GitLab

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: Single directional chamfer distance and option to use non-absolute cosine similarity

Reviewed By: bottler

Differential Revision: D46593980

fbshipit-source-id: b2e591706a0cdde1c2d361614cecebb84a581433

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:
Although we can load per-vertex normals in `load_obj`, saving per-vertex normals is not supported in `save_obj`.

This patch fixes this by allowing passing per-vertex normal data in `save_obj`:
``` python
def save_obj(
    f: PathOrStr,
    verts,
    faces,
    decimal_places: Optional[int] = None,
    path_manager: Optional[PathManager] = None,
    *,
    verts_normals: Optional[torch.Tensor] = None,
    faces_normals: Optional[torch.Tensor] = None,
    verts_uvs: Optional[torch.Tensor] = None,
    faces_uvs: Optional[torch.Tensor] = None,
    texture_map: Optional[torch.Tensor] = None,
) -> None:
    """
    Save a mesh to an .obj file.

    Args:
        f: File (str or path) to which the mesh should be written.
        verts: FloatTensor of shape (V, 3) giving vertex coordinates.
        faces: LongTensor of shape (F, 3) giving faces.
        decimal_places: Number of decimal places for saving.
        path_manager: Optional PathManager for interpreting f if
            it is a str.
        verts_normals: FloatTensor of shape (V, 3) giving the normal per vertex.
        faces_normals: LongTensor of shape (F, 3) giving the index into verts_normals
            for each vertex in the face.
        verts_uvs: FloatTensor of shape (V, 2) giving the uv coordinate per vertex.
        faces_uvs: LongTensor of shape (F, 3) giving the index into verts_uvs for
            each vertex in the face.
        texture_map: FloatTensor of shape (H, W, 3) representing the texture map
            for the mesh which will be saved as an image. The values are expected
            to be in the range [0, 1],
    """
```

Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1511

Reviewed By: shapovalov

Differential Revision: D45086045

Pulled By: bottler

fbshipit-source-id: 666efb0d2c302df6cf9f2f6601d83a07856bf32f

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:
Added a suit of functions and code additions to experimental_gltf_io.py file to enable saving Meshes in TexturesVertex format into .glb file.
Also added a test to tets_io_gltf.py to check the functionality with the test described in Test Plane.

Reviewed By: bottler

Differential Revision: D44969144

fbshipit-source-id: 9ce815a1584b510442fa36cc4dbc8d41cc3786d5

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

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