Summary: Fixes a bug which would crash render_flyaround anytime visualize_preds_keys is adjusted
Reviewed By: shapovalov
Differential Revision: D41124462
fbshipit-source-id: 127045a91a055909f8bd56c8af81afac02c00f60
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: To be consistent with CUDA hashing, the diff replaces boost hasher with a simplified hasher for storing unique global edge_ids.
Reviewed By: kjchalup
Differential Revision: D41140382
fbshipit-source-id: 2ce598e5edcf6369fe13bd15d1f5e014b252027b
Summary: Autogenerate docs for the renderer too. This will be helpful but make a slightly ugly TOC
Reviewed By: kjchalup
Differential Revision: D40977315
fbshipit-source-id: 10831de3ced68080cb5671c5dc31d4da8500f761
Summary:
Every time I try to run code, I get this warning:
```
warnings.warn("Can't import pucuda.gl, not importing MeshRasterizerOpenGL.")
```
Of course, `pucuda` is a typo of `pycuda`.
This PR fixes the typo
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1379
Reviewed By: kjchalup
Differential Revision: D41295562
Pulled By: bottler
fbshipit-source-id: 2bfa2a2dbe20a5347861d36fbff5094994c1253d
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:
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:
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: 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
Summary: Try to document implicitron. Most of this is autogenerated.
Reviewed By: shapovalov
Differential Revision: D40623742
fbshipit-source-id: 453508277903b7d987b1703656ba1ee09bc2c570
Summary: The bug lead to non-coinciding origins of the rays emitted from perspective cameras when unit_directions=True
Reviewed By: bottler
Differential Revision: D40865610
fbshipit-source-id: 398598e9e919b53e6bea179f0400e735bbb5b625
Summary: Some things fail if a parameter is not wraped; in particular, it prevented other tensors moving to GPU.
Reviewed By: bottler
Differential Revision: D40819932
fbshipit-source-id: a23b38ceacd7f0dc131cb0355fef1178e3e2f7fd
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:
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: Keep the cause of hydra errors visible in some more cases.
Reviewed By: shapovalov
Differential Revision: D40516202
fbshipit-source-id: 8d214be5cc808a37738add77cc305fe099788546
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:
Added initialization configuration for the last layer of the MLP decoding function. You can now set:
- last activation function (tensorf uses sigmoid)
- last bias init (tensorf uses 0, because of sigmoid ofc)
- option to use xavier initialization (we use relu so this should not be set)
Reviewed By: davnov134
Differential Revision: D40304981
fbshipit-source-id: ec398eb2235164ae85cb7c09b9660e843490ea04
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: More helpful errors when the output channels aren't 1 for density and 3 for color
Reviewed By: shapovalov
Differential Revision: D40341088
fbshipit-source-id: 6074bf7fefe11c8e60fee4db2760b776419bcfee
Summary: Couldn't build p3d on devfair because C++17 is unsupported. Two structured bindings sneaked in.
Reviewed By: bottler
Differential Revision: D40280967
fbshipit-source-id: 9627f3f9f76247a6cefbeac067fdead67c6f4e14
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:
avoid creating a numpy array of random things just to split it: this can now generate a warning e.g. if the list contains lists of varying lengths. There might also be a performance win here, and we could do more of the same if we care about that.
(The vanilla way to avoid the new warning is to replace `np.split(a,` with `np.split(np.array(a, dtype=object), ` btw.)
Reviewed By: shapovalov
Differential Revision: D40209308
fbshipit-source-id: daae33a23ceb444e8e7241f72ce1525593e2f239
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: Avoids use of `torch.cat` operation when rendering a volume by instead issuing multiple calls to `torch.nn.functional.grid_sample`. Density and color tensors can be large.
Reviewed By: bottler
Differential Revision: D40072399
fbshipit-source-id: eb4cd34f6171d54972bbf2877065f973db497de0
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
