GitLab

Summary:
extracted blob loader
added documentation for blob_loader
did some refactoring on fields
for detailed steps and discussions see:
https://github.com/facebookresearch/pytorch3d/pull/1463
https://github.com/fairinternal/pixar_replay/pull/160

Reviewed By: bottler

Differential Revision: D44061728

fbshipit-source-id: eefb21e9679003045d73729f96e6a93a1d4d2d51

Summary: Provide an extension point pre_expand to let a configurable class A make sure another class B is registered before A is expanded. This reduces top level imports.

Reviewed By: bottler

Differential Revision: D44504122

fbshipit-source-id: c418bebbe6d33862d239be592d9751378eee3a62

Summary:
Introduces the OverfitModel for NeRF-style training with overfitting to one scene.
It is a specific case of GenericModel. It has been disentangle to ease usage.

## General modification

1. Modularize a minimum GenericModel to introduce OverfitModel
2. Introduce OverfitModel and ensure through unit testing that it behaves like GenericModel.

## Modularization

The following methods have been extracted from GenericModel to allow modularity with ManyViewModel:
- get_objective is now a call to weighted_sum_losses
- log_loss_weights
- prepare_inputs

The generic methods have been moved to an utils.py file.

Simplify the code to introduce OverfitModel.

Private methods like chunk_generator are now public and can now be used by ManyViewModel.

Reviewed By: shapovalov

Differential Revision: D43771992

fbshipit-source-id: 6102aeb21c7fdd56aa2ff9cd1dd23fd9fbf26315

Summary: New function

Reviewed By: davidsonic

Differential Revision: D42776590

fbshipit-source-id: 2a6e73480bcf2d1749f86bcb22d1942e3e8d3167

Reviewed By: bottler

Differential Revision: D43432438

fbshipit-source-id: 58159b2febb67febb533511eb2d1f47d40dad032

Summary: Indexing with a big matrix now fails with a ValueError, possibly because of pytorch improvements. Remove the testcase for it.

Reviewed By: davidsonic

Differential Revision: D42609741

fbshipit-source-id: 0a5a6632ed199cb942bfc4cc4ed347b72e491125

Reviewed By: shapovalov

Differential Revision: D42780711

fbshipit-source-id: 075fcae5097147b782f7ffc935f5430b824f58fd

Summary: For the new API, filtering iterators over sequences by subsets is quite helpful. The change is backwards compatible.

Reviewed By: bottler

Differential Revision: D42739669

fbshipit-source-id: d150a404aeaf42fd04a81304c63a4cba203f897d

Summary:
Fixes some issues with RayBundle plotting:
- allows plotting raybundles on gpu
- view -> reshape since we do not require contiguous raybundle tensors as input

Reviewed By: bottler, shapovalov

Differential Revision: D42665923

fbshipit-source-id: e9c6c7810428365dca4cb5ec80ef15ff28644163

Summary: docstring and shape fix

Reviewed By: shapovalov

Differential Revision: D42609661

fbshipit-source-id: fd50234872ad61b5452821eeb89d51344f70c957

Reviewed By: shapovalov

Differential Revision: D42545069

fbshipit-source-id: e25fb4049dcebd715df43bab3ce813ecb5f85abe

Summary: Use IndexError so that a camera object is an iterable

Reviewed By: shapovalov

Differential Revision: D42312021

fbshipit-source-id: 67c417d5f1398e8b30a6944468eda057b4ceb444

Summary: lint fixes

Reviewed By: davidsonic

Differential Revision: D42451530

fbshipit-source-id: 120bdd58fc074a713895df15df4e9efa9ea0a420

Summary: Make GLB files report their own length correctly. They were off by 28.

Reviewed By: davidsonic

Differential Revision: D41838340

fbshipit-source-id: 9cd66e8337c142298d5ae1d7c27e51fd812d5c7b

Summary: Write the amalgamated mesh from the Mesh module to glb. In this version, the json header and the binary data specified by the buffer are merged into glb. The image texture attributes are added.

Reviewed By: bottler

Differential Revision: D41489778

fbshipit-source-id: 3af0e9a8f9e9098e73737a254177802e0fb6bd3c

Summary: Optional[some_configurable] won't autogenerate the enabled flag

Reviewed By: shapovalov

Differential Revision: D41522104

fbshipit-source-id: 555ff6b343faf6f18aad2f92fbb7c341f5e991c6

Summary:
Torch CUDA extension for Marching Cubes
- MC involving 3 steps:
  - 1st forward pass to collect vertices and occupied state for each voxel
  - Compute compactVoxelArray to skip non-empty voxels
  - 2nd pass to genereate interpolated vertex positions and faces by marching through the grid
- In contrast to existing MC:
   - Bind each interpolated vertex with a global edge_id to address floating-point precision
   - Added deduplication process to remove redundant vertices and faces

Benchmarks (ms):

| N / V(^3)      | python          | C++             |   CUDA   | Speedup |
| 2 / 20          |    12176873  |       24338     |     4363   | 2790x/5x|
| 1 / 100          |     -             |    3070511     |   27126   |    113x    |
| 2 / 100          |     -             |    5968934     |   53129   |    112x    |
| 1 / 256          |     -             |  61278092     | 430900   |    142x    |
| 2 / 256          |     -             |125687930     | 856941   |    146x   |

Reviewed By: kjchalup

Differential Revision: D39644248

fbshipit-source-id: d679c0c79d67b98b235d12296f383d760a00042a

Summary:
Rasterize MC was not adapted to heterogeneous bundles.

There are some caveats though:
1) on CO3D, we get up to 18 points per image, which is too few for a reasonable visualisation (see below);
2) rasterising for a batch of 100 is slow.

I also moved the unpacking code close to the bundle to be able to reuse it.

{F789678778}

Reviewed By: bottler, davnov134

Differential Revision: D41008600

fbshipit-source-id: 9f10f1f9f9a174cf8c534b9b9859587d69832b71

Summary: Fix indexing of directions after filtering of points by scaffold.

Reviewed By: shapovalov

Differential Revision: D40853482

fbshipit-source-id: 9cfdb981e97cb82edcd27632c5848537ed2c6837

Summary:
Allows loading of multiple categories.
Multiple categories are provided in a comma-separated list of category names.

Reviewed By: bottler, shapovalov

Differential Revision: D40803297

fbshipit-source-id: 863938be3aa6ffefe9e563aede4a2e9e66aeeaa8

Reviewed By: shapovalov

Differential Revision: D40622304

fbshipit-source-id: 277515a55c46d9b8300058b439526539a7fe00a0

Summary:
According to the profiler trace D40326775, _check_valid_rotation_matrix is slow because of aten::all_close operation and _safe_det_3x3 bottlenecks. Disable the check by default unless environment variable PYTORCH3D_CHECK_ROTATION_MATRICES is set to 1.

Comparison after applying the change:
```
Profiling/Function    get_world_to_view (ms)   Transform_points(ms)    specular(ms)
before                12.751                    18.577                  21.384
after                 4.432 (34.7%)             9.248 (49.8%)           11.507 (53.8%)
```

Profiling trace:
https://pxl.cl/2h687
More details in https://docs.google.com/document/d/1kfhEQfpeQToikr5OH9ZssM39CskxWoJ2p8DO5-t6eWk/edit?usp=sharing

Reviewed By: kjchalup

Differential Revision: D40442503

fbshipit-source-id: 954b58de47de235c9d93af441643c22868b547d0

Summary:
Adds the ability to have different learning rates for different parts of the model. The trainable parts of the implicitron have a new member

       param_groups: dictionary where keys are names of individual parameters,
            or module’s members and values are the parameter group where the
            parameter/member will be sorted to. "self" key is used to denote the
            parameter group at the module level. Possible keys, including the "self" key
            do not have to be defined. By default all parameters are put into "default"
            parameter group and have the learning rate defined in the optimizer,
            it can be overriden at the:
                - module level with “self” key, all the parameters and child
                    module s parameters will be put to that parameter group
                - member level, which is the same as if the `param_groups` in that
                    member has key=“self” and value equal to that parameter group.
                    This is useful if members do not have `param_groups`, for
                    example torch.nn.Linear.
                - parameter level, parameter with the same name as the key
                    will be put to that parameter group.

And in the optimizer factory, parameters and their learning rates are recursively gathered.

Reviewed By: shapovalov

Differential Revision: D40145802

fbshipit-source-id: 631c02b8d79ee1c0eb4c31e6e42dbd3d2882078a

Summary:
Small config system fix. Allows get_default_args to work on an instance which has been created with a dict (instead of a DictConfig) as an args field. E.g.

```
gm = GenericModel(
        raysampler_AdaptiveRaySampler_args={"scene_extent": 4.0}
    )
    OmegaConf.structured(gm1)
```

Reviewed By: shapovalov

Differential Revision: D40341047

fbshipit-source-id: 587d0e8262e271df442a80858949a48e5d6db3df

Summary: Tensorf does relu or softmax after the density grid. This diff adds the ability to replicate that.

Reviewed By: bottler

Differential Revision: D40023228

fbshipit-source-id: 9f19868cd68460af98ab6e61c7f708158c26dc08

Summary:
TensoRF at step 2000 does volume croping and resizing.
At those steps it calculates part of the voxel grid which has density big enough to have objects and resizes the grid to fit that object.
Change is done on 3 levels:
- implicit function subscribes to epochs and at specific epochs finds the bounding box of the object and calls resizing of the color and density voxel grids to fit it
- VoxelGrid module calls cropping of the underlaying voxel grid and resizing to fit previous size it also adjusts its extends and translation to match wanted size
- Each voxel grid has its own way of cropping the underlaying data

Reviewed By: kjchalup

Differential Revision: D39854548

fbshipit-source-id: 5435b6e599aef1eaab980f5421d3369ee4829c50

Summary: Forward method is sped up using the scaffold, a low resolution voxel grid which is used to filter out the points in empty space. These points will be predicted as having 0 density and (0, 0, 0) color. The points which were not evaluated as empty space will be passed through the steps outlined above.

Reviewed By: kjchalup

Differential Revision: D39579671

fbshipit-source-id: 8eab8bb43ef77c2a73557efdb725e99a6c60d415

Summary: Avoid certain hardcoded paths in co3dv2 data

Reviewed By: davnov134

Differential Revision: D40209309

fbshipit-source-id: 0e83a15baa47d5bd07d2d23c6048cb4522c1ccba

Summary:
Torch C++ extension for Marching Cubes

- Add torch C++ extension for marching cubes. Observe a speed up of ~255x-324x speed up (over varying batch sizes and spatial resolutions)

- Add C++ impl in existing unit-tests.

(Note: this ignores all push blocking failures!)

Reviewed By: kjchalup

Differential Revision: D39590638

fbshipit-source-id: e44d2852a24c2c398e5ea9db20f0dfaa1817e457

Summary: Overhaul of marching_cubes_naive for better performance and to avoid relying on unstable hashing. In particular, instead of hashing vertex positions, we index each interpolated vertex with its corresponding edge in the 3d grid.

Reviewed By: kjchalup

Differential Revision: D39419642

fbshipit-source-id: b5fede3525c545d1d374198928dfb216262f0ec0

Summary:
Threaded the for loop:
```
for (int yi = 0; yi < H; ++yi) {...}
```
in function `RasterizeMeshesNaiveCpu()`.
Chunk size is approx equal.

Reviewed By: bottler

Differential Revision: D40063604

fbshipit-source-id: 09150269405538119b0f1b029892179501421e68

Summary:
Changed ray_sampler and metrics to be able to use mixed frame raysampling.

Ray_sampler now has a new member which it passes to the pytorch3d raysampler.
If the raybundle is heterogeneous metrics now samples images by padding xys first. This reduces memory consumption.

Reviewed By: bottler, kjchalup

Differential Revision: D39542221

fbshipit-source-id: a6fec23838d3049ae5c2fd2e1f641c46c7c927e3

Summary: new implicitronRayBundle with added cameraIDs and camera counts. Added to enable a single raybundle inside Implicitron and easier extension in the future. Since RayBundle is named tuple and RayBundleHeterogeneous is dataclass and RayBundleHeterogeneous cannot inherit RayBundle. So if there was no ImplicitronRayBundle every function that uses RayBundle now would have to use Union[RayBundle, RaybundleHeterogeneous] which is confusing and unecessary complicated.

Reviewed By: bottler, kjchalup

Differential Revision: D39262999

fbshipit-source-id: ece160e32f6c88c3977e408e966789bf8307af59

Summary:
Added heterogeneous raysampling to pytorch3d raysampler, different cameras are sampled different number of times.

 It now returns RayBundle if heterogeneous raysampling is off and new RayBundleHeterogeneous (with added fields `camera_ids` and `camera_counts`).  Heterogeneous raysampling is on if `n_rays_total` is not None.

Reviewed By: bottler

Differential Revision: D39542222

fbshipit-source-id: d3d88d822ec7696e856007c088dc36a1cfa8c625

Summary:
This is quite a thin wrapper – not sure we need it. The motivation is that `Transform3d` is not as matrix-centric now, it can be converted to SE(3) logarithm equally easily.

It simplifies things like averaging cameras and getting axis-angle of camera rotation (previously, one would need to call `se3_log_map(cameras.get_world_to_camera_transform().get_matrix())`), now one fewer thing to call / discover.

Reviewed By: bottler

Differential Revision: D39928000

fbshipit-source-id: 85248d5b8af136618f1d08791af5297ea5179d19

Summary:
`get_rotation_to_best_fit_xy` is useful to expose externally, however there was a bug (which we probably did not care about for our use case): it could return a rotation matrix with det(R) == −1.
The diff fixes that, and also makes centroid optional (it can be computed from points).

Reviewed By: bottler

Differential Revision: D39926791

fbshipit-source-id: 5120c7892815b829f3ddcc23e93d4a5ec0ca0013

Summary: Any module can be subscribed to step updates from the training loop. Once the training loop publishes a step the voxel grid changes its dimensions. During the construction of VoxelGridModule and its parameters it does not know which is the resolution that will be loaded from checkpoint, so before the checkpoint loading a hook runs which changes the VoxelGridModule's parameters to match shapes of the loaded checkpoint.

Reviewed By: bottler

Differential Revision: D39026775

fbshipit-source-id: 0d359ea5c8d2eda11d773d79c7513c83585d5f17

Summary:
User reported that cloned cameras fail to save. The error with latest PyTorch is

```
pickle.PicklingError: Can't pickle ~T_destination: attribute lookup T_destination on torch.nn.modules.module failed
```

This fixes it.

Reviewed By: btgraham

Differential Revision: D39692258

fbshipit-source-id: 75bbf3b8dfa0023dc28bf7d4cc253ca96e46a64d

Summary: Adding a checkerboard mesh utility to Pytorch3d.

Reviewed By: bottler

Differential Revision: D39718916

fbshipit-source-id: d43cd30e566b5db068bae6eed0388057634428c8

Summary:
We need to make packing/unpacking in 2 places for mixed frame raysampling (metrics and raysampler) but those tensors that need to be unpacked/packed have more than two dimensions.
I could have reshaped and stored dimensions but this seems to just complicate code there with something which packed_to_padded should support.
I could have made a separate function for implicitron but it would confusing to have two different padded_to_packed functions inside pytorch3d codebase one of which does packing for (b, max) and (b, max, f) and the other for (b, max, …)

Reviewed By: bottler

Differential Revision: D39729026

fbshipit-source-id: 2bdebf290dcc6c316b7fe1aeee49bbb5255e508c