GitLab

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:
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: 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: 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: Simple wrapper around voxel grids to make them a module

Reviewed By: bottler

Differential Revision: D38829762

fbshipit-source-id: dfee85088fa3c65e396cc7d3bf7ebaaffaadb646

Summary: Added voxel grid classes from TensoRF, both in their factorized (CP and VM) and full form.

Reviewed By: bottler

Differential Revision: D38465419

fbshipit-source-id: 8b306338af58dc50ef47a682616022a0512c0047