Summary: The diff support colors in cubify for align = "center"
Reviewed By: bottler
Differential Revision: D53777011
fbshipit-source-id: ccb2bd1e3d89be3d1ac943eff08f40e50b0540d9
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:
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:
We need to make packing/unpacking in 2 places for mixed frame raysampling (metrics and raysampler) but those tensors that need to be unpacked/packed have more than two dimensions.
I could have reshaped and stored dimensions but this seems to just complicate code there with something which packed_to_padded should support.
I could have made a separate function for implicitron but it would confusing to have two different padded_to_packed functions inside pytorch3d codebase one of which does packing for (b, max) and (b, max, f) and the other for (b, max, …)
Reviewed By: bottler
Differential Revision: D39729026
fbshipit-source-id: 2bdebf290dcc6c316b7fe1aeee49bbb5255e508c
Summary:
One of the docstrings is a disaster see https://pytorch3d.readthedocs.io/en/latest/modules/ops.html
Also some minor fixes I encountered when browsing the code
Reviewed By: bottler
Differential Revision: D38581595
fbshipit-source-id: 3b6ca97788af380a44df9144a6a4cac782c6eab8
Summary:
Applies new import merging and sorting from µsort v1.0.
When merging imports, µsort will make a best-effort to move associated
comments to match merged elements, but there are known limitations due to
the diynamic nature of Python and developer tooling. These changes should
not produce any dangerous runtime changes, but may require touch-ups to
satisfy linters and other tooling.
Note that µsort uses case-insensitive, lexicographical sorting, which
results in a different ordering compared to isort. This provides a more
consistent sorting order, matching the case-insensitive order used when
sorting import statements by module name, and ensures that "frog", "FROG",
and "Frog" always sort next to each other.
For details on µsort's sorting and merging semantics, see the user guide:
https://usort.readthedocs.io/en/stable/guide.html#sorting
Reviewed By: lisroach
Differential Revision: D36402260
fbshipit-source-id: 7cb52f09b740ccc580e61e6d1787d27381a8ce00
Summary:
Applies the black-fbsource codemod with the new build of pyfmt.
paintitblack
Reviewed By: lisroach
Differential Revision: D36324783
fbshipit-source-id: 280c09e88257e5e569ab729691165d8dedd767bc
Summary:
Applies new import merging and sorting from µsort v1.0.
When merging imports, µsort will make a best-effort to move associated
comments to match merged elements, but there are known limitations due to
the diynamic nature of Python and developer tooling. These changes should
not produce any dangerous runtime changes, but may require touch-ups to
satisfy linters and other tooling.
Note that µsort uses case-insensitive, lexicographical sorting, which
results in a different ordering compared to isort. This provides a more
consistent sorting order, matching the case-insensitive order used when
sorting import statements by module name, and ensures that "frog", "FROG",
and "Frog" always sort next to each other.
For details on µsort's sorting and merging semantics, see the user guide:
https://usort.readthedocs.io/en/stable/guide.html#sorting
Reviewed By: bottler
Differential Revision: D35553814
fbshipit-source-id: be49bdb6a4c25264ff8d4db3a601f18736d17be1
Summary: D35513897 (4b94649f7b) was a pyre infer job which got some things wrong. Correct by adding the correct types, so these things shouldn't need worrying about again.
Reviewed By: patricklabatut
Differential Revision: D35546144
fbshipit-source-id: 89f6ea2b67be27aa0b0b14afff4347cccf23feb7
Summary: Add option to not rescale the features, giving more control. https://github.com/facebookresearch/pytorch3d/issues/1137
Reviewed By: nikhilaravi
Differential Revision: D35219577
fbshipit-source-id: cbbb643b91b71bc908cedc6dac0f63f6d1355c85
Summary:
Added L1 norm for KNN and chamfer op
* The norm is now specified with a variable `norm` which can only be 1 or 2
Reviewed By: bottler
Differential Revision: D35419637
fbshipit-source-id: 77813fec650b30c28342af90d5ed02c89133e136
Summary:
Error Reproduction:
python=3.8.12
pytorch=1.9.1
pytorch3d=0.6.1
cudatoolkit=11.1.74
test.py:
```python
import torch
from pytorch3d.ops import cubify
voxels = torch.Tensor([[[[0,1], [0,0]], [[0,1], [0,0]]]]).float()
meshes = cubify(voxels, 0.5, device="cpu")
```
The error appears when `device="cpu"` and `pytorch=1.9.1` (works fine with pytorch=1.10.2)
Error message:
```console
/home/kyle/anaconda3/envs/adapt-net/lib/python3.8/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.
To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at /opt/conda/conda-bld/pytorch_1631630839582/work/aten/src/ATen/native/BinaryOps.cpp:467.)
return torch.floor_divide(self, other)
Traceback (most recent call last):
File "test.py", line 5, in <module>
meshes = cubify(voxels, 0.5, device="cpu")
File "/home/kyle/anaconda3/envs/adapt-net/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context
return func(*args, **kwargs)
File "/home/kyle/Desktop/pytorch3d/pytorch3d/ops/cubify.py", line 227, in cubify
idleverts.scatter_(0, grid_faces.flatten(), 0)
RuntimeError: Expected index [60] to be smaller than self [27] apart from dimension 0 and to be smaller size than src [27]
```
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1067
Reviewed By: nikhilaravi
Differential Revision: D34893567
Pulled By: bottler
fbshipit-source-id: aa95980f7319302044141f7821ef48129cfa37a6
Summary: Use the newer eigh to avoid deprecation warnings in newer pytorch.
Reviewed By: patricklabatut
Differential Revision: D34375784
fbshipit-source-id: 40efe0d33fdfa071fba80fc97ed008cbfd2ef249
Summary: D33970393 (e9fb6c27e3) ran an inference to add some typing. Remove some where it was a bit too confident. (Also fix some pyre errors in plotly_vis caused by new mismatch.)
Reviewed By: patricklabatut
Differential Revision: D34004689
fbshipit-source-id: 430182b0ff0b91be542a3120da6d6b1d2b247c59
Summary: Update all FB license strings to the new format.
Reviewed By: patricklabatut
Differential Revision: D33403538
fbshipit-source-id: 97a4596c5c888f3c54f44456dc07e718a387a02c
Summary:
A small numerical fix for IoU for 3D boxes, fixes GH #992
* Adds a check for boxes with zero side areas (invalid boxes)
* Fixes numerical issue when two boxes have coplanar sides
Reviewed By: nikhilaravi
Differential Revision: D33195691
fbshipit-source-id: 8a34b4d1f1e5ec2edb6d54143930da44bdde0906
Summary: Fix#873, that grid_sizes defaults to the wrong dtype in points2volumes code, and mask doesn't have a proper default.
Reviewed By: nikhilaravi
Differential Revision: D31503545
fbshipit-source-id: fa32a1a6074fc7ac7bdb362edfb5e5839866a472
Summary: Make eps=1e-4 by default for coplanar check and also enable it to be set by the user in call to `box3d_overlap`.
Reviewed By: gkioxari
Differential Revision: D31596836
fbshipit-source-id: b57fe603fd136cfa58fddf836922706d44fe894e
Summary: Single C++ function for the core of points2vols, not used anywhere yet. Added ability to control align_corners and the weight of each point, which may be useful later.
Reviewed By: nikhilaravi
Differential Revision: D29548607
fbshipit-source-id: a5cda7ec2c14836624e7dfe744c4bbb3f3d3dfe2
Summary: C++ Implementation of algorithm to compute 3D bounding boxes for batches of bboxes of shape (N, 8, 3) and (M, 8, 3).
Reviewed By: gkioxari
Differential Revision: D30905190
fbshipit-source-id: 02e2cf025cd4fa3ff706ce5cf9b82c0fb5443f96
Summary:
I have implemented an exact solution for 3D IoU of oriented 3D boxes.
This file includes:
* box3d_overlap: which computes the exact IoU of box1 and box2
* box3d_overlap_sampling: which computes an approximate IoU of box1 and box2 by sampling points within the boxes
Note that both implementations currently do not support batching.
Our exact IoU implementation is based on the fact that the intersecting shape of the two 3D boxes will be formed by segments of the surface of the boxes. Our algorithm computes these segments by reasoning whether triangles of one box are within the second box and vice versa. We deal with intersecting triangles by clipping them.
Reviewed By: gkioxari
Differential Revision: D30667497
fbshipit-source-id: 2f747f410f90b7f854eeaf3036794bc3ac982917
Summary:
CUDA implementation of farthest point sampling algorithm.
## Visual comparison
Compared to random sampling, farthest point sampling gives better coverage of the shape.
{F658631262}
## Reduction
Parallelized block reduction to find the max value at each iteration happens as follows:
1. First split the points into two equal sized parts (e.g. for a list with 8 values):
`[20, 27, 6, 8 | 11, 10, 2, 33]`
2. Use half of the thread (4 threads) to compare pairs of elements from each half (e.g elements [0, 4], [1, 5] etc) and store the result in the first half of the list:
`[20, 27, 6, 33 | 11, 10, 2, 33]`
Now we no longer care about the second part but again divide the first part into two
`[20, 27 | 6, 33| -, -, -, -]`
Now we can use 2 threads to compare the 4 elements
4. Finally we have gotten down to a single pair
`[20 | 33 | -, - | -, -, -, -]`
Use 1 thread to compare the remaining two elements
5. The max will now be at thread id = 0
`[33 | - | -, - | -, -, -, -]`
The reduction will give the farthest point for the selected batch index at this iteration.
Reviewed By: bottler, jcjohnson
Differential Revision: D30401803
fbshipit-source-id: 525bd5ae27c4b13b501812cfe62306bb003827d2
