Summary: Adding a function in pytorch3d.structures.meshes to join multiple meshes into a Meshes object representing a single mesh. The function currently ignores all textures.
Reviewed By: nikhilaravi
Differential Revision: D21876908
fbshipit-source-id: 448602857e9d3d3f774d18bb4e93076f78329823
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: [Folly] Cut the `FOR_EACH_ENUMERATE` macro, which may be replaced by a combination of range-for, `ranges::view::enumerate`, and structured bindings.
Reviewed By: markisaa
Differential Revision: D21813019
fbshipit-source-id: fc9ac09a4e2f72f1433d0a518f03d5cd69a59c55
Summary: Adds support to hard_rgb_blend and hard blending shaders in shader.py (HardPhongShader, HardGouraudShader, and HardFlatShader) for changing the background color on which objects are rendered
Reviewed By: nikhilaravi
Differential Revision: D21746062
fbshipit-source-id: 08001200f4339d6a69c52405c6b8f4cac9f3f56e
Summary:
Update the transform method in the mesh rasterizer class to use the new `update_padded` method on the `Meshes` class to directly update the mesh vertices.
Also added a benchmark.
Reviewed By: gkioxari
Differential Revision: D21700352
fbshipit-source-id: c330e4040c681729eb2cc7bdfd92fb4a51a1a7d6
Summary:
Three changes to Meshes
1. `num_verts_per_mesh` and `num_faces_per_mesh` are assigned at construction time and are returned without the need for `compute_packed`
2. `update_padded` updates `verts_padded` and shallow copies faces list and faces_padded and existing attributes from construction.
3. `padded_to_packed_idx` does not need `compute_packed`
Reviewed By: nikhilaravi
Differential Revision: D21653674
fbshipit-source-id: dc6815a2e2a925fe4a834fe357919da2b2c14527
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:
Fix division by zero when alpha is 1.0
In this case, the nominator is already 0 and we need to make sure division with 0 does not occur which would produce nans
Reviewed By: nikhilaravi
Differential Revision: D21650478
fbshipit-source-id: bc457105b3050fef1c8bd4e58e7d6d15c0c81ffd
Summary: Add ability to decode ply files which use types like int32.
Reviewed By: nikhilaravi
Differential Revision: D21639208
fbshipit-source-id: 0ede7d4aa353a6e940446680a18e7ac0c48fafee
Summary: I was trying to speed up the lighting computations, but my ideas didn't work. Even if that didn't work, we can at least commit the benchmarking script I wrote for diffuse and specular shading.
Reviewed By: nikhilaravi
Differential Revision: D21580171
fbshipit-source-id: 8b60c0284e91ecbe258b6aae839bd5c2bbe788aa
Summary:
Update the cuda kernels to:
- remove contiguous checks for the grad tensors and for cpu functions which use accessors
- for cuda implementations call `.contiguous()` on all tensors in the host function before invoking the kernel
Reviewed By: gkioxari
Differential Revision: D21598008
fbshipit-source-id: 9b97bda4582fd4269c8a00999874d4552a1aea2d
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:
Make flat shading differentiable again
Currently test fails with P130944403 which looks weird.
Reviewed By: nikhilaravi
Differential Revision: D21567106
fbshipit-source-id: 65995b64739e08397b3d021b65625e3c377cd1a5
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: Use `self.__class__` when creating new instances, to slightly accommodate inheritance.
Reviewed By: nikhilaravi
Differential Revision: D21504476
fbshipit-source-id: b4600d15462fc1985da95a4cf761c7d794cfb0bb
Summary:
Fixes the case where the rotation angle is exactly 0/PI.
Added a test for `so3_log_map(identity_matrix)`.
Reviewed By: nikhilaravi
Differential Revision: D21477078
fbshipit-source-id: adff804da97f6f0d4f50aa1f6904a34832cb8bfe
Summary: Fix to enable a mesh/point rasterizer to be initialized without having to specify the camera.
Reviewed By: jcjohnson, gkioxari
Differential Revision: D21362359
fbshipit-source-id: 4f84ea18ad9f179c7b7c2289ebf9422a2f5e26de
Summary: This has been failing intermittently
Reviewed By: nikhilaravi
Differential Revision: D21403157
fbshipit-source-id: 51b74d6c813b52effe72d14b565e250fcabbb463
Summary:
Ran the linter.
TODO: need to update the linter as per D21353065.
Reviewed By: bottler
Differential Revision: D21362270
fbshipit-source-id: ad0e781de0a29f565ad25c43bc94a19b1828c020
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
Summary: Update version number for version 0.2.0.
Reviewed By: nikhilaravi
Differential Revision: D21157358
fbshipit-source-id: 32a5b93e5dc65a31a806a5ce7231f8603fe02e85
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
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
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
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
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
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:
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
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
Summary: cuda 10.2 location on linux. Also remove unused conda test dependencies.
Reviewed By: nikhilaravi
Differential Revision: D21176409
fbshipit-source-id: dd3f339a92233ff16877ba76506ddf8f4418715d
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
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
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
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
Summary: This is mostly replacing the old PackedTensorAccessor with the new PackedTensorAccessor64.
Reviewed By: gkioxari
Differential Revision: D21088773
fbshipit-source-id: 5973e5a29d934eafb7c70ec5ec154ca076b64d27
Summary: A couple of files for the removed nearest_neighbor functionality are left behind.
Reviewed By: nikhilaravi
Differential Revision: D21088624
fbshipit-source-id: 4bb29016b4e5f63102765b384c363733b60032fa
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
