GitLab

Reviewed By: bottler

Differential Revision: D60992234

fbshipit-source-id: 899db6ed590ef966ff651c11027819e59b8401a3

Reviewed By: inseokhwang

Differential Revision: D54438157

fbshipit-source-id: a6acfe146ed29fff82123b5e458906d4b4cee6a2

Differential Revision: D51645956

fbshipit-source-id: 1ae7279efa0a27bb9bc5255527bafebb84fdafd0

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: simple

Reviewed By: shapovalov

Differential Revision: D46438865

fbshipit-source-id: 0f41cb3ddd7e7aca4513267d33299531f7e8d373

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

Reviewed By: bottler

Differential Revision: D46727179

fbshipit-source-id: cf619aee4c4c0222a74b30ea590cf37f08f014cc

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: We don't want to use print directly in stats.print() method. Instead this method will return the output string to the caller.

Reviewed By: shapovalov

Differential Revision: D45356240

fbshipit-source-id: 2cabe3cdfb9206bf09aa7b3cdd2263148a5ba145

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: If a configurable class inherits torch.nn.Module and is instantiated, automatically call `torch.nn.Module.__init__` on it before doing anything else.

Reviewed By: shapovalov

Differential Revision: D42760349

fbshipit-source-id: 409894911a4252b7987e1fd218ee9ecefbec8e62

Summary: We don’t see much value in reporting metrics by camera difficulty while supporting that in new datasets is quite painful, hence deprecating training cameras in the data API and ignoring in evaluation.

Reviewed By: bottler

Differential Revision: D42678879

fbshipit-source-id: aad511f6cb2ca82745f31c19594e1d80594b61d7

Summary: Allows to send kwargs to render_flyaround from visualize_reconstruction

Reviewed By: bottler, shapovalov

Differential Revision: D41157683

fbshipit-source-id: 74d8d7de4e991a31b14e72d76de5efdb8ab4b2c5

Summary: Allow choosing the device and the distance

Reviewed By: shapovalov

Differential Revision: D42451605

fbshipit-source-id: 214f02d09da94eb127b3cc308d5bae800dc7b9e2

Summary: followup D41534315 (https://github.com/facebookresearch/pytorch3d/commit/5add065f8a7d4dc29ab28de4f9548d44eadc2aca)

Reviewed By: shapovalov

Differential Revision: D41842250

fbshipit-source-id: 54413e30880717f083fdc7acbce2eb3e646ffb94

Summary:
Running:
- clearly points users to experiment.py/visualize_reconstruction.py
Reproducing:
- Adds NeRF training on Blender
- Adds CO3Dv2 configs

Reviewed By: bottler

Differential Revision: D41534315

fbshipit-source-id: e85f5f1eafed8c35c9e91d748a04f238509cf8ec

Summary: <See title>

Reviewed By: bottler

Differential Revision: D41534524

fbshipit-source-id: 9c39198b9b8d5fc95f857b03ad39bfe0bd720cbb

Summary:
Addresses the following issue:
https://github.com/facebookresearch/pytorch3d/issues/1345#issuecomment-1272881244

I.e., when installed from conda, `pytorch3d_implicitron_visualizer` crashes since it invokes `main()` while `main` requires a single positional arg `argv`.

Reviewed By: shapovalov

Differential Revision: D41533497

fbshipit-source-id: e53a923eb8b2f0f9c0e92e9c0866d9cb310c4799

Summary:
Enum fields cause the following to crash since they are loaded as strings:
```
config = OmegaConf.load(autodumped_cfg_file)
Experiment(**config)
```

It would be good to come up with the general solution but for now just fixing the visualisation script.

Reviewed By: bottler

Differential Revision: D41140426

fbshipit-source-id: 71c1c6b1fffe3b5ab1ca0114cfa3f0d81160278f

Summary:
Allow a module's param_group member to specify overrides to the param groups of its members or their members.
Also logging for param group assignments.

This allows defining `params.basis_matrix` in the param_groups of a voxel_grid.

Reviewed By: shapovalov

Differential Revision: D41080667

fbshipit-source-id: 49f3b0e5b36e496f78701db0699cbb8a7e20c51e

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

Summary: see title

Reviewed By: shapovalov

Differential Revision: D40803670

fbshipit-source-id: 211189167837af577d6502a698e2f3fb3aec3e30

Summary: Yaml bool case fix

Reviewed By: shapovalov

Differential Revision: D40623031

fbshipit-source-id: 29b2fba171c2cbebfa03834e38b614d07275c997

Reviewed By: shapovalov

Differential Revision: D40622304

fbshipit-source-id: 277515a55c46d9b8300058b439526539a7fe00a0

Summary: Add option to flat pad the last delta. Might to help when training on rgb only.

Reviewed By: shapovalov

Differential Revision: D40587475

fbshipit-source-id: c763fa38948600ea532c730538dc4ff29d2c3e0a

Summary: Make Implicitron run without visdom installed.

Reviewed By: shapovalov

Differential Revision: D40587974

fbshipit-source-id: dc319596c7a4d10a4c54c556dabc89ad9d25c2fb

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: Loads the whole dataset and moves it to the device and sends it to for sampling to enable full dataset heterogeneous raysampling.

Reviewed By: bottler

Differential Revision: D39263009

fbshipit-source-id: c527537dfc5f50116849656c9e171e868f6845b1

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: Allow using the new `foreach` option on optimizers.

Reviewed By: shapovalov

Differential Revision: D39694843

fbshipit-source-id: 97109c245b669bc6edff0f246893f95b7ae71f90

Summary: Various fixes to get visualize_reconstruction running, and an interactive test for it.

Reviewed By: kjchalup

Differential Revision: D39286691

fbshipit-source-id: 88735034cc01736b24735bcb024577e6ab7ed336

Summary: Workaround for oddity with new hydra.

Reviewed By: davnov134

Differential Revision: D39280639

fbshipit-source-id: 76e91947f633589945446db93cf2dbc259642f8a

Summary:
Move the flyaround rendering function into core implicitron.
The unblocks an example in the facebookresearch/co3d repo.

Reviewed By: bottler

Differential Revision: D39257801

fbshipit-source-id: 6841a88a43d4aa364dd86ba83ca2d4c3cf0435a4

Reviewed By: kjchalup

Differential Revision: D39198333

fbshipit-source-id: 3f4ebcf625215f21d165073837578ff69b05f72d

Summary:
Adds yaml configs to train selected methods on CO3Dv2.

Few more updates:
1) moved some fields to base classes so that we can check is_multisequence in experiment.py
2) skip loading all train cameras for multisequence datasets, without this, co3d-fewview is untrainable
3) fix bug in json index dataset provider v2

Reviewed By: kjchalup

Differential Revision: D38952755

fbshipit-source-id: 3edac6fc8e20775aa70400bd73a0e6d52b091e0c

Summary: Fixes the blender synthetic configs.

Reviewed By: kjchalup

Differential Revision: D38786095

fbshipit-source-id: 6d0784ced41a3f2904f074221108cdb56bd20e7f

Summary:
generic_model_args no longer exists. Update some references to it, mostly in doc.

This fixes the testing of all the yaml files in test_forward pass.

Reviewed By: shapovalov

Differential Revision: D38789202

fbshipit-source-id: f11417efe772d7f86368b3598aa66c52b1309dbf

Summary: Adds additional source views to the eval batches for evaluating many-view models on CO3D Challenge

Reviewed By: bottler

Differential Revision: D38705904

fbshipit-source-id: cf7d00dc7db926fbd1656dd97a729674e9ff5adb

Summary: add link in main readme

Reviewed By: kjchalup

Differential Revision: D38560053

fbshipit-source-id: 0814febb67d0580394cfa2664e49e31ff7254bd4