GitLab

Summary:
## Context

Bins are used in mipnerf to allow to manipulate easily intervals. For example, by doing the following, `bins[..., :-1]` you will obtain all the left coordinates of your intervals, while doing `bins[..., 1:]` is equals to the right coordinates of your intervals.

We introduce here the support of bins like in MipNerf implementation.

## RayPointRefiner

Small changes have been made to modify RayPointRefiner.
- If bins is None

```
mids = torch.lerp(ray_bundle.lengths[..., 1:], ray_bundle.lengths[…, :-1], 0.5)
z_samples = sample_pdf(
		mids, # [..., npt]
		weights[..., 1:-1], # [..., npt - 1]
               ….
            )
```

- If bins is not None
In the MipNerf implementation the sampling is done on all the bins. It allows us to use the full weights tensor without slashing it.

```
z_samples = sample_pdf(
		ray_bundle.bins, # [..., npt + 1]
		weights, # [..., npt]
               ...
            )
```

## RayMarcher

Add a ray_deltas optional argument. If None, keep the same deltas computation from ray_lengths.

Reviewed By: shapovalov

Differential Revision: D46389092

fbshipit-source-id: d4f1963310065bd31c1c7fac1adfe11cbeaba606

Summary:
Add blurpool has defined in [MIP-NeRF](https://arxiv.org/abs/2103.13415).
It has been added has an option for RayPointRefiner.

Reviewed By: shapovalov

Differential Revision: D46356189

fbshipit-source-id: ad841bad86d2b591a68e1cb885d4f781cf26c111

Summary: Add a new implicit module Integral Position Encoding based on [MIP-NeRF](https://arxiv.org/abs/2103.13415).

Reviewed By: shapovalov

Differential Revision: D46352730

fbshipit-source-id: c6a56134c975d80052b3a11f5e92fd7d95cbff1e

Summary:
Introduce methods to approximate the radii of conical frustums along rays as described in [MipNerf](https://arxiv.org/abs/2103.13415):

- Two new attributes are added to ImplicitronRayBundle: bins and radii. Bins is of size n_pts_per_ray + 1. It allows us to manipulate easily and n_pts_per_ray intervals. For example we need the intervals coordinates in the radii computation for \(t_{\mu}, t_{\delta}\). Radii are used to store the radii of the conical frustums.

- Add 3 new methods to compute the radii:
   - approximate_conical_frustum_as_gaussians: It computes the mean along the ray direction, the variance of the
      conical frustum  with respect to t and variance of the conical frustum with respect to its radius. This
      implementation follows the stable computation defined in the paper.
   - compute_3d_diagonal_covariance_gaussian: Will leverage the two previously computed variances to find the
     diagonal covariance of the Gaussian.
   - conical_frustum_to_gaussian: Mix everything together to compute the means and the diagonal covariances along
     the ray of the Gaussians.

- In AbstractMaskRaySampler, introduces the attribute `cast_ray_bundle_as_cone`. If False it won't change the previous behaviour of the RaySampler. However if True, the samplers will sample `n_pts_per_ray +1` instead of `n_pts_per_ray`. This points are then used to set the bins attribute of ImplicitronRayBundle. The support of HeterogeneousRayBundle has not been added since the current code does not allow it. A safeguard has been added to avoid a silent bug in the future.

Reviewed By: shapovalov

Differential Revision: D45269190

fbshipit-source-id: bf22fad12d71d55392f054e3f680013aa0d59b78

Summary: Make test work in isolation, and when run internally make it not try the sqlalchemy files.

Reviewed By: shapovalov

Differential Revision: D46352513

fbshipit-source-id: 7417a25d7a5347d937631c9f56ae4e3242dd622e

Summary: Making it easier for the clients to use these datasets.

Reviewed By: bottler

Differential Revision: D46727179

fbshipit-source-id: cf619aee4c4c0222a74b30ea590cf37f08f014cc

Summary:
Adds stratified sampling of sequences within categories applied after category / sequence filters but before the num sequence limit.
It respects the insertion order into the sequence_annots table, i.e. takes top N sequences within each category.

Reviewed By: bottler

Differential Revision: D46724002

fbshipit-source-id: 597cb2a795c3f3bc07f838fc51b4e95a4f981ad3

Summary: Fine implicit function was called before the coarse implicit function.

Reviewed By: shapovalov

Differential Revision: D46224224

fbshipit-source-id: 6b1cc00cc823d3ea7a5b42774c9ec3b73a69edb5

Summary:
1. We may need to store arrays of unknown shape in the database. It implements and tests serialisation.

2. Previously, when an inexisting metadata file was passed to SqlIndexDataset, it would try to open it and create an empty file, then crash. We now open the file in a read-only mode, so the error message is more intuitive. Note that the implementation is SQLite specific.

Reviewed By: bottler

Differential Revision: D46047857

fbshipit-source-id: 3064ae4f8122b4fc24ad3d6ab696572ebe8d0c26

Summary: I don't know why RE tests sometimes fail here, but maybe it's a race condition. If that's right, this should fix it.

Reviewed By: shapovalov

Differential Revision: D46020054

fbshipit-source-id: 20b746b09ad9bd77c2601ac681047ccc6cc27ed9

Summary:
This is mostly a refactoring diff to reduce friction in extending the frame data.

Slight functional changes: dataset getitem now accepts (seq_name, frame_number_as_singleton_tensor) as a non-advertised feature. Otherwise this code crashes:
```
item = dataset[0]
dataset[item.sequence_name, item.frame_number]
```

Reviewed By: bottler

Differential Revision: D45780175

fbshipit-source-id: 75b8e8d3dabed954a804310abdbd8ab44a8dea29

Summary: Import generic path; avoiding incorrect path patching.

Reviewed By: bottler

Differential Revision: D45573976

fbshipit-source-id: e6ff4d759deb936e3b636defa1e0851fb0127b46

Summary:
I forgot to include these tests to D45086611 when transferring code from pixar_replay repo.

They test the new ORM types used in SQL dataset and are SQL Alchemy 2.0 specific.

An important test for extending types is a proof of concept for generality of SQL Dataset. The idea is to extend FrameAnnotation and FrameData in parallel.

Reviewed By: bottler

Differential Revision: D45529284

fbshipit-source-id: 2a634e518f580c312602107c85fc320db43abcf5

Summary:
Moving SQL dataset to PyTorch3D. It has been extensively tested in pixar_replay.

It requires SQLAlchemy 2.0, which is not supported in fbcode. So I exclude the sources and tests that depend on it from buck TARGETS.

Reviewed By: bottler

Differential Revision: D45086611

fbshipit-source-id: 0285f03e5824c0478c70ad13731525bb5ec7deef

Summary:
We currently support caching bounding boxes in MaskAnnotation. If present, they are not re-computed from the mask. However, the masks need to be loaded for the bbox to be set.

This diff fixes that. Even if load_masks / load_blobs are unset, the bounding box can be picked up from the metadata.

Reviewed By: bottler

Differential Revision: D45144918

fbshipit-source-id: 8a2e2c115e96070b6fcdc29cbe57e1cee606ddcd

Summary: The code does not crash if depth map/mask are not given.

Reviewed By: bottler

Differential Revision: D45082985

fbshipit-source-id: 3610d8beb4ac897fbbe52f56a6dd012a6365b89b

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

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

Reviewed By: shapovalov

Differential Revision: D41522104

fbshipit-source-id: 555ff6b343faf6f18aad2f92fbb7c341f5e991c6

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:
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:
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:
`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: Call expand_args_field when instantiating an object.

Reviewed By: shapovalov

Differential Revision: D39541931

fbshipit-source-id: de8e1038927ff0112463394412d5d8c26c4a1e17

Summary: Add the ability to process arbitrary point shapes `[n_grids, ..., 3]` instead of  only `[n_grids, n_points, 3]`.

Reviewed By: bottler

Differential Revision: D39574373

fbshipit-source-id: 0a9ecafe9ea58cd8f909644de43a1185ecf934f4

Summary:
- indicate location of OmegaConf.structured failures
- split the data gathering from enable_get_default_args to ease experimenting with it.
- comment fixes.
- nicer error when a_class_type has weird type.

Reviewed By: kjchalup

Differential Revision: D39434447

fbshipit-source-id: b80c7941547ca450e848038ef5be95b7ebbe8f3e