Summary:
Optimizing sample_farthest_poinst by reducing CPU/GPU sync:
1. replacing iterative randint for starting indexes for 1 function call, if length is constant
2. Avoid sync in fetching maxumum of sample points, if we sample the same amount
3. Initializing 1 tensor for samples and indixes
compare
https://fburl.com/mlhub/7wk0xi98
Before
{F1980383703}
after
{F1980383707}
Histogram match pretty closely
{F1980464338}
Reviewed By: bottler
Differential Revision: D78731869
fbshipit-source-id: 060528ae7a1e0fbbd005d129c151eaf9405841de
Summary:
Fixes hard crashes (bus errors) when using MPS device (Apple Silicon) by implementing CPU checks throughout files in csrc subdirectories to check if on same mesh on a CPU device.
Note that this is the fourth and ultimate part of a larger change through multiple files & directories.
Reviewed By: bottler
Differential Revision: D77698176
fbshipit-source-id: 5bc9e3c5cea61afd486aed7396f390d92775ec6d
Summary:
Adds CHECK_CPU macros that checks if a tensor is on the CPU device throughout csrc directories and subdir up to `pulsar`.
Note that this is the third part of a larger change, and to keep diffs better organized, subsequent diffs will update the remaining directories.
Reviewed By: bottler
Differential Revision: D77696998
fbshipit-source-id: 470ca65b23d9965483b5bdd30c712da8e1131787
Summary:
Adds CHECK_CPU macros that checks if a tensor is on the CPU device throughout csrc directories up to `marching_cubes`. Directories updated include those in `gather_scatter`, `interp_face_attrs`, `iou_box3d`, `knn`, and `marching_cubes`.
Note that this is the second part of a larger change, and to keep diffs better organized, subsequent diffs will update the remaining directories.
Reviewed By: bottler
Differential Revision: D77558550
fbshipit-source-id: 762a0fe88548dc8d0901b198a11c40d0c36e173f
Summary:
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1986
Adds device checks to prevent crashes on unsupported devices in PyTorch3D. Updates the `pytorch3d_cutils.h` file to include new macro CHECK_CPU that checks if a tensor is on the CPU device. This macro is then used in the directories from `ball_query` to `face_area_normals` to ensure that tensors are not on unsupported devices like MPS.
Note that this is the first part of a larger change, and to keep diffs better organized, subsequent diffs will update the remaining directories.
Reviewed By: bottler
Differential Revision: D77473296
fbshipit-source-id: 13dc84620dee667bddebad1dade2d2cb5a59c737
Summary:
The current implementation of `matrix_to_quaternion` and `_sqrt_positive_part` uses boolean indexing, which can slow down performance and cause incompatibility with `torch.compile` unless `torch._dynamo.config.capture_dynamic_output_shape_ops` is set to `True`.
To enhance performance and compatibility, I recommend using `torch.gather` to select the best-conditioned quaternions and `F.relu` instead of `x>0` (bottler's suggestion)
For a detailed comparison of the implementation differences when using `torch.compile`, please refer to my Bento notebook
N7438339.
Reviewed By: bottler
Differential Revision: D77176230
fbshipit-source-id: 9a6a2e0015b5865056297d5f45badc3c425b93ce
Summary: Resolved self-assignment warnings in the `renderer.forward.device.h` file by removing redundant assignments of the `stream` variable to itself in `cub::DeviceSelect::Flagged` function calls. This change eliminates compiler errors and ensures cleaner, more efficient code execution.
Reviewed By: bottler
Differential Revision: D76554140
fbshipit-source-id: 28eae0186246f51a8ac8002644f184349aa49560
Summary:
I could not access https://github.com/NVlabs/cub/issues/172 to understand whether IntWrapper was still necessary but the comment is from 5 years ago and causes problems for the ROCm build.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1964
Reviewed By: MichaelRamamonjisoa
Differential Revision: D71937895
Pulled By: bottler
fbshipit-source-id: 5e0351e1bd8599b670436cd3464796eca33156f6
Summary:
CUDA kernel variables matching the type `(thread|block|grid).(Idx|Dim).(x|y|z)` [have the data type `uint`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/#built-in-variables).
Many programmers mistakenly use implicit casts to turn these data types into `int`. In fact, the [CUDA Programming Guide](https://docs.nvidia.com/cuda/cuda-c-programming-guide/) it self is inconsistent and incorrect in its use of data types in programming examples.
The result of these implicit casts is that our kernels may give unexpected results when exposed to large datasets, i.e., those exceeding >~2B items.
While we now have linters in place to prevent simple mistakes (D71236150), our codebase has many problematic instances. This diff fixes some of them.
Reviewed By: dtolnay
Differential Revision: D71355356
fbshipit-source-id: cea44891416d9efd2f466d6c45df4e36008fa036
Summary:
A continuation of https://github.com/facebookresearch/pytorch3d/issues/1948 -- this commit fixes a small numerical issue with `matrix_to_axis_angle(..., fast=True)` near `pi`.
bottler feel free to check this out, it's a single-line change.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1953
Reviewed By: MichaelRamamonjisoa
Differential Revision: D70088251
Pulled By: bottler
fbshipit-source-id: 54cc7f946283db700cec2cd5575cf918456b7f32
Summary:
Remove headers flagged by facebook-unused-include-check over fbcode.vision.
+ format and autodeps
This is a codemod. It was automatically generated and will be landed once it is approved and tests are passing in sandcastle.
You have been added as a reviewer by Sentinel or Butterfly.
Autodiff project: uiv
Autodiff partition: fbcode.vision
Autodiff bookmark: ad.uiv.fbcode.vision
Reviewed By: dtolnay
Differential Revision: D70403619
fbshipit-source-id: d109c15774eeb3d809875f75fa2a26ed20d7f9a6
Summary:
This is an extension of https://github.com/facebookresearch/pytorch3d/issues/1544 with various speed, stability, and readability improvements. (I could not find a way to make a commit to the existing PR). This PR is still based on the [Rodrigues' rotation formula](https://en.wikipedia.org/wiki/Rotation_formalisms_in_three_dimensions#Rotation_matrix_%E2%86%94_Euler_axis/angle).
The motivation is the same; this change speeds up the conversions up to 10x, depending on the device, batch size, etc.
### Notes
- As the angles get very close to `π`, the existing implementation and the proposed one start to differ. However, (my understanding is that) this is not a problem as the axis can not be stably inferred from the rotation matrix in this case in general.
- bottler , I tried to follow similar conventions as existing functions to deal with weird angles, let me know if something needs to be changed to merge this.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1948
Reviewed By: MichaelRamamonjisoa
Differential Revision: D69193009
Pulled By: bottler
fbshipit-source-id: e5ed34b45b625114ec4419bb89e22a6aefad4eeb
Summary:
This is a somewhat not BC change: some None paths will be replaced by metadata paths, even when they were not used for data loading.
Moreover, removing the legacy fix to the paths in the old CO3D release.
Reviewed By: bottler
Differential Revision: D69048238
fbshipit-source-id: 2a8b26d7b9f5e2adf39c65888b5863a5a9de1996
Summary: Update Pytorch3D to be able to run assetgen (see later diffs in the stack)
Reviewed By: shapovalov
Differential Revision: D65942513
fbshipit-source-id: 1d01141c9f7e106608fa591be6e0d3262cb5944f
Summary: We did not often extend sequence-level metadata but now for applications like text-to-3D/video, we need to store captions and similar.
Reviewed By: bottler
Differential Revision: D68269926
fbshipit-source-id: f8af308adce51863d719a335d85cd2558943bd4c
Summary:
It is often easier to store the mask together with RGB, especially for renders. The logic in this diff:
* if load_mask and mask_path provided, take the mask from mask_path,
* otherwise, check if the image has the alpha channel and take it as a mask.
Reviewed By: antoinetlc
Differential Revision: D68160212
fbshipit-source-id: d9b6779f90027a4987ba96800983f441edff9c74
Summary: This function makes it easier to extend FrameData class with new channels; brushing it up a bit.
Reviewed By: bottler
Differential Revision: D67816470
fbshipit-source-id: 6575415c864d0f539e283889760cd2331bf226a7
Summary: Now that we have SQLAlchemy 2.0, we can fully use them.
Reviewed By: bottler
Differential Revision: D66920096
fbshipit-source-id: 25c0ea1c4f7361e66348035519627dc961b9e6e6
Summary:
Converts the directory specified to use the Ruff formatter in pyfmt
ruff_dog
If this diff causes merge conflicts when rebasing, please run
`hg status -n -0 --change . -I '**/*.{py,pyi}' | xargs -0 arc pyfmt`
on your diff, and amend any changes before rebasing onto latest.
That should help reduce or eliminate any merge conflicts.
allow-large-files
Reviewed By: bottler
Differential Revision: D66472063
fbshipit-source-id: 35841cb397e4f8e066e2159550d2f56b403b1bef
Summary:
- Hipified Pytorch Pulsar
- Created separate target for Pulsar tests and enabled RE testing
- Pytorch3D full test suite requires additional work like fixing EGL
dependencies on AMD
Reviewed By: danzimm
Differential Revision: D61339912
fbshipit-source-id: 0d10bc966e4de4a959f3834a386bad24e449dc1f
Summary: `c10::optional` is an alias for `std::optional`. Let's remove the alias and use the real thing.
Reviewed By: meyering
Differential Revision: D63402341
fbshipit-source-id: 241383e7ca4b2f3f1f9cac3af083056123dfd02b
Summary: `c10::optional` is an alias for `std::optional`. Let's remove the alias and use the real thing.
Reviewed By: palmje
Differential Revision: D63409387
fbshipit-source-id: fb6db59a14db9e897e2e6b6ad378f33bf2af86e8
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
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
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: new implicitronRayBundle with added cameraIDs and camera counts. Added to enable a single raybundle inside Implicitron and easier extension in the future. Since RayBundle is named tuple and RayBundleHeterogeneous is dataclass and RayBundleHeterogeneous cannot inherit RayBundle. So if there was no ImplicitronRayBundle every function that uses RayBundle now would have to use Union[RayBundle, RaybundleHeterogeneous] which is confusing and unecessary complicated.
Reviewed By: bottler, kjchalup
Differential Revision: D39262999
fbshipit-source-id: ece160e32f6c88c3977e408e966789bf8307af59
Summary:
Added heterogeneous raysampling to pytorch3d raysampler, different cameras are sampled different number of times.
It now returns RayBundle if heterogeneous raysampling is off and new RayBundleHeterogeneous (with added fields `camera_ids` and `camera_counts`). Heterogeneous raysampling is on if `n_rays_total` is not None.
Reviewed By: bottler
Differential Revision: D39542222
fbshipit-source-id: d3d88d822ec7696e856007c088dc36a1cfa8c625
Summary:
This is quite a thin wrapper – not sure we need it. The motivation is that `Transform3d` is not as matrix-centric now, it can be converted to SE(3) logarithm equally easily.
It simplifies things like averaging cameras and getting axis-angle of camera rotation (previously, one would need to call `se3_log_map(cameras.get_world_to_camera_transform().get_matrix())`), now one fewer thing to call / discover.
Reviewed By: bottler
Differential Revision: D39928000
fbshipit-source-id: 85248d5b8af136618f1d08791af5297ea5179d19
Summary:
`get_rotation_to_best_fit_xy` is useful to expose externally, however there was a bug (which we probably did not care about for our use case): it could return a rotation matrix with det(R) == −1.
The diff fixes that, and also makes centroid optional (it can be computed from points).
Reviewed By: bottler
Differential Revision: D39926791
fbshipit-source-id: 5120c7892815b829f3ddcc23e93d4a5ec0ca0013
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:
User reported that cloned cameras fail to save. The error with latest PyTorch is
```
pickle.PicklingError: Can't pickle ~T_destination: attribute lookup T_destination on torch.nn.modules.module failed
```
This fixes it.
Reviewed By: btgraham
Differential Revision: D39692258
fbshipit-source-id: 75bbf3b8dfa0023dc28bf7d4cc253ca96e46a64d
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: The implicit function and its members and internal working
Reviewed By: kjchalup
Differential Revision: D38829764
fbshipit-source-id: 28394fe7819e311ed52c9defc9a1b29f37fbc495
Summary: Allow using the new `foreach` option on optimizers.
Reviewed By: shapovalov
Differential Revision: D39694843
fbshipit-source-id: 97109c245b669bc6edff0f246893f95b7ae71f90
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:
Added export of UV textures to IO.save_mesh in Pytorch3d
MeshObjFormat now passes verts_uv, faces_uv, and texture_map as input to save_obj
TODO: check if TexturesUV.verts_uv_list or TexturesUV.verts_uv_padded() should be passed to save_obj
IO.save_mesh(obj_file, meshes, decimal_places=2) should be IO().save_mesh(obj_file, meshes, decimal_places=2)
Reviewed By: bottler
Differential Revision: D39617441
fbshipit-source-id: 4628b7f26f70e38c65f235852b990c8edb0ded23
Summary:
A significant speedup (e.g. >2% of a forward pass).
Move NDCMultinomialRaysampler parts of AbstractMaskRaySampler to members instead of living in a dict. The dict was hiding them from the nn.Module system so their _xy_grid members were remaining on the CPU. Therefore they were being copied to the GPU in every forward pass.
(We couldn't easily use a ModuleDict here because the enum keys are not strs.)
Reviewed By: shapovalov
Differential Revision: D39668589
fbshipit-source-id: 719b88e4a08fd7263a284e0ab38189e666bd7e3a
Summary:
- indicate location of OmegaConf.structured failures
- split the data gathering from enable_get_default_args to ease experimenting with it.
- comment fixes.
- nicer error when a_class_type has weird type.
Reviewed By: kjchalup
Differential Revision: D39434447
fbshipit-source-id: b80c7941547ca450e848038ef5be95b7ebbe8f3e
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: Samples batches without replacement if the number of samples is not specified. This makes sure that we always iterate over the whole dataset in each epoch.
Reviewed By: bottler
Differential Revision: D39270786
fbshipit-source-id: 0c983d1f5e0af711463abfb23939bc0d2b5172a0
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
Summary:
The self._stratified_sampling attribute is always overridden unless stratified_sampling is explicitly set to None. However, the desired default behavior is that the value of self._stratified_sampling is used unless the argument stratified_sampling is set to True/False. Changing the default to None achieves this
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1324
Reviewed By: bottler
Differential Revision: D39259775
Pulled By: davnov134
fbshipit-source-id: e01bb747ac80c812eb27bf22e67f5e14f29acadd
Summary: On each call of the stats.update the object calculates current average iteration time by getting time elapsed from the time_start and then dividing it by the current number of steps. It saves the result to AverageMeter object which when queried returns the average of things saved, so the time is averaged twice which biases it towards the start value (which is often larger).
Reviewed By: kjchalup
Differential Revision: D39206989
fbshipit-source-id: ccab5233d7aaca1ac4fd626fb329b83c7c0d6af9
Summary: Currently some implicit functions in implicitron take a raybundle, others take ray_points_world. raybundle is what they really need. However, the raybundle is going to become a bit more flexible later, as it will contain different numbers of rays for each camera.
Reviewed By: bottler
Differential Revision: D39173751
fbshipit-source-id: ebc038e426d22e831e67a18ba64655d8a61e1eb9
Summary: Update the docstring for try_get_projection_transform on the API design.
Reviewed By: kjchalup
Differential Revision: D39227333
fbshipit-source-id: c9d0e625735d4972116d1f71865fb9b763e684de
Summary:
1) Update rasterizer/point rasterizer to accommodate fisheyecamera. Specifically, transform_points is in placement of explicit transform compositions.
2) In rasterizer unittests, update corresponding tests for rasterizer and point_rasterizer. Address comments to test fisheye against perspective camera when distortions are turned off.
3) Address comments to add end2end test for fisheyecameras. In test_render_meshes, fisheyecameras are added to camera enuerations whenever possible.
4) Test renderings with fisheyecameras of different params on cow mesh.
5) Use compositions for linear cameras whenever possible.
Reviewed By: kjchalup
Differential Revision: D38932736
fbshipit-source-id: 5b7074fc001f2390f4cf43c7267a8b37fd987547
Summary:
Amend FisheyeCamera by adding tests for all
combination of params and for different batch_sizes.
Reviewed By: kjchalup
Differential Revision: D39176747
fbshipit-source-id: 830d30da24beeb2f0df52db0b17a4303ed53b59c
Summary: A dummy value in test_opengl_utils seems to be able to break tests in test_mesh_renderer_opengl{,_to}.
Reviewed By: kjchalup
Differential Revision: D39173275
fbshipit-source-id: 83b15159f70135ea575d5085c7b6b37badd6e49e
Summary: D38919607 (c4545a7cbc) and D38858887 (06cbba2628) were premature, turns out CUDA 10.2 doesn't support C++17.
Reviewed By: bottler
Differential Revision: D39156205
fbshipit-source-id: 5e2e84cc4a57d1113a915166631651d438540d56
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: Address comments to add benchmarkings for cameras and the new fisheye cameras. The dependency functions in test_cameras have been updated in Diff 1. The following two snapshots show benchmarking results.
Reviewed By: kjchalup
Differential Revision: D38991914
fbshipit-source-id: 51fe9bb7237543e4ee112c9f5068a4cf12a9d482
Summary:
1. A Fisheye camera model that generalizes pinhole camera by considering distortions (i.e. radial, tangential and thin-prism distortions).
2. Added tests against perspective cameras when distortions are off and Aria data points when distortions are on.
3. Address comments to test unhandled shapes between points and transforms. Added tests for __FIELDS, shape broadcasts, cuda etc.
4. Address earlier comments for code efficiency (e.g., adopted torch.norm; torch.solve for matrix inverse; removed inplace operations; unnecessary clone; expand in place of repeat etc).
Reviewed By: jcjohnson
Differential Revision: D38407094
fbshipit-source-id: a3ab48c85c496ac87af692d5d461bb3fc2a2db13
Summary: I think there is a typo here could not find any MultiPassEARenderer just MultiPassEmissionAbsorptionRenderer?
Reviewed By: bottler
Differential Revision: D39056641
fbshipit-source-id: 4dd0b123fc795a0083a957786c032e23dc5abac9
Summary: Added replacable decoding functions which will be applied after the voxel grid to get color and density
Reviewed By: bottler
Differential Revision: D38829763
fbshipit-source-id: f21ce206c1c19548206ea2ce97d7ebea3de30a23
Summary: Simple wrapper around voxel grids to make them a module
Reviewed By: bottler
Differential Revision: D38829762
fbshipit-source-id: dfee85088fa3c65e396cc7d3bf7ebaaffaadb646
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: Moved the MLP and transformer from nerf to a new file to be reused.
Reviewed By: bottler
Differential Revision: D38828150
fbshipit-source-id: 8ff77b18b3aeeda398d90758a7bcb2482edce66f
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
Summary: Fix EPS issue that causes numerical instabilities when boxes are very close
Reviewed By: kjchalup
Differential Revision: D38661465
fbshipit-source-id: d2b6753cba9dc2f0072ace5289c9aa815a1a29f6
Summary: Removing hardcoded block reduction operation from `sample_farthest_points.cu` code, and replace it with `cub::BlockReduce` reducing complexity of the code, and letting established libraries do the thinking for us.
Reviewed By: bottler
Differential Revision: D38617147
fbshipit-source-id: b230029c55f05cda0aab1648d3105a8d3e92d27b
Summary: Split Volumes class to data and location part so that location part can be reused in planned VoxelGrid classes.
Reviewed By: bottler
Differential Revision: D38782015
fbshipit-source-id: 489da09c5c236f3b81961ce9b09edbd97afaa7c8
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:
We identified that these logging statements can deteriorate performance in certain cases. I propose removing them from the regular renderer implementation and letting individuals re-insert debug logging wherever needed on a case-by-case basis.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1260
Reviewed By: kjchalup
Differential Revision: D38737439
Pulled By: bottler
fbshipit-source-id: cf9dcbbeae4dbf214c2e17d5bafa00b2ff796393
Summary: Useful for visualising colmap output where some frames are not correctly registered.
Reviewed By: bottler
Differential Revision: D38743191
fbshipit-source-id: e823df2997870dc41d76784e112d4349f904d311
Summary: Previously, "psnr" was evaluated between the masked g.t. image and the render. To avoid confusion, "psnr" is now renamed to "psnr_masked".
Reviewed By: bottler
Differential Revision: D38707511
fbshipit-source-id: 8ee881ab1a05453d6692dde9782333a47d8c1234
Summary: Builds for new PyTorch 1.12.1. Drop builds for PyTorch 1.8.0 and 1.8.1.
Reviewed By: kjchalup
Differential Revision: D38658991
fbshipit-source-id: 6192e226c2154cd051eeee98498d9a395cfd6fd5
Summary: Reports also the PSNR between the unmasked G.T. image and the render.
Reviewed By: bottler
Differential Revision: D38655943
fbshipit-source-id: 1603a2d02116ea1ce037e5530abe1afc65a2ba93
Summary:
**"filename"**: "projects/nerf/nerf/implicit_function.py"
**"warning_type"**: "Incompatible variable type [9]",
**"warning_message"**: " input_skips is declared to have type `Tuple[int]` but is used as type `Tuple[]`.",
**"warning_line"**: 256,
**"fix"**: input_skips: Tuple[int,...] = ()
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1288
Reviewed By: kjchalup
Differential Revision: D38615188
Pulled By: bottler
fbshipit-source-id: a014344dd6cf2125f564f948a3c905ceb84cf994
2022-08-11 08:54:37 -07:00
607 changed files with 27683 additions and 7019 deletions
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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 +146,20 @@ 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
**[Dec 16th 2021]:** PyTorch3D [v0.6.1](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.6.1) released
**[Oct 6th 2021]:** PyTorch3D [v0.6.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.6.0) released
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