GitLab

Summary:
There was a bug when `z_clip_value` is not None but there are no faces which are actually visible in the image due to culling.  In `rasterize_meshes.py` a function `convert_clipped_rasterization_to_original_faces` is called to convert the clipped face indices etc back to the unclipped versions, but the case where there is no clipping was not handled correctly.

Fixes Github Issue #632

Reviewed By: bottler

Differential Revision: D29116150

fbshipit-source-id: fae82a0b4848c84b3ed7c7b04ef5c9848352cf5c

Summary: Fixed multiple issues with shape broadcasting in lighting, shading and blending and updated the tests.

Reviewed By: bottler

Differential Revision: D28997941

fbshipit-source-id: d3ef93f979344076b1d9098a86178b4da63844c8

Summary: Nightly builds to support PyTorch 1.8.0 and PyTorch 1.8.1.

Reviewed By: patricklabatut

Differential Revision: D29098081

fbshipit-source-id: fc6b36e919892ea41979a03e64a6fc8003528b78

Summary: Increase code coverage of subdivide_meshes and re-include it in code coverage test

Reviewed By: bottler

Differential Revision: D29097476

fbshipit-source-id: 3403ae38a90c4b53f24188eed11faae202a235b5

Summary:
When `z_clip_value = None` and faces are outside the view frustum the shape of one of the tensors in `clip.py` is incorrect.

`faces_num_clipped_verts` should be (F,) but it was (F,3).  Added a new test to ensure this case is handled.

Reviewed By: bottler

Differential Revision: D29051282

fbshipit-source-id: 5f4172ba4d4a75d928404dde9abf48aef18c68bd

Summary:
When textures are set on `Meshes` we need to check if the dimensions actually match that of the verts/faces in the mesh. There was a github issue where someone tried to set the attribute after construction of the `Meshes` object and ran into an error when trying to sample textures.

The desired usage is to initialize the class with the textures (not set an attribute afterwards) but in any case we need to check the dimensions match before sampling textures.

Reviewed By: bottler

Differential Revision: D29020249

fbshipit-source-id: 9fb8a5368b83c9ec53652df92b96fc8b2613f591

Summary: Omit specific code from code coverage computation. This is done to make code coverage test pass again. Test coverage for shader.py and subdivide_meshes.py will be increased in later diffs to re-include them.

Reviewed By: bottler

Differential Revision: D29061105

fbshipit-source-id: addac35a216c96de9f559e2d8fe42496adc85791

Summary:
There are a couple times in the code where Device, make_device and get_device are imported by:
`from ..common.types import Device, get_device, make_device`
which will not work without this init file, at least on python 3.8.

Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/706

Reviewed By: bottler

Differential Revision: D29031835

Pulled By: patricklabatut

fbshipit-source-id: 15743e3c8cffdfcca51b6d2f377b923da9cbe6f9

Summary: Make Transform3d.to() not ignore a different dtype when device is the same and no copy is requested. Fix other methods where dtype is ignored.

Reviewed By: nikhilaravi

Differential Revision: D28981171

fbshipit-source-id: 4528e6092f4a693aecbe8131ede985fca84e84cf

Summary: Fix type annotations for device type

Reviewed By: nikhilaravi

Differential Revision: D28971179

fbshipit-source-id: 410b673c76dfd65ac51b2d144f17ed86a04a3058

Summary:
Tidy uses of `torch.device` in `Volumes`:
- Allow `str` or `torch.device` in `Volumes.to()` method
- Consistently use `torch.device` for internal type
- Fix comparison of devices

Reviewed By: nikhilaravi

Differential Revision: D28970876

fbshipit-source-id: c640cc22ced684a54cc450ac38a0f4b3435d47be

Summary:
Tidy uses of `torch.device` in `Pointclouds`:
- Allow `str` or `torch.device` in `Pointclouds.to()` method
- Consistently use `torch.device` for internal type
- Fix comparison of devices

Reviewed By: nikhilaravi

Differential Revision: D28970221

fbshipit-source-id: 3ca7104d4c0d9b20b0cff4f00e3ce931c5f1528a

Summary:
Tidy uses of `torch.device` in `Meshes`:
- Allow `str` or `torch.device` in `Meshes.to()` method
- Consistently use `torch.device` for internal type
- Fix comparison of devices

Reviewed By: nikhilaravi

Differential Revision: D28969461

fbshipit-source-id: 16d3c1f5458954bb11fdf0efea88542e94dccd7a

Summary:
Tidy uses of `torch.device` in `Transforms3d`:
- Allow `str` or `torch.device` in user-facing methods
- Consistently use `torch.device` for internal types
- Fix comparison of devices

Reviewed By: nikhilaravi

Differential Revision: D28929486

fbshipit-source-id: bd1d6cc7ede3d8fd549fd3224a9b07eec53f8164

Summary: Introduce device type and utility functions in common types module

Reviewed By: nikhilaravi

Differential Revision: D28970930

fbshipit-source-id: 191ec07390ed66a958c23eb2b43229312492e0b7

Summary: Add missing dtype parameter type annotations

Reviewed By: nikhilaravi

Differential Revision: D28943370

fbshipit-source-id: 2a411d78895f3f3aa9ab0e4807c17a13e7f25caf

Summary: Improve type annotations for volumes and remove a few pyre fixmes

Reviewed By: nikhilaravi

Differential Revision: D28943371

fbshipit-source-id: ca2b7a50d72a392910e65cee5e564f34523414d2

Summary: Updated the alpha channel in the `hard_rgb_blend` function to return the mask of the pixels which have overlapping mesh faces.

Reviewed By: bottler

Differential Revision: D29001604

fbshipit-source-id: 22a2173d769f2d3ad34892d68ceb628f073bca22

Differential Revision: D28962863

fbshipit-source-id: 7acc8e2f08ea7a94294647431561ff2cfd52a97d

Summary: If we are not visualizing the training with visdom, then there are a couple of outputs of the coarse rendering step which are not small and are returned by the renderer but never used. We don't need to bother transferring them to the CPU.

Reviewed By: nikhilaravi

Differential Revision: D28939958

fbshipit-source-id: 7e0d6681d6524f7fb57b6b20164580006120de80

Summary:
One test hits problems with CUDA 11.1 and pytorch 1.8. This seems to be a known bug, so we just run that test on the cpu in the problematic cases.

Note - the full test run is much slower with cuda 11.1 than 10.2, but this is known.

Reviewed By: patricklabatut

Differential Revision: D28938933

fbshipit-source-id: cf8ed84cd10a0b52d8f4292edbef7bd4844fea65

Summary: Improve type annotations for textures and remove a few pyre fixmes

Reviewed By: nikhilaravi

Differential Revision: D28942630

fbshipit-source-id: 422f2bdf07b435869461ca103d71473aa0c2b814

Summary:
All classes implicitly inherit from `object` since Python 3, so remove unnecessary explicit inheritance.

From the [official documentation](https://docs.python.org/3/library/functions.html#object):

> `object` is a base for all classes.

Reviewed By: nikhilaravi

Differential Revision: D28942595

fbshipit-source-id: 466c0d19d8a93a6263e7ad734c3e87160cfa6066

Summary: Remove extra pyre fixmes

Reviewed By: bottler

Differential Revision: D28929468

fbshipit-source-id: 175b7986d49b56de7af063e97a9b0423570f9093

Summary: Restore assertNormsClose's printing of its message on failure which I broke in D26233419 (https://github.com/facebookresearch/pytorch3d/commit/cd9786e787386c185ef915b3983c5d2861a32907).

Reviewed By: nikhilaravi

Differential Revision: D28799743

fbshipit-source-id: e7a24b2558b68991c731bbd55fb3ca6c1df98f69

Summary: make assertClose print its failure information even if a message is supplied.

Reviewed By: nikhilaravi

Differential Revision: D28799745

fbshipit-source-id: 787c8c356342420cd8f40fdc0b2aba036142298e

Summary:
Caught a silly typo in the docs, figured I'd send in a PR. Thanks for sharing your work!

Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/693

Reviewed By: bottler

Differential Revision: D28837851

Pulled By: patricklabatut

fbshipit-source-id: c326189007c3537429a8aa2a3b2850d60b74b16d

Summary:
Inside the implicit function, the color and density calculations are independent and time is saved by putting them on separate streams.

(In fact, colors is slower than densities, and the raymarcher does some calculation with the densities before the colors. So theoretically we could go further and not join the streams together until the colors are actually needed. The code would be more complicated. But the profile suggests that the raymarcher is quick and so this wouldn't be expected to make a big difference.)

In inference, this might increase memory usage, so it isn't an obvious win. That is why I have added a flag.

Reviewed By: nikhilaravi

Differential Revision: D28648549

fbshipit-source-id: c087de80d8ccfce1dad3a13e71df2f305a36952e

Summary: When harmonic embedding is used, we always cat its input onto its output before proceeding. Avoid an intermediate tensor by making the module do that for itself.

Reviewed By: davnov134

Differential Revision: D28185791

fbshipit-source-id: 98d92c94a918dd42e16cdadcaac71dabbc7de5c3

Summary:
Add custom layer to avoid repeating copied data for every ray position.

This should also save time in the backward pass because there are fewer multiplies with the weights.

Reviewed By: theschnitz

Differential Revision: D28382412

fbshipit-source-id: 1ba7356cd8520ebd598568ae503e47d31d3989eb

Differential Revision: D28766465

fbshipit-source-id: 9c5b123ac279e25965ebc8827d9a947bbb6f3ac5

Summary:
The multiplicative factors in function embeddings go from `2**0` to `2**(self.n_harmonic_functions-1)`, and not from `2**0` to `2**self.n_harmonic_functions`.

Pull Request resolved: https://github.com/fairinternal/pytorch3d/pull/13

Reviewed By: nikhilaravi

Differential Revision: D28637894

Pulled By: ignacio-rocco

fbshipit-source-id: da20f39eba9aaa09af5b24be1554a3bfd7556281

Summary: Experimental data loader for taking the default scene from a GLB file and converting it to a single mesh in PyTorch3D.

Reviewed By: nikhilaravi

Differential Revision: D25900167

fbshipit-source-id: bff22ac00298b83a0bd071ae5c8923561e1d81d7

Summary:
Specific object to represent light which is 100% everywhere. Sometimes lighting is irrelevant, for example when viewing a mesh which has lighting already baked in.

This is not primarily aiming for a performance win but I think the test which has changed might be a bit faster.

Reviewed By: theschnitz

Differential Revision: D26405151

fbshipit-source-id: 82eae55de0bee918548a3eaf031b002cb95e726c

Summary: Function to relatively rotate a camera position. Also document how to relatively translate a camera position.

Reviewed By: theschnitz

Differential Revision: D25900166

fbshipit-source-id: 2ddaf06ee7c5e2a2e973c04d7dee6ccb61c6ff84

Summary:
If you join several meshes which have TexturesUV textures using join_meshes_as_scene then we amalgamate all the texture images in to a single one. This now checks if some of the images are equal (i.e. the tensors are the same tensor, in the `is` sense; they have the same `id` in Python) and only uses one copy if they are.

I have an example of a massive scene made of several textured meshes with some shared, where this makes the difference between fitting the data on the GPU and not.

Reviewed By: theschnitz

Differential Revision: D25982364

fbshipit-source-id: a8228805f38475c796302e27328a340d9b56c8ef

Summary: Currently the Rotate transform does not consider the R's device at all, resulting in errors if you're expecting it to be on cuda but it gets the default casting to cpu. This updates the transform to respect R's device.

Reviewed By: nikhilaravi

Differential Revision: D27828118

fbshipit-source-id: ddd99f73eadbd990688eb22f3d1ffbacbe168c81

Summary: Add gitignore file to ignore data and checkpoints in the NeRF project.

Reviewed By: nikhilaravi

Differential Revision: D28382413

fbshipit-source-id: 747d69f4353a76a28acde8ba26a896cb2278f976

Summary: Avoid test files explicitly importing TestCase objects from each other, because doing so causes the tests to be discovered twice by unittest discover. This means moving a few static functions out of their classes. I noticed this while trying to fix failures from yesterday.

Reviewed By: nikhilaravi

Differential Revision: D28194679

fbshipit-source-id: ac6e6585603bd4ef9c098cdd56891d94f8923ba6

Summary:
A couple of tests are failing in open source after my changes yesterday because of numerical issues calculating normals. In particular we have meshes with very few vertices and several faces, where the normals should be zero but end up non-negligible after F.normalize. I have no idea why the different environments produce different results, so that the tests are passing internally.

An example. Consider a mesh with the following faces:
```
tensor([[4, 0, 2],
        [4, 1, 2],
        [3, 1, 0],
        [1, 3, 1],
        [3, 0, 1],
        [4, 0, 0],
        [4, 0, 2]])
```
At vertex 3, there is one zero-area face and there are two other faces, which are back-to-back with each other. This vertex should have zero normal. The open source calculation produces a small but nonzero normal which varies unpredictably with changes in scale/offset, which can cause test failures.

In this diff, the main change is to increase the number of vertices to make this less likely to happen. Also a small change to init_mesh to make it not generate a batch of empty meshes.

Reviewed By: nikhilaravi

Differential Revision: D28220984

fbshipit-source-id: 79fdc62e5f5f8836de5a3a9980cfd6fe44590359