Summary: these are failing in ci
Reviewed By: das-intensity
Differential Revision: D62594666
fbshipit-source-id: 5e3a7441be2978803dc2d3e361365e0fffa7ad3b
Summary:
Make the negative index actually not an error
fixes https://github.com/facebookresearch/pytorch3d/issues/1368
Reviewed By: das-intensity
Differential Revision: D62177991
fbshipit-source-id: e5ed433bde1f54251c4d4b6db073c029cbe87343
Summary:
Apparently pytorch 2.4 is now supported as per [this closed issue](https://github.com/facebookresearch/pytorch3d/issues/1863).
Added the `2.4.0` & `2.4.1` versions to `regenerate.py` then ran that as per the `README_fb.md` which generated `config.yml` changes.
Reviewed By: bottler
Differential Revision: D62517831
fbshipit-source-id: 002e276dfe2fa078136ff2f6c747d937abbadd1a
Summary:
X-link: https://github.com/pytorch/pytorch/pull/133343
X-link: https://github.com/fairinternal/pytorch3d/pull/45
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1851
Enables pytorch3d to build on AMD. An important part of enabling this was not compiling the Pulsar backend when the target is AMD. There are simply too many kernel incompatibilites to make it work (I tried haha). Fortunately, it doesnt seem like most modern applications of pytorch3d rely on Pulsar. We should be able to unlock most of pytorch3d's goodness on AMD without it.
Reviewed By: bottler, houseroad
Differential Revision: D61171993
fbshipit-source-id: fd4aee378a3568b22676c5bf2b727c135ff710af
Summary: To avoid the installation instructions for PyTorch3D becoming out-of-date, instead of specifying certain Python versions, update to just `Python`. Reader will understand it has to be a Python version compatible with GitHub.
Reviewed By: bottler
Differential Revision: D60919848
fbshipit-source-id: 5e974970a0db3d3d32fae44e5dd30cbc1ce237a9
Summary:
* Adds a "max" option for the point_reduction input to the
chamfer_distance function.
* When combining the x and y directions, maxes the losses instead
of summing them when point_reduction="max".
* Moves batch reduction to happen after the directions are
combined.
* Adds test_chamfer_point_reduction_max and
test_single_directional_chamfer_point_reduction_max tests.
Fixes https://github.com/facebookresearch/pytorch3d/issues/1838
Reviewed By: bottler
Differential Revision: D60614661
fbshipit-source-id: 7879816acfda03e945bada951b931d2c522756eb
Summary: This diff is fixing a backwards compatibility issue in PyTorch3D's dataset API. The code ensures that the `crop_bbox_xywh` attribute is set when box_crop flag is on. This is an implementation detail that people should not really use, however some people depend on this behaviour.
Reviewed By: bottler
Differential Revision: D59777449
fbshipit-source-id: b875e9eb909038b8629ccdade87661bb2c39d529
Summary: This is not actually needed and is causing a conda-forge confusion to do with python_abi - which needs users to have `-c conda-forge` when they install pytorch3d.
Reviewed By: patricklabatut
Differential Revision: D59587930
fbshipit-source-id: 961ae13a62e1b2b2ce6d8781db38bd97eca69e65
Summary: Problems with timeouts on old builds.
Reviewed By: MichaelRamamonjisoa
Differential Revision: D58819435
fbshipit-source-id: e1976534a102ad3841f3b297c772e916aeea12cb
Summary:
Currently, it is not possible to access a sub-transform using an indexer for all 3d transforms inheriting the `Transforms3d` class.
For instance:
```python
from pytorch3d import transforms
N = 10
r = transforms.random_rotations(N)
T = transforms.Transform3d().rotate(R=r)
R = transforms.Rotate(r)
x = T[0] # ok
x = R[0] # TypeError: __init__() got an unexpected keyword argument 'matrix'
```
This is because all these classes (namely `Rotate`, `Translate`, `Scale`, `RotateAxisAngle`) inherit the `__getitem__()` method from `Transform3d` which has the [following code on line 201](https://github.com/facebookresearch/pytorch3d/blob/main/pytorch3d/transforms/transform3d.py#L201):
```python
return self.__class__(matrix=self.get_matrix()[index])
```
The four classes inheriting `Transform3d` are not initialized through a matrix argument, hence they error.
I propose to modify the `__getitem__()` method of the `Transform3d` class to fix this behavior. The least invasive way to do it I can think of consists of creating an empty instance of the current class, then setting the `_matrix` attribute manually. Thus, instead of
```python
return self.__class__(matrix=self.get_matrix()[index])
```
I propose to do:
```python
instance = self.__class__.__new__(self.__class__)
instance._matrix = self.get_matrix()[index]
return instance
```
As far as I can tell, this modification occurs no modification whatsoever for the user, except for the ability to index all 3d transforms.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1801
Reviewed By: MichaelRamamonjisoa
Differential Revision: D58410389
Pulled By: bottler
fbshipit-source-id: f371e4c63d2ae4c927a7ad48c2de8862761078de
Summary: Undoes the pytorch3d changes in D57294278 because they break builds for for PyTorch<2.1 .
Reviewed By: MichaelRamamonjisoa
Differential Revision: D57379779
fbshipit-source-id: 47a12511abcec4c3f4e2f62eff5ba99deb2fab4c
Summary:
Currently, it checks that the `2`th dimension of `p2` is the same size as the `2`th dimension of `p2` instead of `p1`.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1815
Reviewed By: MichaelRamamonjisoa
Differential Revision: D58586966
Pulled By: bottler
fbshipit-source-id: d4f723fa264f90fe368c10825c1acdfdc4c406dc
Summary: We can now move ray bundle to float dtype (e.g. from fp16 like types).
Reviewed By: bottler
Differential Revision: D57493109
fbshipit-source-id: 4e18a427e968b646fe5feafbff653811cd007981
Summary: `c10::optional` was switched to be `std::optional` after PyTorch moved to C++17. Let's eliminate `c10::optional`, if we can.
Reviewed By: albanD
Differential Revision: D57294278
fbshipit-source-id: f6f26133c43f8d92a4588f59df7d689e7909a0cd
Summary:
This diff removes a variable that was set, but which was not used.
LLVM-15 has a warning `-Wunused-but-set-variable` which we treat as an error because it's so often diagnostic of a code issue. Unused but set variables often indicate a programming mistake, but can also just be unnecessary cruft that harms readability and performance.
Removing this variable will not change how your code works, but the unused variable may indicate your code isn't working the way you thought it was. I've gone through each of these by hand, but mistakes may have slipped through. If you feel the diff needs changes before landing, **please commandeer** and make appropriate changes: there are hundreds of these and responding to them individually is challenging.
For questions/comments, contact r-barnes.
- If you approve of this diff, please use the "Accept & Ship" button :-)
Reviewed By: bottler
Differential Revision: D56886956
fbshipit-source-id: 0c515ed98b812b1c106a59e19ec90751ce32e8c0
Summary:
For larger N and Mi value (e.g. N=154, Mi=238) I notice list_to_packed() has become a bottleneck for my application. By removing the for loop and running on GPU, i see a 10-20 x speedup.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1737
Reviewed By: MichaelRamamonjisoa
Differential Revision: D54187993
Pulled By: bottler
fbshipit-source-id: 16399a24cb63b48c30460c7d960abef603b115d0
Summary:
adjusted sample_nums to match the number of columns in the image grid. It originally produced image grid with 5 axes but only 3 images and after this fix, the block would work as intended.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1768
Reviewed By: MichaelRamamonjisoa
Differential Revision: D55632872
Pulled By: bottler
fbshipit-source-id: 44d633a8068076889e49d49b8a7910dba0db37a7
Summary:
### Generalise tutorials' pip searching:
## Required Information:
This diff contains changes to several PyTorch3D tutorials.
**Purpose of this diff:**
Replace the current installation code with a more streamlined approach that tries to install the wheel first and falls back to installing from source if the wheel is not found.
**Why this diff is required:**
This diff makes it easier to cope with new PyTorch releases and reduce the need for manual intervention, as the current process involves checking the version of PyTorch in Colab and building a new wheel if it doesn't match the expected version, which generates additional work each time there is a a new PyTorch version in Colab.
**Changes:**
Before:
```
if torch.__version__.startswith("2.1.") and sys.platform.startswith("linux"):
# We try to install PyTorch3D via a released wheel.
pyt_version_str=torch.__version__.split("+")[0].replace(".", "")
version_str="".join([
f"py3{sys.version_info.minor}_cu",
torch.version.cuda.replace(".",""),
f"_pyt{pyt_version_str}"
])
!pip install fvcore iopath
!pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html
else:
# We try to install PyTorch3D from source.
!pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'
```
After:
```
pyt_version_str=torch.__version__.split("+")[0].replace(".", "")
version_str="".join([
f"py3{sys.version_info.minor}_cu",
torch.version.cuda.replace(".",""),
f"_pyt{pyt_version_str}"
])
!pip install fvcore iopath
if sys.platform.startswith("linux"):
# We try to install PyTorch3D via a released wheel.
!pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html
pip_list = !pip freeze
need_pytorch3d = not any(i.startswith("pytorch3d==") for i in pip_list)
if need_pytorch3d:
# We try to install PyTorch3D from source.
!pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'
```
Reviewed By: bottler
Differential Revision: D55431832
fbshipit-source-id: a8de9162470698320241ae8401427dcb1ce17c37
Summary:
Fix an inclusive vs exclusive scan mix-up that was accidentally introduced when removing the Thrust dependency (`Thrust::exclusive_scan`) and reimplementing it using `at::cumsum` (which does an inclusive scan).
This fixes two Github reported issues:
* https://github.com/facebookresearch/pytorch3d/issues/1731
* https://github.com/facebookresearch/pytorch3d/issues/1751
Reviewed By: bottler
Differential Revision: D54605545
fbshipit-source-id: da9e92f3f8a9a35f7b7191428d0b9a9ca03e0d4d
Summary: The diff support colors in cubify for align = "center"
Reviewed By: bottler
Differential Revision: D53777011
fbshipit-source-id: ccb2bd1e3d89be3d1ac943eff08f40e50b0540d9
Summary: Add an option to run tests without the OpenGL Renderer.
Reviewed By: patricklabatut
Differential Revision: D53573400
fbshipit-source-id: 54a14e7b2f156d24e0c561fdb279f4a9af01b793
Summary:
Fixes https://github.com/facebookresearch/pytorch3d/issues/1641. The bug was caused by the mistaken downcasting of an int64_t into int, causing issues only on inputs large enough to have hashes that escaped the bounds of an int32.
Also added a test case for this issue.
Reviewed By: bottler
Differential Revision: D53505370
fbshipit-source-id: 0fdd0efc6d259cc3b0263e7ff3a4ab2c648ec521
Summary: This change updates the type of p2_idx from size_t to int64_t to address compiler warnings related to signed/unsigned comparison.
Reviewed By: bottler
Differential Revision: D52879393
fbshipit-source-id: de5484d78a907fccdaae3ce036b5e4a1a0a4de70
Summary: Fixed `get_rgbd_point_cloud` to take any number of image input channels.
Reviewed By: bottler
Differential Revision: D52796276
fbshipit-source-id: 3ddc0d1e337a6cc53fc86c40a6ddb136f036f9bc
Summary:
An OSS user has pointed out in https://github.com/facebookresearch/pytorch3d/issues/1703 that the output of matrix_to_quaternion (in that file) can be non standardized.
This diff solves the issue by adding a line of standardize at the end of the function
Reviewed By: bottler
Differential Revision: D52368721
fbshipit-source-id: c8d0426307fcdb7fd165e032572382d5ae360cde
Summary: Implement `submeshes` for TexturesUV. Fix what Meshes.submeshes passes to the texture's submeshes function to make this possible.
Reviewed By: bottler
Differential Revision: D52192060
fbshipit-source-id: 526734962e3376aaf75654200164cdcebfff6997
Summary: Performance improvement: Use torch.lerp to map uv coordinates to the range needed for grid_sample (i.e. map [0, 1] to [-1, 1] and invert the y-axis)
Reviewed By: bottler
Differential Revision: D51961728
fbshipit-source-id: db19a5e3f482e9af7b96b20f88a1e5d0076dac43
Summary: User confusion (https://github.com/facebookresearch/pytorch3d/issues/1579) about how zbuf is used for alpha compositing. Added small description and reference to paper to help give some context.
Reviewed By: bottler
Differential Revision: D51374933
fbshipit-source-id: 8c489a5b5d0a81f0d936c1348b9ade6787c39c9a
Summary: Fixes lint in test_render_points in the PyTorch3D library.
Differential Revision: D51289841
fbshipit-source-id: 1eae621eb8e87b0fe5979f35acd878944f574a6a
Summary:
When the ply format looks as follows:
```
comment TextureFile ***.png
element vertex 892
property double x
property double y
property double z
property double nx
property double ny
property double nz
property double texture_u
property double texture_v
```
`MeshPlyFormat` class will read uv from the ply file and read the uv map as commented as TextureFile.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1100
Reviewed By: MichaelRamamonjisoa
Differential Revision: D50885176
Pulled By: bottler
fbshipit-source-id: be75b1ec9a17a1ed87dbcf846a9072ea967aec37
Summary: Remove unused argument `mask_points` from `get_rgbd_point_cloud` and fix `get_implicitron_sequence_pointcloud`, which assumed it was used.
Reviewed By: MichaelRamamonjisoa
Differential Revision: D50885848
fbshipit-source-id: c0b834764ad5ef560107bd8eab04952d000489b8
Summary: I think we include more thrust than needed, and maybe removing it will help things like https://github.com/facebookresearch/pytorch3d/issues/1610 with DebugSyncStream errors on Windows.
Reviewed By: shapovalov
Differential Revision: D48949888
fbshipit-source-id: add889c0acf730a039dc9ffd6bbcc24ded20ef27
Summary: Python3 makes the use of `(object)` in class inheritance unnecessary. Let's modernize our code by eliminating this.
Reviewed By: itamaro
Differential Revision: D48673863
fbshipit-source-id: 032d6028371f0350252e6b731c74f0f5933c83cd
Summary:
The `chamfer_distance` function currently allows `"sum"` or `"mean"` reduction, but does not support returning unreduced (per-point) loss terms. Unreduced losses could be useful if the user wishes to inspect individual losses, or perform additional modifications to loss terms before reduction. One example would be implementing a robust kernel over the loss.
This PR adds a `None` option to the `point_reduction` parameter, similar to `batch_reduction`. In case of bi-directional chamfer loss, both the forward and backward distances are returned (a tuple of Tensors of shape `[D, N]` is returned). If normals are provided, similar logic applies to normals as well.
This PR addresses issue https://github.com/facebookresearch/pytorch3d/issues/622.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1605
Reviewed By: jcjohnson
Differential Revision: D48313857
Pulled By: bottler
fbshipit-source-id: 35c824827a143649b04166c4817449e1341b7fd9
Summary:
Something's wrong with recommonmark/CommonMark/six, let's see if this fixes it.
https://readthedocs.org/projects/pytorch3d/builds/21292632/
```
File "/home/docs/checkouts/readthedocs.org/user_builds/pytorch3d/envs/latest/lib/python3.11/site-packages/sphinx/config.py", line 368, in eval_config_file
execfile_(filename, namespace)
File "/home/docs/checkouts/readthedocs.org/user_builds/pytorch3d/envs/latest/lib/python3.11/site-packages/sphinx/util/pycompat.py", line 150, in execfile_
exec_(code, _globals)
File "/home/docs/checkouts/readthedocs.org/user_builds/pytorch3d/checkouts/latest/docs/conf.py", line 25, in <module>
from recommonmark.parser import CommonMarkParser
File "/home/docs/checkouts/readthedocs.org/user_builds/pytorch3d/envs/latest/lib/python3.11/site-packages/recommonmark/parser.py", line 6, in <module>
from CommonMark import DocParser, HTMLRenderer
File "/home/docs/checkouts/readthedocs.org/user_builds/pytorch3d/envs/latest/lib/python3.11/site-packages/CommonMark/__init__.py", line 3, in <module>
from CommonMark.CommonMark import HTMLRenderer
File "/home/docs/checkouts/readthedocs.org/user_builds/pytorch3d/envs/latest/lib/python3.11/site-packages/CommonMark/CommonMark.py", line 18, in <module>
HTMLunescape = html.parser.HTMLParser().unescape
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'HTMLParser' object has no attribute 'unescape'
```
Reviewed By: shapovalov
Differential Revision: D47471545
fbshipit-source-id: 48e121e20da535b3cc46b6bd2393d28869067b8b
Summary: New versions of cuda etc. I haven't committed recent changes to this for a while
Reviewed By: shapovalov
Differential Revision: D47396136
fbshipit-source-id: d6c27f5056fa8f4a74a628fa1d831159000acf55
Summary: This is needed from september 2023. As a side effect, implicitron docs should build better because typing.get_args exists etc.
Reviewed By: shapovalov
Differential Revision: D47363855
fbshipit-source-id: a954c5b81b1e5a4435fca146a11aea0d2ca96f45
Summary:
Blender uses OpenEXR to dump depth maps, so we have to support it.
OpenCV requires to explicitly accepth the vulnerabilities by setting the env var before exporting.
We can set it but I think it should be user’s responsibility.
OpenCV error reporting is adequate, so I don’t handle the error on our side.
Reviewed By: bottler
Differential Revision: D47403884
fbshipit-source-id: 2fcadd1df9d0efa0aea563bcfb2e3180b3c4d1d7
Summary:
For fg-masking depth, we assumed np.array but passed a Tensor; for defining the default depth_mask, vice versa.
Note that we change the intended behaviour for the latter, assuming that 0s are areas with empty depth. When loading depth masks, we replace NaNs with zeros, so it is sensible. It is not a BC change as that branch would crash if executed. Since there was no reports, I assume no one cared.
Reviewed By: bottler
Differential Revision: D47403588
fbshipit-source-id: 1094104176d7d767a5657b5bbc9f5a0cc9da0ede
Summary:
Convert ImplicitronRayBundle to a "classic" class instead of a dataclass. This change is introduced as a way to preserve the ImplicitronRayBundle interface while allowing two outcomes:
- init lengths arguments is now a Optional[torch.Tensor] instead of torch.Tensor
- lengths is now a property which returns a `torch.Tensor`. The lengths property will either recompute lengths from bins or return the stored _lengths. `_lenghts` is None if bins is set. It saves us a bit of memory.
Reviewed By: shapovalov
Differential Revision: D46686094
fbshipit-source-id: 3c75c0947216476ebff542b6f552d311024a679b
Summary:
## Context
Bins are used in mipnerf to allow to manipulate easily intervals. For example, by doing the following, `bins[..., :-1]` you will obtain all the left coordinates of your intervals, while doing `bins[..., 1:]` is equals to the right coordinates of your intervals.
We introduce here the support of bins like in MipNerf implementation.
## RayPointRefiner
Small changes have been made to modify RayPointRefiner.
- If bins is None
```
mids = torch.lerp(ray_bundle.lengths[..., 1:], ray_bundle.lengths[…, :-1], 0.5)
z_samples = sample_pdf(
mids, # [..., npt]
weights[..., 1:-1], # [..., npt - 1]
….
)
```
- If bins is not None
In the MipNerf implementation the sampling is done on all the bins. It allows us to use the full weights tensor without slashing it.
```
z_samples = sample_pdf(
ray_bundle.bins, # [..., npt + 1]
weights, # [..., npt]
...
)
```
## RayMarcher
Add a ray_deltas optional argument. If None, keep the same deltas computation from ray_lengths.
Reviewed By: shapovalov
Differential Revision: D46389092
fbshipit-source-id: d4f1963310065bd31c1c7fac1adfe11cbeaba606
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: We now use unittest.mock
Reviewed By: shapovalov
Differential Revision: D45868799
fbshipit-source-id: cd1042dc2c49c82c7b9e024f761c496049a31beb
Summary: Make test work in isolation, and when run internally make it not try the sqlalchemy files.
Reviewed By: shapovalov
Differential Revision: D46352513
fbshipit-source-id: 7417a25d7a5347d937631c9f56ae4e3242dd622e
Summary:
Hi,
Not sure this is the best fix. But while running this notebook, I only ever saw a blank canvas when trying to visualize the dolphin. It might be that I have a broken dependency, like plotly. I also don't know what the visualization is "supposed" to look like.
But incase other people have this issue, this one line change solved the whole problem for me. Now I have a happy, rotatable dolphin.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1549
Reviewed By: shapovalov
Differential Revision: D46350930
Pulled By: bottler
fbshipit-source-id: e19aa71eb05a93e2955262a2c90d1f0d09576228
Summary: Fix for https://github.com/facebookresearch/pytorch3d/issues/1441 where we were indexing with a tensor on the wrong device.
Reviewed By: shapovalov
Differential Revision: D46276449
fbshipit-source-id: 7750ed45ffecefa5d291fd1eadfe515310c2cf0d
Summary: Making it easier for the clients to use these datasets.
Reviewed By: bottler
Differential Revision: D46727179
fbshipit-source-id: cf619aee4c4c0222a74b30ea590cf37f08f014cc
Summary: In D42739669, I forgot to update the API of existing implementations of DatasetBase to take `subset_filter`. Looks like only one was missing.
Reviewed By: bottler
Differential Revision: D46724488
fbshipit-source-id: 13ab7a457f853278cf06955aad0cc2bab5fbcce6
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: 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: Drop support for PyTorch 1.9.0 and 1.9.1.
Reviewed By: shapovalov
Differential Revision: D45704329
fbshipit-source-id: c0fe3ecf6a1eb9bcd4163785c0cb4bf4f5060f50
Summary:
typing.NamedTuple was simplified in 3.10
These two fields were the same in 3.8, so this should be a no-op
#buildmore
Reviewed By: bottler
Differential Revision: D45373526
fbshipit-source-id: 2b26156f5f65b7be335133e9e705730f7254260d
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:
If my understanding is right, prp_screen[1] should be 32 rather than 48.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1501
Reviewed By: shapovalov
Differential Revision: D45044406
Pulled By: bottler
fbshipit-source-id: 7dd93312db4986f4701e642ba82d94333466b921
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: Remove the need of tuple and reversed in the raysampling xy_grid computation
Reviewed By: bottler
Differential Revision: D45269342
fbshipit-source-id: d0e4c0923b9a2cca674b35e8d64862043a0eab3b
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:
The pattern
```
X.Y if hasattr(X, "Y") else Z
```
can be replaced with
```
getattr(X, "Y", Z)
```
The [getattr](https://www.w3schools.com/python/ref_func_getattr.asp) function gives more succinct code than the [hasattr](https://www.w3schools.com/python/ref_func_hasattr.asp) function. Please use it when appropriate.
**This diff is very low risk. Green tests indicate that you can safely Accept & Ship.**
Reviewed By: bottler
Differential Revision: D44886893
fbshipit-source-id: 86ba23e837217e1ebd64bf8e27d286257894839e
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: For safety checks, make inplace forward operations in cuda and c++ call increment_version.
Reviewed By: davidsonic
Differential Revision: D44302504
fbshipit-source-id: 6ff62251e352d6778cb54399e2e11459e16e77ba
Summary: - Replace all the relative imports for generic models to absolute import: (from . import y => from pytorch3.x import y)
Reviewed By: shapovalov
Differential Revision: D43620682
fbshipit-source-id: 937318b339b5020d17b511a891c7b000ff659328
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
Summary:
Aligning the logic with the official CO3Dv2 evaluation: 92283c4368/co3d/dataset/utils.py (L7)
This will make the evaluator work with the datasets that do not define known/unseen subsets.
Reviewed By: bottler
Differential Revision: D42803136
fbshipit-source-id: cfac389eab010c32d2e33b40fc7f6ed845c327ef
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: ChainDataset is iterable, and it toes not go along with a custom batch sampler.
Reviewed By: bottler
Differential Revision: D42742315
fbshipit-source-id: 40a715c8d24abe72cb2777634247d7467f628564
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:
V2 dataset does not have the concept of known/unseen frames. Test-time conditining is done with train-set frames, which violates the previous check.
Also fixing a corner case in VideoWriter.
Reviewed By: bottler
Differential Revision: D42706976
fbshipit-source-id: d43be3dd3060d18cb9f46d5dcf6252d9f084110f
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: 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:
The file [rasterizer.py](de3a474d2b/pytorch3d/renderer/mesh/rasterizer.py (L201)) contains a duplicate line before the check if the projection_transform exists. This causes an exception in the case that a projection transform matrix is already provided. The corresponding lines should be (and are already) in the else case of the if-statement.
Removing these lines fixes the bug and produces the desired behavior.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1421
Reviewed By: shapovalov
Differential Revision: D42450999
Pulled By: bottler
fbshipit-source-id: f7464e87ec9ff8768455656324b0b008132c8a54
Summary: Use IndexError so that a camera object is an iterable
Reviewed By: shapovalov
Differential Revision: D42312021
fbshipit-source-id: 67c417d5f1398e8b30a6944468eda057b4ceb444
Summary:
- Fix the numbers in the headers. Currently, there are no header number 2, the tutorial jump from 1 to 3.
- Clean some unnecessary code.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1423
Reviewed By: shapovalov
Differential Revision: D42478609
Pulled By: bottler
fbshipit-source-id: c49fc10b7d38c3573c92fea737101e6c06bbea38
Summary:
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1394
The bash logic for building conda packages became fiddly to edit. We need to switch cuda-toolkit to pytorch-cuda when PyTorch>=1.12 which was going to be a pain, so here I rewrite the code in python and do it.
Reviewed By: shapovalov
Differential Revision: D42036406
fbshipit-source-id: 8bb80c2f7545477182b23fc97c8514dcafcee176
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: Python 3.7 not needed any more
Reviewed By: shapovalov
Differential Revision: D41841033
fbshipit-source-id: c0cfd048c70e6b9e47224ab8cddcd6b5f4fc5597
Summary: All mac builds now pytorch 1.13
Reviewed By: shapovalov
Differential Revision: D41841035
fbshipit-source-id: b932eb2fefed77ae22f9757f9bd628ce12b11fad
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: 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
The core library is written in PyTorch. Several components have underlying implementation in CUDA for improved performance. A subset of these components have CPU implementations in C++/PyTorch. It is advised to use PyTorch3D with GPU support in order to use all the features.
- Data structure for storing and manipulating triangle meshes
- Efficient operations on triangle meshes (projective transformations, graph convolution, sampling, loss functions)
- A differentiable mesh renderer
- Implicitron, see [its README](projects/implicitron_trainer), a framework for new-view synthesis via implicit representations.
- Implicitron, see [its README](projects/implicitron_trainer), a framework for new-view synthesis via implicit representations. ([blog post](https://ai.facebook.com/blog/implicitron-a-new-modular-extensible-framework-for-neural-implicit-representations-in-pytorch3d/))
PyTorch3D is designed to integrate smoothly with deep learning methods for predicting and manipulating 3D data.
For this reason, all operators in PyTorch3D:
@@ -24,6 +24,8 @@ For this reason, all operators in PyTorch3D:
Within FAIR, PyTorch3D has been used to power research projects such as [Mesh R-CNN](https://arxiv.org/abs/1906.02739).
See our [blog post](https://ai.facebook.com/blog/-introducing-pytorch3d-an-open-source-library-for-3d-deep-learning/) to see more demos and learn about PyTorch3D.
## Installation
For detailed instructions refer to [INSTALL.md](INSTALL.md).
@@ -56,6 +58,11 @@ Get started with PyTorch3D by trying one of the tutorial notebooks.
| [Fit Textured Volume in Implicitron](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/implicitron_volumes.ipynb)| [Implicitron Config System](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/implicitron_config_system.ipynb)|
@@ -95,6 +102,7 @@ In alphabetical order:
* Amitav Baruah
* Steve Branson
* Krzysztof Chalupka
* Jiali Duan
* Luya Gao
* Georgia Gkioxari
* Taylor Gordon
@@ -138,6 +146,16 @@ If you are using the pulsar backend for sphere-rendering (the `PulsarPointRender
Please see below for a timeline of the codebase updates in reverse chronological order. We are sharing updates on the releases as well as research projects which are built with PyTorch3D. The changelogs for the releases are available under [`Releases`](https://github.com/facebookresearch/pytorch3d/releases), and the builds can be installed using `conda` as per the instructions in [INSTALL.md](INSTALL.md).
**[Aug 10th 2022]:** PyTorch3D [v0.7.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.7.0) released with Implicitron and MeshRasterizerOpenGL.
**[Apr 28th 2022]:** PyTorch3D [v0.6.2](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.6.2) released
stored either in a GLB container file or a glTF JSON file with embedded binary data.
This must be enabled explicitly, as described in
`pytorch3d/io/experimental_gltf_io.ply`.
`pytorch3d/io/experimental_gltf_io.py`.
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