- 05 May, 2020 2 commits
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Georgia Gkioxari authored
Summary: Add alignment modes for cubify operation. Reviewed By: nikhilaravi Differential Revision: D21393199 fbshipit-source-id: 7022044e591229a6ed5efc361fd3215e65f43f86
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Jeremy Reizenstein authored
Summary: This has been failing intermittently Reviewed By: nikhilaravi Differential Revision: D21403157 fbshipit-source-id: 51b74d6c813b52effe72d14b565e250fcabbb463
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- 04 May, 2020 2 commits
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Jeremy Reizenstein authored
Summary: Fix documentation of KNN, issue #180 Reviewed By: gkioxari Differential Revision: D21384761 fbshipit-source-id: 2b36ee496f2060d17827d2fd66c490cdfa766866
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Nikhila Ravi authored
Summary: Ran the linter. TODO: need to update the linter as per D21353065. Reviewed By: bottler Differential Revision: D21362270 fbshipit-source-id: ad0e781de0a29f565ad25c43bc94a19b1828c020
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- 01 May, 2020 1 commit
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Jeremy Reizenstein authored
Summary: Use nn.functional.interpolate instead of a TorchVision transform to resize texture maps to a common value. This works on all devices. This fixes issue #175. Also fix the condition so it only happens when needed. Reviewed By: nikhilaravi Differential Revision: D21324510 fbshipit-source-id: c50eb06514984995bd81f2c44079be6e0b4098e4
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- 30 Apr, 2020 1 commit
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Georgia Gkioxari authored
Summary: Fix self assignment of normals when estimating normals Reviewed By: nikhilaravi Differential Revision: D21315980 fbshipit-source-id: 2aa5864c3f066e39e07343f192cc6423ce1ae771
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- 26 Apr, 2020 1 commit
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Jeremy Reizenstein authored
Summary: Update version number for version 0.2.0. Reviewed By: nikhilaravi Differential Revision: D21157358 fbshipit-source-id: 32a5b93e5dc65a31a806a5ce7231f8603fe02e85
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- 25 Apr, 2020 2 commits
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Jeremy Reizenstein authored
Summary: Bump the nvidia driver used in the conda tests. Add an environment variable (unused) to allow building without ninja. Print relative error on assertClose failure. Reviewed By: nikhilaravi Differential Revision: D21227373 fbshipit-source-id: 5dd8eb097151da27d3632daa755a1e7b9ac97845
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Nikhila Ravi authored
Summary: Some updates to the issue templates, readme and install.md Creating an FAQ for installation similar to: https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md#common-installation-issues Reviewed By: gkioxari Differential Revision: D21117899 fbshipit-source-id: d287c3a7a99c2e425b4e0cffca55a7b225d79e11
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- 24 Apr, 2020 4 commits
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Nikhila Ravi authored
Summary: Cuda test failing on circle with the error `random_ expects 'from' to be less than 'to', but got from=0 >= to=0` This is because the `high` value in `torch.randint` is 1 more than the highest value in the distribution from which a value is drawn. So if there is only 1 cuda device available then the low and high are 0. Reviewed By: gkioxari Differential Revision: D21236669 fbshipit-source-id: 46c312d431c474f1f2c50747b1d5e7afbd7df3a9
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Michele Sanna authored
Summary: Signed-off-by:
Michele Sanna <sanna@arrival.com> fixes the bin_size calculation with a formula for any image_size > 64. Matches the values chosen so far. simple test: ``` import numpy as np import matplotlib.pyplot as plt image_size = np.arange(64, 2048) bin_size = np.where(image_size <= 64, 8, (2 ** np.maximum(np.ceil(np.log2(image_size)) - 4, 4)).astype(int)) print(image_size) print(bin_size) for ims, bins in zip(image_size, bin_size): if ims <= 64: assert bins == 8 elif ims <= 256: assert bins == 16 elif ims <= 512: assert bins == 32 elif ims <= 1024: assert bins == 64 elif ims <= 2048: assert bins == 128 assert (ims + bins - 1) // bins < 22 plt.plot(image_size, bin_size) plt.grid() plt.show() ```  Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/90 Reviewed By: jcjohnson Differential Revision: D21160372 Pulled By: nikhilaravi fbshipit-source-id: 660cf5832f4ca5be243c435a6bed969596fc0188
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Nikhila Ravi authored
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
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Nikhila Ravi authored
Summary: Updated the load obj function to support creating of a per face texture map using the information in an .mtl file. Uses the approach from in SoftRasterizer. Currently I have ported in the SoftRasterizer code but this is only to help with comparison and will be deleted before landing. The ShapeNet Test data will also be deleted. Here is the [Design doc](https://docs.google.com/document/d/1AUcLP4QwVSqlfLAUfbjM9ic5vYn9P54Ha8QbcVXW2eI/edit?usp=sharing). ## Added - texture atlas creation functions in PyTorch based on the SoftRas cuda implementation - tests to compare SoftRas vs PyTorch3D implementation to verify it matches (using real shapenet data with meshes consisting of multiple textures) - benchmarks tests ## Remaining todo: - add more tests for obj io to test the new functions and the two texturing options - replace the shapenet data with the output from SoftRas saved as a file. # MAIN FILES TO REVIEW - `obj_io.py` - `test_obj_io.py` [still some tests to be added but have comparisons with SoftRas for now] The reference SoftRas implementations are in `softras_load_obj.py` and `load_textures.cu`. Reviewed By: gkioxari Differential Revision: D20754859 fbshipit-source-id: 42ace9dfb73f26e29d800c763f56d5b66c60c5e2
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- 23 Apr, 2020 2 commits
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Jeremy Reizenstein authored
Summary: Use aten instead of torch interface in all cuda code. This allows the cuda build to work with pytorch 1.5 with GCC 5 (e.g. the compiler of ubuntu 16.04LTS). This wasn't working. It has been failing with errors like the below, perhaps due to a bug in nvcc. ``` torch/include/torch/csrc/api/include/torch/nn/cloneable.h:68:61: error: invalid static_cast from type ‘const torch::OrderedDict<std::basic_string<char>, std::shared_ptr<torch::nn::Module> >’ to type ‘torch::OrderedDict<std::basic_string<char>, std::shared_ptr<torch::nn::Module> > ``` Reviewed By: nikhilaravi Differential Revision: D21204029 fbshipit-source-id: ca6bdbcecf42493365e1c23a33fe35e1759fe8b6
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Roman Shapovalov authored
Summary: davnov134 found that the algorithm crashes if X is an axis-aligned plane. This is because I implemented scaling control points by `X.std()` as a poor man’s version of PCA whitening. I checked that it does not bring consistent improvements, so let’s get rid of it. The algorithm still results in slightly higher errors on the axis aligned planes but at least it does not crash. As a next step, I will experiment with detecting a planar case and using 3-point barycentric coordinates rather than 4-points. Reviewed By: davnov134 Differential Revision: D21179968 fbshipit-source-id: 1f002fce5541934486b51808be0e910324977222
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- 22 Apr, 2020 5 commits
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Justin Johnson authored
Summary: We have multiple KNN CUDA implementations. From python, users can currently request a particular implementation via the `version` flag, but they have no way of knowing which implementations can be used for a given problem. This diff exposes a function `pytorch3d._C.knn_check_version(version, D, K)` that returns whether a particular version can be used. Reviewed By: nikhilaravi Differential Revision: D21162573 fbshipit-source-id: 6061960bdcecba454fd920b00036f4e9ff3fdbc0
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Nikhila Ravi authored
Reviewed By: gkioxari Differential Revision: D21180328 fbshipit-source-id: 218919c614c1ea54b5147871bd91960b8346524b
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Jeremy Reizenstein authored
Summary: Modify test_chamfer for more robustness. Avoid empty pointclouds, including where point_reduction is mean, for which we currently return nan (*), and so that we aren't looking at an empty gradient. Make sure we aren't using padding as points in the homogenous cases in the tests, which will lead to a tie between closest points and therefore a potential instability in the gradient - see https://github.com/pytorch/pytorch/issues/35699. (*) This doesn't attempt to fix the nan. Reviewed By: nikhilaravi, gkioxari Differential Revision: D21157322 fbshipit-source-id: a609e84e25a24379c8928ff645d587552526e4af
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Jeremy Reizenstein authored
Summary: cuda 10.2 location on linux. Also remove unused conda test dependencies. Reviewed By: nikhilaravi Differential Revision: D21176409 fbshipit-source-id: dd3f339a92233ff16877ba76506ddf8f4418715d
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Nikhila Ravi authored
Summary: Added backface culling as an option to the `raster_settings`. This is needed for the full forward rendering of shapenet meshes with texture (some meshes contain multiple overlapping segments which have different textures). For a triangle (v0, v1, v2) define the vectors A = (v1 - v0) and B = (v2 − v0) and use this to calculate the area of the triangle as: ``` area = 0.5 * A x B area = 0.5 * ((x1 − x0)(y2 − y0) − (x2 − x0)(y1 − y0)) ``` The area will be positive if (v0, v1, v2) are oriented counterclockwise (a front face), and negative if (v0, v1, v2) are oriented clockwise (a back face). We can reuse the `edge_function` as it already calculates the triangle area. Reviewed By: jcjohnson Differential Revision: D20960115 fbshipit-source-id: 2d8a4b9ccfb653df18e79aed8d05c7ec0f057ab1
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- 21 Apr, 2020 3 commits
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charleschiu2012 authored
Summary: fix Args' definition at line 1016, 1018, 1020 in function pytorch3d.renderer.cameras.look_at_view_transform. Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/120 Reviewed By: bottler Differential Revision: D20597565 Pulled By: nikhilaravi fbshipit-source-id: e10a221e3dccc0adf20b26808ad67328408a4388
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Jeremy Reizenstein authored
Summary: Add conda packages for pytorch 1.5. Make wheels be only pytorch 1.5. Note that pytorch 1.4 has conda packages for cuda 9.2, 10.0 and 10.1, whilst pytorch 1.5 has packages for cuda 9.2, 10.1 and 10.2. We mirror these choices. Reviewed By: nikhilaravi Differential Revision: D21157392 fbshipit-source-id: 2f7311e6a83774a6d6c8afb8110b8bd9f37f1454
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Georgia Gkioxari authored
Summary: Making meshrcnn compatible with new PyTorch3D features/API changes. Reviewed By: nikhilaravi Differential Revision: D21149516 fbshipit-source-id: 1c7b8c1c1f5a2abe7d379fee10ded5d2db21515a
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- 20 Apr, 2020 2 commits
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Nikhila Ravi authored
Summary: Fix a bug which resulted in a rendering artifacts if the image size was not a multiple of 16. Fix: Revert coarse rasterization to original implementation and only update fine rasterization to reverse the ordering of Y and X axis. This is much simpler than the previous approach! Additional changes: - updated mesh rendering end-end tests to check outputs from both naive and coarse to fine rasterization. - added pointcloud rendering end-end tests Reviewed By: gkioxari Differential Revision: D21102725 fbshipit-source-id: 2e7e1b013dd6dd12b3a00b79eb8167deddb2e89a
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Jeremy Reizenstein authored
Summary: None of the current test_build tests make sense during `conda build`. Also remove the unnecessary dependency on the `six` library. Reviewed By: nikhilaravi Differential Revision: D20893852 fbshipit-source-id: 685f0446eaa0bd9151eeee89fc630a1ddc0252ff
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- 17 Apr, 2020 7 commits
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Jeremy Reizenstein authored
Summary: mostly recent lintish things Reviewed By: nikhilaravi Differential Revision: D21089003 fbshipit-source-id: 028733c1d875268f1879e4481da475b7100ba0b6
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Jeremy Reizenstein authored
Summary: This is mostly replacing the old PackedTensorAccessor with the new PackedTensorAccessor64. Reviewed By: gkioxari Differential Revision: D21088773 fbshipit-source-id: 5973e5a29d934eafb7c70ec5ec154ca076b64d27
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Jeremy Reizenstein authored
Reviewed By: gkioxari Differential Revision: D21088730 fbshipit-source-id: f8c125ac8c8009d45712ae63237ca64acf1faf45
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Jeremy Reizenstein authored
Summary: A couple of files for the removed nearest_neighbor functionality are left behind. Reviewed By: nikhilaravi Differential Revision: D21088624 fbshipit-source-id: 4bb29016b4e5f63102765b384c363733b60032fa
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Roman Shapovalov authored
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
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David Novotny authored
Summary: Made a CameraBase class. Added `unproject_points` method for each camera class. Reviewed By: nikhilaravi Differential Revision: D20373602 fbshipit-source-id: 7e3da5ae420091b5fcab400a9884ef29ad7a7343
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David Novotny authored
Summary: Estimates normals of a point cloud. Reviewed By: gkioxari Differential Revision: D20860182 fbshipit-source-id: 652ec2743fa645e02c01ffa37c2971bf27b89cef
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- 16 Apr, 2020 4 commits
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David Novotny authored
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
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Nikhila Ravi authored
Summary: It seemed that even though the chamfer diff was rebased on top of the knn autograd diff, some of the final updates did not get applied. I'm really surprised that the sandcastle tests did not fail and prevent the diff from landing. Reviewed By: gkioxari Differential Revision: D21066156 fbshipit-source-id: 5216efe95180c1b6082d0bac404fa1920cfb7b02
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Nikhila Ravi authored
Summary: Resolved trailing whitespace warnings. Reviewed By: gkioxari Differential Revision: D21023982 fbshipit-source-id: 14ea2ca372c13cfa987acc260264ca99ce44c461
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Nikhila Ravi authored
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
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- 15 Apr, 2020 3 commits
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Nikhila Ravi authored
Summary: Allow Pointclouds objects and heterogenous data to be provided for Chamfer loss. Remove "none" as an option for point_reduction because it doesn't make sense and in the current implementation is effectively the same as "sum". Possible improvement: create specialised operations for sum and cosine_similarity of padded tensors, to avoid having to create masks. sum would be useful elsewhere. Reviewed By: gkioxari Differential Revision: D20816301 fbshipit-source-id: 0f32073210225d157c029d80de450eecdb64f4d2
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Siddhant Ranade authored
Summary: Fixed a bug in creating the projection matrix for the SfMPerspectiveCameras class. Also fixed the corresponding test in test_cameras.py. The p0x, p0y are 2D coordinates for the principal point in the NDCS, and therefore should be added AFTER the perspective z divide. I.e. we expect <a href="https://www.codecogs.com/eqnedit.php?latex=\begin{bmatrix}&space;x\&space;y\&space;z&space;\end{bmatrix}_{\text{NDCS}}&space;=&space;\begin{bmatrix}&space;f_xX/Z&space;+&space;p_x\&space;f_yY/Z&space;+&space;p_y\&space;1&space;/&space;Z\&space;\end{bmatrix}&space;=&space;\text{normalize}\left(&space;\begin{bmatrix}&space;f_xX&space;+&space;p_xZ\&space;f_yY&space;+&space;p_yZ\&space;1\&space;Z&space;\end{bmatrix}&space;\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\begin{bmatrix}&space;x\&space;y\&space;z&space;\end{bmatrix}_{\text{NDCS}}&space;=&space;\begin{bmatrix}&space;f_xX/Z&space;+&space;p_x\&space;f_yY/Z&space;+&space;p_y\&space;1&space;/&space;Z\&space;\end{bmatrix}&space;=&space;\text{normalize}\left(&space;\begin{bmatrix}&space;f_xX&space;+&space;p_xZ\&space;f_yY&space;+&space;p_yZ\&space;1\&space;Z&space;\end{bmatrix}&space;\right)" title="\begin{bmatrix} x\ y\ z \end{bmatrix}_{\text{NDCS}} = \begin{bmatrix} f_xX/Z + p_x\ f_yY/Z + p_y\ 1 / Z\ \end{bmatrix} = \text{normalize}\left( \begin{bmatrix} f_xX + p_xZ\ f_yY + p_yZ\ 1\ Z \end{bmatrix} \right)" /></a> The current behavior is <a href="https://www.codecogs.com/eqnedit.php?latex=\begin{bmatrix}&space;x\&space;y\&space;z&space;\end{bmatrix}_{\text{NDCS}}&space;=&space;\begin{bmatrix}&space;f_xX/Z&space;+&space;p_x/Z\&space;f_yY/Z&space;+&space;p_y/Z\&space;1&space;/&space;Z\&space;\end{bmatrix}&space;=&space;\text{normalize}\left(&space;\begin{bmatrix}&space;f_xX&space;+&space;p_x\&space;f_yY&space;+&space;p_y\&space;1\&space;Z&space;\end{bmatrix}&space;\right)" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\begin{bmatrix}&space;x\&space;y\&space;z&space;\end{bmatrix}_{\text{NDCS}}&space;=&space;\begin{bmatrix}&space;f_xX/Z&space;+&space;p_x/Z\&space;f_yY/Z&space;+&space;p_y/Z\&space;1&space;/&space;Z\&space;\end{bmatrix}&space;=&space;\text{normalize}\left(&space;\begin{bmatrix}&space;f_xX&space;+&space;p_x\&space;f_yY&space;+&space;p_y\&space;1\&space;Z&space;\end{bmatrix}&space;\right)" title="\begin{bmatrix} x\ y\ z \end{bmatrix}_{\text{NDCS}} = \begin{bmatrix} f_xX/Z + p_x/Z\ f_yY/Z + p_y/Z\ 1 / Z\ \end{bmatrix} = \text{normalize}\left( \begin{bmatrix} f_xX + p_x\ f_yY + p_y\ 1\ Z \end{bmatrix} \right)" /></a> which is incorrect. Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/148 Reviewed By: gkioxari Differential Revision: D21039003 Pulled By: davnov134 fbshipit-source-id: 3e19ac22adbcc39b731ae14052a72fd4ddda2af5
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Georgia Gkioxari authored
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
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- 11 Apr, 2020 1 commit
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Georgia Gkioxari authored
Summary: Implementation of point to mesh distances. The current diff contains two types: (a) Point to Edge (b) Point to Face ``` Benchmark Avg Time(μs) Peak Time(μs) Iterations -------------------------------------------------------------------------------- POINT_MESH_EDGE_4_100_300_5000_cuda:0 2745 3138 183 POINT_MESH_EDGE_4_100_300_10000_cuda:0 4408 4499 114 POINT_MESH_EDGE_4_100_3000_5000_cuda:0 4978 5070 101 POINT_MESH_EDGE_4_100_3000_10000_cuda:0 9076 9187 56 POINT_MESH_EDGE_4_1000_300_5000_cuda:0 1411 1487 355 POINT_MESH_EDGE_4_1000_300_10000_cuda:0 4829 5030 104 POINT_MESH_EDGE_4_1000_3000_5000_cuda:0 7539 7620 67 POINT_MESH_EDGE_4_1000_3000_10000_cuda:0 12088 12272 42 POINT_MESH_EDGE_8_100_300_5000_cuda:0 3106 3222 161 POINT_MESH_EDGE_8_100_300_10000_cuda:0 8561 8648 59 POINT_MESH_EDGE_8_100_3000_5000_cuda:0 6932 7021 73 POINT_MESH_EDGE_8_100_3000_10000_cuda:0 24032 24176 21 POINT_MESH_EDGE_8_1000_300_5000_cuda:0 5272 5399 95 POINT_MESH_EDGE_8_1000_300_10000_cuda:0 11348 11430 45 POINT_MESH_EDGE_8_1000_3000_5000_cuda:0 17478 17683 29 POINT_MESH_EDGE_8_1000_3000_10000_cuda:0 25961 26236 20 POINT_MESH_EDGE_16_100_300_5000_cuda:0 8244 8323 61 POINT_MESH_EDGE_16_100_300_10000_cuda:0 18018 18071 28 POINT_MESH_EDGE_16_100_3000_5000_cuda:0 19428 19544 26 POINT_MESH_EDGE_16_100_3000_10000_cuda:0 44967 45135 12 POINT_MESH_EDGE_16_1000_300_5000_cuda:0 7825 7937 64 POINT_MESH_EDGE_16_1000_300_10000_cuda:0 18504 18571 28 POINT_MESH_EDGE_16_1000_3000_5000_cuda:0 65805 66132 8 POINT_MESH_EDGE_16_1000_3000_10000_cuda:0 90885 91089 6 -------------------------------------------------------------------------------- Benchmark Avg Time(μs) Peak Time(μs) Iterations -------------------------------------------------------------------------------- POINT_MESH_FACE_4_100_300_5000_cuda:0 1561 1685 321 POINT_MESH_FACE_4_100_300_10000_cuda:0 2818 2954 178 POINT_MESH_FACE_4_100_3000_5000_cuda:0 15893 16018 32 POINT_MESH_FACE_4_100_3000_10000_cuda:0 16350 16439 31 POINT_MESH_FACE_4_1000_300_5000_cuda:0 3179 3278 158 POINT_MESH_FACE_4_1000_300_10000_cuda:0 2353 2436 213 POINT_MESH_FACE_4_1000_3000_5000_cuda:0 16262 16336 31 POINT_MESH_FACE_4_1000_3000_10000_cuda:0 9334 9448 54 POINT_MESH_FACE_8_100_300_5000_cuda:0 4377 4493 115 POINT_MESH_FACE_8_100_300_10000_cuda:0 9728 9822 52 POINT_MESH_FACE_8_100_3000_5000_cuda:0 26428 26544 19 POINT_MESH_FACE_8_100_3000_10000_cuda:0 42238 43031 12 POINT_MESH_FACE_8_1000_300_5000_cuda:0 3891 3982 129 POINT_MESH_FACE_8_1000_300_10000_cuda:0 5363 5429 94 POINT_MESH_FACE_8_1000_3000_5000_cuda:0 20998 21084 24 POINT_MESH_FACE_8_1000_3000_10000_cuda:0 39711 39897 13 POINT_MESH_FACE_16_100_300_5000_cuda:0 5955 6001 84 POINT_MESH_FACE_16_100_300_10000_cuda:0 12082 12144 42 POINT_MESH_FACE_16_100_3000_5000_cuda:0 44996 45176 12 POINT_MESH_FACE_16_100_3000_10000_cuda:0 73042 73197 7 POINT_MESH_FACE_16_1000_300_5000_cuda:0 8292 8374 61 POINT_MESH_FACE_16_1000_300_10000_cuda:0 19442 19506 26 POINT_MESH_FACE_16_1000_3000_5000_cuda:0 36059 36194 14 POINT_MESH_FACE_16_1000_3000_10000_cuda:0 64644 64822 8 -------------------------------------------------------------------------------- ``` Reviewed By: jcjohnson Differential Revision: D20590462 fbshipit-source-id: 42a39837b514a546ac9471bfaff60eefe7fae829
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