GitLab

Summary: Add conda builds for the newly released PyTorch version 1.9.1.

Reviewed By: patricklabatut

Differential Revision: D31140206

fbshipit-source-id: 697549a3ef0db8248f4f9b5c00cf1407296b5022

Summary:
Copy some descriptions of renderer parameters to more places so they are easier to find.

Also a couple of small corrections, and make RasterizationSettings a dataclass.

Reviewed By: nikhilaravi, patricklabatut

Differential Revision: D30899822

fbshipit-source-id: 805cf366acb7d51cb308fa574deff0657c199673

Summary: Attempt to fix #659, an observation that the rasterizer is nondeterministic, by resolving tied faces by picking those with lower index.

Reviewed By: nikhilaravi, patricklabatut

Differential Revision: D30699039

fbshipit-source-id: 39ed797eb7e9ce7370ae71259ad6b757f9449923

Summary: Unlike other cu files, sigmoid_alpha_blend uses torch/extension.h. Avoid for possible build speed win and because of a reported problem #843 on windows with CUDA 11.4.

Reviewed By: nikhilaravi

Differential Revision: D31054121

fbshipit-source-id: 53a1f985a1695a044dfd2ee1a5b0adabdf280595

Summary: Rename sample_farthest_point.cpp to not match its CUDA equivalent.

Reviewed By: nikhilaravi

Differential Revision: D31006645

fbshipit-source-id: 135b511cbde320d2b3e07fc5b027971ef9210aa9

Summary: Remove use of nonstandard C++. Noticed on windows in issue https://github.com/facebookresearch/pytorch3d/issues/843. (We use `__restrict__` in CUDA, where it is fine, even on windows)

Reviewed By: nikhilaravi

Differential Revision: D31006516

fbshipit-source-id: 929ba9b3216cb70fad3ffa3274c910618d83973f

Differential Revision: D31042748

fbshipit-source-id: fffb983bd6765d306a407587ddf64e68e57e9ecc

Summary:
CUDA implementation of farthest point sampling algorithm.

## Visual comparison

Compared to random sampling, farthest point sampling gives better coverage of the shape.

{F658631262}

## Reduction

Parallelized block reduction to find the max value at each iteration happens as follows:

1. First split the points into two equal sized parts (e.g. for a list with 8 values):
`[20, 27, 6, 8 | 11, 10, 2, 33]`
2. Use half of the thread (4 threads) to compare pairs of elements from each half (e.g elements [0, 4], [1, 5] etc) and store the result in the first half of the list:
`[20, 27, 6, 33 | 11, 10, 2, 33]`
Now we no longer care about the second part but again divide the first part into two
`[20, 27 | 6, 33| -, -, -, -]`
Now we can use 2 threads to compare the 4 elements
4. Finally we have gotten down to a single pair
`[20 | 33 | -, - | -, -, -, -]`
Use 1 thread to compare the remaining two elements
5. The max will now be at thread id = 0
`[33 | - | -, - | -, -, -, -]`
The reduction will give the farthest point for the selected batch index at this iteration.

Reviewed By: bottler, jcjohnson

Differential Revision: D30401803

fbshipit-source-id: 525bd5ae27c4b13b501812cfe62306bb003827d2

Summary: C++ implementation of iterative farthest point sampling.

Reviewed By: jcjohnson

Differential Revision: D30349887

fbshipit-source-id: d25990f857752633859fe00283e182858a870269

Summary:
This is a naive python implementation of the iterative farthest point sampling algorithm along with associated simple tests. The C++/CUDA implementations will follow in subsequent diffs.

The algorithm is used to subsample a pointcloud with better coverage of the space of the pointcloud.

The function has not been added to `__init__.py`. I will add this after the full C++/CUDA implementations.

Reviewed By: jcjohnson

Differential Revision: D30285716

fbshipit-source-id: 33f4181041fc652776406bcfd67800a6f0c3dd58

Summary: Fix issue #826. This is a correction to the joining of TexturesUV into a single scene.

Reviewed By: nikhilaravi

Differential Revision: D30767092

fbshipit-source-id: 03ba6a1d2f22e569d1b3641cd13ddbb8dcb87ec7

Summary:
* HAT_INV_SKEW_SYMMETRIC_TOL was a global variable and torch script gives an error when compiling that function. Move it to the function scope.
* torch script gives error when compiling acos_linear_extrapolation because bound is a union of tuple and float. The tuple version is kept in this diff.

Reviewed By: patricklabatut

Differential Revision: D30614916

fbshipit-source-id: 34258d200dc6a09fbf8917cac84ba8a269c00aef

Summary: In D30349234 (https://github.com/facebookresearch/pytorch3d/commit/1b8d86a104eab24ac25863c423d084d611f64bae) we introduced persistent=False to some register_buffer calls, which depend on PyTorch 1.6. We go back to the old behaviour for PyTorch 1.5.

Reviewed By: nikhilaravi

Differential Revision: D30731327

fbshipit-source-id: ab02ef98ee87440ef02479b72f4872b562ab85b5

Summary:
There has historically been a lot of duplication between the coarse rasterization logic for point clouds and meshes. This diff factors out the shared logic, so coarse rasterization of point clouds and meshes share the same core logic.

Previously the only difference between the coarse rasterization kernels for points and meshes was the logic for checking whether a {point / triangle} intersects a tile in the image. We implement a generic coarse rasterization kernel that takes a set of 2D bounding boxes rather than geometric primitives; we then implement separate kernels that compute 2D bounding boxes for points and triangles.

This change does not affect the Python API at all. It also should not change any rasterization behavior, since this diff is just a refactoring of the existing logic.

I see this diff as the first in a few pieces of rasterizer refactoring. Followup diffs should do the following:
- Add a check for bin overflow in the generic coarse rasterizer kernel: allocate a global scalar to flag bin overflow which kernel worker threads can write to in case they detect bin overflow. The C++ launcher function can then check this flag after the kernel returns and issue a warning to the user in case of overflow.
- As a slightly more involved mechanism, if bin overflow is detected then the coarse kernel can continue running in order to count how many elements fall into each bin, without actually writing out their indices to the coarse output tensor. Then the actual number of entries per bin can be used to re-allocate the output tensor and re-run the coarse rasterization kernel so that bin overflow can be automatically avoided.
- The unification of the coarse and fine rasterization kernels also allows us to insert an extra CUDA kernel prior to coarse rasterization that filters out primitives outside the view frustum. This would be helpful for rendering full scenes (e.g. Matterport data) where only a small piece of the mesh is actually visible at any one time.

Reviewed By: bottler

Differential Revision: D25710361

fbshipit-source-id: 9c9dea512cb339c42adb3c92e7733fedd586ce1b

Summary: Renaming parts of the mesh coarse rasterization and separating the bounding box calculation. All in preparation for sharing code with point rasterization.

Reviewed By: bottler

Differential Revision: D30369112

fbshipit-source-id: 3508c0b1239b355030cfa4038d5f3d6a945ebbf4

Summary: In preparation for sharing coarse rasterization between point clouds and meshes, move the functions to a new file. No code changes.

Reviewed By: bottler

Differential Revision: D30367812

fbshipit-source-id: 9e73835a26c4ac91f5c9f61ff682bc8218e36c6a

Summary: Change cyclic deps test to be independent of test discovery order. Also let it work without plotly.

Reviewed By: nikhilaravi

Differential Revision: D30669614

fbshipit-source-id: 2eadf3f8b56b6096c5466ce53b4f8ac6df27b964

Summary: Regenerate config.yml after a recent bad merge which lost a few builds.

Reviewed By: nikhilaravi

Differential Revision: D30696918

fbshipit-source-id: 3ecdfca8682baed13692ec710aa7c25dbd24dd44

Summary: Fixes GitHub issue #751. The vectorized implementation of bilinear interpolation didn't properly handle the edge cases in the same way as the `grid_sample` method in PyTorch.

Reviewed By: bottler

Differential Revision: D30684208

fbshipit-source-id: edf241ecbd72d46b94ad340a4e601e26c83db88e

Summary: Replace master with main in hard coded paths or mentions in documentation

Reviewed By: bottler

Differential Revision: D30696097

fbshipit-source-id: d5ff67bb026d90d1543d10ab027f916e8361ca69

Summary:
As suggested in #802. By not persisting the _xy_grid buffer, we can allow (in some cases) a model with one image_size to be loaded from a saved model which was trained at a different resolution.

Also avoid persisting _frequencies in HarmonicEmbedding for similar reasons.

BC-break: This will cause load_state_dict, in strict mode, to complain if you try to load an old model with the new code.

Reviewed By: patricklabatut

Differential Revision: D30349234

fbshipit-source-id: d6061d1e51c9f79a78d61a9f732c9a5dfadbbb47

Summary:
Use PyTorch3D's new faster sample_pdf function instead of local Python implementation.

Also clarify deps for the Python implementation.

Reviewed By: gkioxari

Differential Revision: D30512109

fbshipit-source-id: 84cfdc00313fada37a6b29837de96f6a4646434f

Summary:
Great work! :)
Just found a link in the examples that is not working. This will fix it.

Best,
Alex

Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/818

Reviewed By: nikhilaravi

Differential Revision: D30637532

Pulled By: patricklabatut

fbshipit-source-id: ed6c52375d1e760cb0fb2c0a66648dfeb0c6ed46

Summary: We won't support PyTorch 1.4 in the next release. PyTorch 1.5.0 came out in June 2020, more than a year ago.

Reviewed By: patricklabatut

Differential Revision: D30424388

fbshipit-source-id: 25499096066c9a2b909a0550394f5210409f0d74

Summary: New test that each subpackage of pytorch3d imports cleanly.

Reviewed By: patricklabatut

Differential Revision: D30001632

fbshipit-source-id: ca8dcac94491fc22f33602b3bbef481cba927094

Differential Revision: D30479084

fbshipit-source-id: 6b22dd0afe4dfb1be6249e43a56657519f11dcf1

Summary: Implement the sample_pdf function from the NeRF project as compiled operators.. The binary search (in searchsorted) is replaced with a low tech linear search, but this is not a problem for the envisaged numbers of bins.

Reviewed By: gkioxari

Differential Revision: D26312535

fbshipit-source-id: df1c3119cd63d944380ed1b2657b6ad81d743e49

Summary: Copy the sample_pdf operation from the NeRF project in to PyTorch3D, in preparation for optimizing it.

Reviewed By: gkioxari

Differential Revision: D27117930

fbshipit-source-id: 20286b007f589a4c4d53ed818c4bc5f2abd22833

Summary: Small fix for omitting this argument.

Reviewed By: nikhilaravi

Differential Revision: D29548610

fbshipit-source-id: f25032fab3faa2f09006f5fcf8628138555f2f20

Summary:
Add a CPU version to the benchmarks.

```
Benchmark                                                               Avg Time(μs)      Peak Time(μs) Iterations
--------------------------------------------------------------------------------
ADD_POINTS_TO_VOLUMES_cpu_10_trilinear_[25, 25, 25]_1000                    10100           46422             50
ADD_POINTS_TO_VOLUMES_cpu_10_trilinear_[25, 25, 25]_10000                   28442           32100             18
ADD_POINTS_TO_VOLUMES_cpu_10_trilinear_[25, 25, 25]_100000                 241127          254269              3
ADD_POINTS_TO_VOLUMES_cpu_10_trilinear_[101, 111, 121]_1000                 54149           79480             10
ADD_POINTS_TO_VOLUMES_cpu_10_trilinear_[101, 111, 121]_10000               125459          212734              4
ADD_POINTS_TO_VOLUMES_cpu_10_trilinear_[101, 111, 121]_100000              512739          512739              1
ADD_POINTS_TO_VOLUMES_cpu_10_nearest_[25, 25, 25]_1000                       2866           13365            175
ADD_POINTS_TO_VOLUMES_cpu_10_nearest_[25, 25, 25]_10000                      7026           12604             72
ADD_POINTS_TO_VOLUMES_cpu_10_nearest_[25, 25, 25]_100000                    48822           55607             11
ADD_POINTS_TO_VOLUMES_cpu_10_nearest_[101, 111, 121]_1000                   38098           38576             14
ADD_POINTS_TO_VOLUMES_cpu_10_nearest_[101, 111, 121]_10000                  48006           54120             11
ADD_POINTS_TO_VOLUMES_cpu_10_nearest_[101, 111, 121]_100000                131563          138536              4
ADD_POINTS_TO_VOLUMES_cpu_100_trilinear_[25, 25, 25]_1000                   64615           91735              8
ADD_POINTS_TO_VOLUMES_cpu_100_trilinear_[25, 25, 25]_10000                 228815          246095              3
ADD_POINTS_TO_VOLUMES_cpu_100_trilinear_[25, 25, 25]_100000               3086615         3086615              1
ADD_POINTS_TO_VOLUMES_cpu_100_trilinear_[101, 111, 121]_1000               464298          465292              2
ADD_POINTS_TO_VOLUMES_cpu_100_trilinear_[101, 111, 121]_10000             1053440         1053440              1
ADD_POINTS_TO_VOLUMES_cpu_100_trilinear_[101, 111, 121]_100000            6736236         6736236              1
ADD_POINTS_TO_VOLUMES_cpu_100_nearest_[25, 25, 25]_1000                     11940           12440             42
ADD_POINTS_TO_VOLUMES_cpu_100_nearest_[25, 25, 25]_10000                    56641           58051              9
ADD_POINTS_TO_VOLUMES_cpu_100_nearest_[25, 25, 25]_100000                  711492          711492              1
ADD_POINTS_TO_VOLUMES_cpu_100_nearest_[101, 111, 121]_1000                 326437          329846              2
ADD_POINTS_TO_VOLUMES_cpu_100_nearest_[101, 111, 121]_10000                418514          427911              2
ADD_POINTS_TO_VOLUMES_cpu_100_nearest_[101, 111, 121]_100000              1524285         1524285              1
ADD_POINTS_TO_VOLUMES_cuda:0_10_trilinear_[25, 25, 25]_1000                  5949           13602             85
ADD_POINTS_TO_VOLUMES_cuda:0_10_trilinear_[25, 25, 25]_10000                 5817           13001             86
ADD_POINTS_TO_VOLUMES_cuda:0_10_trilinear_[25, 25, 25]_100000               23833           25971             21
ADD_POINTS_TO_VOLUMES_cuda:0_10_trilinear_[101, 111, 121]_1000               9029           16178             56
ADD_POINTS_TO_VOLUMES_cuda:0_10_trilinear_[101, 111, 121]_10000             11595           18601             44
ADD_POINTS_TO_VOLUMES_cuda:0_10_trilinear_[101, 111, 121]_100000            46986           47344             11
ADD_POINTS_TO_VOLUMES_cuda:0_10_nearest_[25, 25, 25]_1000                    2554            9747            196
ADD_POINTS_TO_VOLUMES_cuda:0_10_nearest_[25, 25, 25]_10000                   2676            9537            187
ADD_POINTS_TO_VOLUMES_cuda:0_10_nearest_[25, 25, 25]_100000                  6567           14179             77
ADD_POINTS_TO_VOLUMES_cuda:0_10_nearest_[101, 111, 121]_1000                 5840           12811             86
ADD_POINTS_TO_VOLUMES_cuda:0_10_nearest_[101, 111, 121]_10000                6102           13128             82
ADD_POINTS_TO_VOLUMES_cuda:0_10_nearest_[101, 111, 121]_100000              11945           11995             42
ADD_POINTS_TO_VOLUMES_cuda:0_100_trilinear_[25, 25, 25]_1000                 7642           13671             66
ADD_POINTS_TO_VOLUMES_cuda:0_100_trilinear_[25, 25, 25]_10000               25190           25260             20
ADD_POINTS_TO_VOLUMES_cuda:0_100_trilinear_[25, 25, 25]_100000             212018          212134              3
ADD_POINTS_TO_VOLUMES_cuda:0_100_trilinear_[101, 111, 121]_1000             40421           45692             13
ADD_POINTS_TO_VOLUMES_cuda:0_100_trilinear_[101, 111, 121]_10000            92078           92132              6
ADD_POINTS_TO_VOLUMES_cuda:0_100_trilinear_[101, 111, 121]_100000          457211          457229              2
ADD_POINTS_TO_VOLUMES_cuda:0_100_nearest_[25, 25, 25]_1000                   3574           10377            140
ADD_POINTS_TO_VOLUMES_cuda:0_100_nearest_[25, 25, 25]_10000                  7222           13023             70
ADD_POINTS_TO_VOLUMES_cuda:0_100_nearest_[25, 25, 25]_100000                48127           48165             11
ADD_POINTS_TO_VOLUMES_cuda:0_100_nearest_[101, 111, 121]_1000               34732           35295             15
ADD_POINTS_TO_VOLUMES_cuda:0_100_nearest_[101, 111, 121]_10000              43050           51064             12
ADD_POINTS_TO_VOLUMES_cuda:0_100_nearest_[101, 111, 121]_100000            106028          106058              5
--------------------------------------------------------------------------------
```

Reviewed By: patricklabatut

Differential Revision: D29522830

fbshipit-source-id: 1e857db03613b0c6afcb68a58cdd7ba032e1a874

Summary: Fixes to a couple of comments on points to volumes, make the mask work in round_points_to_volumes, and remove a duplicate rand calculation

Reviewed By: nikhilaravi

Differential Revision: D29522845

fbshipit-source-id: 86770ba37ef3942b909baf63fd73eed1399635b6

Summary: Much of the code is actually available during the conda tests, as long as we look in the right place. We enable some of them.

Reviewed By: nikhilaravi

Differential Revision: D30249357

fbshipit-source-id: 01c57b6b8c04442237965f23eded594aeb90abfb

Summary: Change doc references to master branch to its new name main.

Reviewed By: nikhilaravi

Differential Revision: D30303018

fbshipit-source-id: cfdbb207dfe3366de7e0ca759ed56f4b8dd894d1

Summary:
Implementation of ball query from PointNet++.  This function is similar to KNN (find the neighbors in p2 for all points in p1). These are the key differences:
-  It will return the **first** K neighbors within a specified radius as opposed to the **closest** K neighbors.
- As all the points in p2 do not need to be considered to find the closest K, the algorithm is much faster than KNN when p2 has a large number of points.
- The neighbors are not sorted
- Due to the radius threshold it is not guaranteed that there will be K neighbors even if there are more than K points in p2.
- The padding value for `idx` is -1 instead of 0.

# Note:
- Some of the code is very similar to KNN so it could be possible to modify the KNN forward kernels to support ball query.
- Some users might want to use kNN with ball query - for this we could provide a wrapper function around the current `knn_points` which enables applying the radius threshold afterwards as an alternative. This could be called `ball_query_knn`.

Reviewed By: jcjohnson

Differential Revision: D30261362

fbshipit-source-id: 66b6a7e0114beff7164daf7eba21546ff41ec450

Summary: New test that notes and tutorials are listed in the website metadata, so that they will be included in the website build.

Reviewed By: nikhilaravi

Differential Revision: D30223799

fbshipit-source-id: 2dca9730b54e68da2fd430a7b47cb7e18814d518

Summary: Recent additions need to be included.

Reviewed By: nikhilaravi

Differential Revision: D30223717

fbshipit-source-id: 4b29a4132ea6fb7c1a530aac5d1e36aa61c663bb

Differential Revision: D30222339

fbshipit-source-id: 97d498df72ef897b8dc2405764e3ffd432082e3c

Summary: Fix to resolve GitHub issue #796 - the cameras were being passed in the renderer forward pass instead of at initialization. The rasterizer was correctly using the cameras passed in the `kwargs` for the projection, but the `cameras` are still part of the `kwargs` for the `get_screen_to_ndc_transform` and `get_ndc_to_screen_transform` functions which is causing issues about duplicate arguments.

Reviewed By: bottler

Differential Revision: D30175679

fbshipit-source-id: 547e88d8439456e728fa2772722df5fa0fe4584d

Summary: PyTorch3D version 0.5.0

Reviewed By: patricklabatut

Differential Revision: D29538174

fbshipit-source-id: 332516faa1d8e7bfa7c74ec3fecddc55439e2550

Summary: At the next release, the prebuilt PyTorch3D wheels will depend on PyTorch 1.9.0. Update the tutorials to expect this.

Reviewed By: nikhilaravi

Differential Revision: D29614450

fbshipit-source-id: 39978a6a55b62fb7c7e62aaa8f138e47cadd631e