit
-
-
Patrick Labatut authored
Summary: OBJ files without vertices or faces should be allowed: - an OBJ with only vertices can represent a point cloud - an OBJ without any vertex or face is just empty - an OBJ with faces referencing inexistent vertices has invalid data Reviewed By: gkioxari Differential Revision: D20392526 fbshipit-source-id: e72c846ff1e5787fb11d527af3fefa261f9eb0ee
-
- 26 Mar, 2020 1 commit
-
-
Justin Johnson authored
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
-
- 23 Mar, 2020 1 commit
-
-
Jeremy Reizenstein authored
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
-
- 22 Mar, 2020 1 commit
-
-
Georgia Gkioxari authored
Summary: Run `/dev/linter.sh` to fix linting Reviewed By: nikhilaravi Differential Revision: D20584037 fbshipit-source-id: 69e45b33d22e3d54b6d37c3c35580bb3e9dc50a5
-
- 20 Mar, 2020 1 commit
-
-
Georgia Gkioxari authored
Summary: Replace view with reshape, add check for nans before mesh sampling Reviewed By: nikhilaravi Differential Revision: D20548456 fbshipit-source-id: c4e1b88e033ecb8f0f3a8f3a33a04ce13a5b5043
-
- 19 Mar, 2020 1 commit
-
-
Olivia authored
Summary: Code for accumulating points in the z-buffer in three ways: 1. weighted sum 2. normalised weighted sum 3. alpha compositing Pull Request resolved: https://github.com/fairinternal/pytorch3d/pull/4 Reviewed By: nikhilaravi Differential Revision: D20522422 Pulled By: gkioxari fbshipit-source-id: 5023baa05f15e338f3821ef08f5552c2dcbfc06c
-
- 18 Mar, 2020 2 commits
-
-
Nikhila Ravi authored
-
Dave Greenwood authored
Summary: Create extrinsic parameters from eye point. Create the rotation and translation from an eye point, look-at point and up vector. see: https://www.khronos.org/registry/OpenGL-Refpages/gl2.1/xhtml/gluLookAt.xml It is arguably easier to initialise a camera position as a point in the world rather than an angle. Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/65 Reviewed By: bottler Differential Revision: D20419652 Pulled By: nikhilaravi fbshipit-source-id: 9caa1330860bb8bde1fb5c3864ed4cde836a5d19
-
- 17 Mar, 2020 2 commits
-
-
Patrick Labatut authored
Summary: Enable spelling linter for Markdown, reStructuredText and IPython notebooks under `fbcode/vision/fair`. Apply suggested fixes. Reviewed By: ppwwyyxx Differential Revision: D20495298 fbshipit-source-id: 95310c7b51f9fa68ba2aa34ecc39a874da36a75c
-
Nikhila Ravi authored
Summary: Fix errors raised by issue on GitHub - extending mesh textures + rendering with Gourad and Phong shaders. https://github.com/facebookresearch/pytorch3d/issues/97 Reviewed By: gkioxari Differential Revision: D20319610 fbshipit-source-id: d1c692ff0b9397a77a9b829c5c731790de70c09f
-
- 16 Mar, 2020 1 commit
-
-
Jeremy Reizenstein authored
Summary: Ensure copyright header consistency and translation unit name uniqueness. Reviewed By: nikhilaravi Differential Revision: D20438802 fbshipit-source-id: 9820cfe4c6efab016a0a8589dfa24bb526692f83
-
- 15 Mar, 2020 1 commit
-
-
Nikhila Ravi authored
Summary: Added a padded to packed utils function which takes either split sizes or a padding value to remove padded elements from a tensor. Reviewed By: gkioxari Differential Revision: D20454238 fbshipit-source-id: 180b807ff44c74c4ee9d5c1ac3b5c4a9b4be57c7
-
- 13 Mar, 2020 1 commit
-
-
Patrick Labatut authored
Summary: Add more complex mesh I/O benchmarks: simple yet non-trivial procedural donut mesh Reviewed By: nikhilaravi Differential Revision: D20390726 fbshipit-source-id: b28b7e3a7f1720823c6bd24faabf688bb0127b7d
-
- 12 Mar, 2020 4 commits
-
-
Patrick Labatut authored
Summary: Use more realistic number of vertices / faces in benchmarks: in typical meshes, |F| ~ 2 |V| (follows from Euler formula + triangles as faces) Reviewed By: nikhilaravi Differential Revision: D20390722 fbshipit-source-id: d615e5810d6f4521391963b2573497c08a58db80
-
Patrick Labatut authored
Summary: Rename mesh I/O benchmarking methods: always (re-)create file-like object and directly return a lambda Reviewed By: nikhilaravi Differential Revision: D20390723 fbshipit-source-id: b45236360869cccdf3d5458a0aafb3ebe269babe
-
Patrick Labatut authored
Summary: Rename mesh I/O benchmarks and associated methods: - add `simple` qualifier (benchmark on more realistic mesh data to be added later) - align naming between OBJ and PLY - prefix with `bm_` to make the benchmarking purpose clear(er) Reviewed By: nikhilaravi Differential Revision: D20390764 fbshipit-source-id: 7714520abfcfe1125067f3c52f7ce19bca359574
-
Patrick Labatut authored
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
-
- 11 Mar, 2020 1 commit
-
-
Jeremy Reizenstein authored
Summary: Make Meshes.__getitem__ carry texture information to the new mesh. Reviewed By: gkioxari Differential Revision: D20283976 fbshipit-source-id: d9ee0580c11ac5b4384df9d8158a07e6eb8d00fe
-
- 06 Mar, 2020 1 commit
-
-
Nikhila Ravi authored
Summary: ## Updates - Defined the world and camera coordinates according to this figure. The world coordinates are defined as having +Y up, +X left and +Z in. {F230888499} - Removed all flipping from blending functions. - Updated the rasterizer to return images with +Y up and +X left. - Updated all the mesh rasterizer tests - The expected values are now defined in terms of the default +Y up, +X left - Added tests where the triangles in the meshes are non symmetrical so that it is clear which direction +X and +Y are ## Questions: - Should we have **scene settings** instead of raster settings? - To be more correct we should be [z clipping in the rasterizer based on the far/near clipping planes](https://github.com/ShichenLiu/SoftRas/blob/master/soft_renderer/cuda/soft_rasterize_cuda_kernel.cu#L400) - these values are also required in the blending functions so should we make these scene level parameters and have a scene settings tuple which is available to the rasterizer and shader? Reviewed By: gkioxari Differential Revision: D20208604 fbshipit-source-id: 55787301b1bffa0afa9618f0a0886cc681da51f3
-
- 04 Mar, 2020 1 commit
-
-
Georgia Gkioxari authored
Summary: Revisions to Poincloud data structure with added normals The biggest changes form the previous version include: a) If the user provides tensor inputs, we make no assumption about padding. Padding is only for internal use for us to convert from list to padded b) If features are not provided or if the poincloud is empty, all forms of features are None. This is so that we don't waste memory on holding dummy tensors. Reviewed By: nikhilaravi Differential Revision: D19791851 fbshipit-source-id: 7e182f7bb14395cb966531653f6dd6b328fd999c
-
- 01 Mar, 2020 1 commit
-
-
Nikhila Ravi authored
Summary: Changed `torch.cumprod` to `torch.prod` in blending functions and added more tests and benchmark tests. This should fix the issue raised on GitHub. Reviewed By: gkioxari Differential Revision: D20163073 fbshipit-source-id: 4569fd37be11aa4435a3ce8736b55622c00ec718
-
- 29 Feb, 2020 1 commit
-
-
Nikhila Ravi authored
Summary: Updates to the Renderer to enable barycentric clipping. This is important when there is blurring in the rasterization step. Also added support for flat shading. Reviewed By: jcjohnson Differential Revision: D19934259 fbshipit-source-id: 036e48636cd80d28a04405d7a29fcc71a2982904
-
- 24 Feb, 2020 1 commit
-
-
Dave Greenwood authored
Summary: BlendParams background_color is immutable , type hint as a sequence allows setting new values in constructor. Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/64 Reviewed By: bottler Differential Revision: D20068911 Pulled By: nikhilaravi fbshipit-source-id: c580a7654dca25629218513841aa16d9d1055588
-
- 21 Feb, 2020 1 commit
-
-
Jeremy Reizenstein authored
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
-
- 20 Feb, 2020 3 commits
-
-
Georgia Gkioxari authored
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
-
Patrick Labatut authored
Summary: Fix spelling of *Gouraud* in [Gouraud shading](https://en.wikipedia.org/wiki/Gouraud_shading). Reviewed By: nikhilaravi Differential Revision: D19943547 fbshipit-source-id: 5c016b7b051a7b33a7b68ed5303b642d9e834bbd
-
Nikhila Ravi authored
Summary: Renamed shaders to be prefixed with Hard/Soft depending on if they use a probabalistic blending (Soft) or use the closest face (Hard). There is some code duplication but I thought it would be cleaner to have separate shaders for each task rather than: - inheritance (which we discussed previously that we want to avoid) - boolean (hard/soft) or a string (hard/soft) - new blending functions other than the ones provided would need if statements in the current shaders which might get messy. Also added a `flat_shading` function and a `FlatShader` - I could make this into a tutorial as it was really easy to add a new shader and it might be a nice showcase. NOTE: There are a few more places where the naming will need to change (e.g the tutorials) but I wanted to reach a consensus on this before changing it everywhere. Reviewed By: jcjohnson Differential Revision: D19761036 fbshipit-source-id: f972f6530c7f66dc5550b0284c191abc4a7f6fc4
-
- 19 Feb, 2020 2 commits
-
-
Georgia Gkioxari authored
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
-
Nikhila Ravi authored
Summary: Fixed the rotation matrices generated by the RotateAxisAngle class and updated the tests. Added documentation for Transforms3d to clarify the conventions. Reviewed By: gkioxari Differential Revision: D19912903 fbshipit-source-id: c64926ce4e1381b145811557c32b73663d6d92d1
-
