Summary: Make black and isort stop disagreeing by removing some unneeded comments around import statements. pyre ignores are moved.
Reviewed By: theschnitz
Differential Revision: D27118137
fbshipit-source-id: 9926d0f21142adcf9b5cfe1d394754317f6386df
Summary:
Defines a function to run marching cubes algorithm on a single or batch of 3D scalar fields. Returns a mesh's faces and vertices.
UPDATES (12/18)
- Input data is now specified as a (B, D, H, W) tensor as opposed to a (B, W, H, D) tensor. This will now be compatible with the Volumes datastructure.
- Add an option to return output vertices in local coordinates instead of world coordinates.
Also added a small fix to remove the dype for device in Transforms3D - if passing in a torch.device instead of str it causes a pyre error.
Reviewed By: jcjohnson
Differential Revision: D24599019
fbshipit-source-id: 90554a200319fed8736a12371cc349e7108aacd0
Summary:
There are a couple of options for supporting non square images:
1) NDC stays at [-1, 1] in both directions with the distance calculations all modified by (W/H). There are a lot of distance based calculations (e.g. triangle areas for barycentric coordinates etc) so this requires changes in many places.
2) NDC is scaled by (W/H) so the smallest side has [-1, 1]. In this case none of the distance calculations need to be updated and only the pixel to NDC calculation needs to be modified.
I decided to go with option 2 after trying option 1!
API Changes:
- Image size can now be specified optionally as a tuple
TODO:
- add a benchmark test for the non square case.
Reviewed By: jcjohnson
Differential Revision: D24404975
fbshipit-source-id: 545efb67c822d748ec35999b35762bce58db2cf4
Summary: Taubin Smoothing for filtering meshes and making them smoother. Taubin smoothing is an iterative approach.
Reviewed By: nikhilaravi
Differential Revision: D24751149
fbshipit-source-id: fb779e955f1a1f6750e704f1b4c6dfa37aebac1a
Summary: Move to a local import for calculating pointcloud normals, similar to _compute_face_areas_normals on Meshes.
Reviewed By: theschnitz
Differential Revision: D24695260
fbshipit-source-id: 9e1eb5d15017975b8c4f4175690cc3654f38d9a4
Summary:
As pointed out in #328, we had an indexing operation where a reshape would do and be faster. The resulting faces_packed_to_edges_packed is no longer contiguous.
Also fix a use of faces_packed_to_edges_packed which might modify the object unintentionally.
Reviewed By: theschnitz
Differential Revision: D24390292
fbshipit-source-id: 225677d8fcc1d6b76efad7706718ecdb5182ffe1
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
Summary:
1. CircleCI tests fail because of different randomisation. I was able to reproduce it on devfair (with an older version of pytorch3d though), but with a new threshold, it works. Let’s push and see if it will work in CircleCI.
2. Fixing linter’s issue with `l` variable name.
Reviewed By: bottler
Differential Revision: D22573244
fbshipit-source-id: 32cebc8981883a3411ed971eb4a617469376964d
Summary: When rendering meshes with Phong shading, interpolate_face_attributes was taking up a nontrivial fraction of the overall shading time. This diff replaces our Python implementation of this function with a CUDA implementation.
Reviewed By: nikhilaravi
Differential Revision: D21610763
fbshipit-source-id: 2bb362a28f698541812aeab539047264b125ebb8
Summary:
There is a bug in efficient PnP that incorrectly weights points. This fixes it.
The test does not pass for the previous version with the bug.
Reviewed By: shapovalov
Differential Revision: D22449357
fbshipit-source-id: f5a22081e91d25681a6a783cce2f5c6be429ca6a
Summary:
Automatic run to suppress type errors.
#pyreupgrade
Differential Revision: D22369027
fbshipit-source-id: 2beb1a43e429a0850944a8849d416bedefd516ed
Summary: Added `sorted` argument to the `knn_points` function. This came up during the benchmarking against Faiss - sorting added extra memory usage. Match the memory usage of Faiss by making sorting optional.
Reviewed By: bottler, gkioxari
Differential Revision: D22329070
fbshipit-source-id: 0828ff9b48eefce99ce1f60089389f6885d03139
Summary: To avoid pytorch warnings and future behaviour changes, stop using torch.div and / with tensors of integers.
Reviewed By: gkioxari, mruberry
Differential Revision: D21857955
fbshipit-source-id: fb9f3000f3d953352cdc721d2a5f73d3a4bbf4b7
Summary:
This diff is auto-generated to upgrade the Pyre version and suppress errors in vision. The upgrade will affect Pyre local configurations in the following directories:
```
vision/ale/search
vision/fair/fvcore
vision/fair/pytorch3d
vision/ocr/rosetta_hash
vision/vogue/personalization
```
Differential Revision: D21688454
fbshipit-source-id: 1f3c3fee42b6da2e162fd0932742ab8c5c96aa45
Summary: lg-zhang found the problem with the quadratic part of ePnP implementation: n262385 . It was caused by a coefficient returned from the linear equation solver being equal to exactly 0.0, which caused `sign()` to return 0, something I had not anticipated. I also made sure we avoid division by zero by clamping all relevant denominators.
Reviewed By: nikhilaravi, lg-zhang
Differential Revision: D21531200
fbshipit-source-id: 9eb2fa9d4f4f8f5f411d4cf1cffcc44b365b7e51
Summary: pytorch is adding checks that mean integer tensors with requires_grad=True need to be avoided. Fix accidentally creating them.
Reviewed By: jcjohnson, gkioxari
Differential Revision: D21576712
fbshipit-source-id: 008218997986800a36d93caa1a032ee91f2bffcd
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
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
Summary: Estimates normals of a point cloud.
Reviewed By: gkioxari
Differential Revision: D20860182
fbshipit-source-id: 652ec2743fa645e02c01ffa37c2971bf27b89cef
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
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
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
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
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
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
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
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
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
