Summary: Update the transform step in the pointcloud rasterizer to use the `update_padded` method on `Pointclouds`. There was an inefficient step using `offset_points` which went via the packed represntation (and required unecessary additional memory). I think this was before the `update_padded` method was added to `Pointclouds`.
Reviewed By: gkioxari
Differential Revision: D22329166
fbshipit-source-id: 76db8a19654fb2f7807635d4f1c1729debdf3320
Summary:
The documentation of `Transform3d` highlights that the class handles points as well as normals. However, `transform_points` is applied to points and normals in the documentation instead of using `transform_normals` for normals, which I believe was intended.
This pull request fixes this typo in the documentation.
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/233
Reviewed By: bottler
Differential Revision: D22287642
Pulled By: nikhilaravi
fbshipit-source-id: 0bc8754097b2e17a34fa3071319d00b78c3bc803
Summary: It's easier to understand code changes if we don't store the output in the notebooks. This is just a run of nbstripout on all the notebooks.
Reviewed By: nikhilaravi
Differential Revision: D22357375
fbshipit-source-id: bffdb426af2f676d448630d717658d609c224811
Summary:
Automatic run to suppress type errors.
#pyreupgrade
Differential Revision: D22369027
fbshipit-source-id: 2beb1a43e429a0850944a8849d416bedefd516ed
Summary: Added `sorted` argument to the `knn_points` function. This came up during the benchmarking against Faiss - sorting added extra memory usage. Match the memory usage of Faiss by making sorting optional.
Reviewed By: bottler, gkioxari
Differential Revision: D22329070
fbshipit-source-id: 0828ff9b48eefce99ce1f60089389f6885d03139
Summary:
Fix default setting of `max_faces_per_bin` and update mesh rasterization benchmark tests.
The previous setting of `max_faces_per_bin` was wrong and for larger mesh sizes and batch sizes it was causing a significant slow down due to an unecessarily large intermediate tensor being created.
Reviewed By: gkioxari
Differential Revision: D22301819
fbshipit-source-id: d5e817f5b917fb5633c9c6a8634b6c8ff65e3508
Summary:
Fixes the default setting of `max_points_per_bin` in `rasterize_points.py`. For large batches with large size pointclouds this was a causing the rasterizer to be very slow.
Expanded the pointcloud rendering benchmarks to include larger size pointclouds and fixed cuda synchronization issue in benchmark.
Reviewed By: gkioxari
Differential Revision: D22301185
fbshipit-source-id: 5077c1ba2c43d73efc1c659f0ec75959ceddf893
Summary: Now pytorch 1.5.1 is released, pytorch 1.5 is ambiguous and causes problems. Now have specific builds for pytorch 1.5.0 and 1.5.1. Here we only change the conda builds.
Reviewed By: gkioxari
Differential Revision: D22196016
fbshipit-source-id: 478327e870f538f54d3480d5a268a1ece5c5c680
Summary: Adding support so that users can select which categories they would like to load with wordnet synset offsets or labels or a combination of both. ShapeNetCore now also supports loading v2.
Reviewed By: nikhilaravi
Differential Revision: D22039207
fbshipit-source-id: 1f0218acb790e5561e2ae373e99cebb9823eea1a
Summary: Skeleton of ShapeNetCore class that loads ShapeNet v1 from a given directory to a Dataset object. Overrides _init_, _len_, and _getitem_ from torch.utils.data.Dataset. Currently getitem returns verts, faces and id_str, where id_str is a concatenation of synset_id and obj_id. Planning on adding support for loading ShapeNet v2, retrieving textures and returning wordnet synsets (not just ids) in next diffs.
Reviewed By: nikhilaravi
Differential Revision: D21986222
fbshipit-source-id: c2c515303f1898b6c495b52cb53c74d691585326
Summary: Fix the new CPU implementation of point_mesh functionality to be compatible with older C++.
Reviewed By: nikhilaravi
Differential Revision: D22066785
fbshipit-source-id: a245849342019a93ff68e186a10985458b007436
Summary:
point_mesh functions were missing CPU implementations.
The indices returned are not always matching, possibly due to numerical instability.
Reviewed By: gkioxari
Differential Revision: D21594264
fbshipit-source-id: 3016930e2a9a0f3cd8b3ac4c94a92c9411c0989d
Summary:
Pytorch seems to be becoming stricter about integer tensors of shape `(1,)` on GPU and not allowing them to be used as `int`s. For example the following no longer works on pytorch master,
foo = torch.tensor([3, 5, 3], device="cuda:0")
torch.arange(10) + foo[0]
because this is the sum of tensors on different devices.
Here fix tests which recently broke because of this.
Reviewed By: nikhilaravi
Differential Revision: D21929745
fbshipit-source-id: 25374f70468d1c895372766f1a9dd61df0833957
Summary: [Folly] Cut the FOR_EACH_KV macro, which may be replaced by a combination of range-for and structured bindings.
Differential Revision: D21826182
fbshipit-source-id: ce4712afd3d0d7806eb1fca8c97009da117f982e
Summary: Adding a function in pytorch3d.structures.meshes to join multiple meshes into a Meshes object representing a single mesh. The function currently ignores all textures.
Reviewed By: nikhilaravi
Differential Revision: D21876908
fbshipit-source-id: 448602857e9d3d3f774d18bb4e93076f78329823
Summary: To avoid pytorch warnings and future behaviour changes, stop using torch.div and / with tensors of integers.
Reviewed By: gkioxari, mruberry
Differential Revision: D21857955
fbshipit-source-id: fb9f3000f3d953352cdc721d2a5f73d3a4bbf4b7
Summary: [Folly] Cut the `FOR_EACH_ENUMERATE` macro, which may be replaced by a combination of range-for, `ranges::view::enumerate`, and structured bindings.
Reviewed By: markisaa
Differential Revision: D21813019
fbshipit-source-id: fc9ac09a4e2f72f1433d0a518f03d5cd69a59c55
Summary: Adds support to hard_rgb_blend and hard blending shaders in shader.py (HardPhongShader, HardGouraudShader, and HardFlatShader) for changing the background color on which objects are rendered
Reviewed By: nikhilaravi
Differential Revision: D21746062
fbshipit-source-id: 08001200f4339d6a69c52405c6b8f4cac9f3f56e
Summary:
Update the transform method in the mesh rasterizer class to use the new `update_padded` method on the `Meshes` class to directly update the mesh vertices.
Also added a benchmark.
Reviewed By: gkioxari
Differential Revision: D21700352
fbshipit-source-id: c330e4040c681729eb2cc7bdfd92fb4a51a1a7d6
Summary:
Three changes to Meshes
1. `num_verts_per_mesh` and `num_faces_per_mesh` are assigned at construction time and are returned without the need for `compute_packed`
2. `update_padded` updates `verts_padded` and shallow copies faces list and faces_padded and existing attributes from construction.
3. `padded_to_packed_idx` does not need `compute_packed`
Reviewed By: nikhilaravi
Differential Revision: D21653674
fbshipit-source-id: dc6815a2e2a925fe4a834fe357919da2b2c14527
Summary:
This diff is auto-generated to upgrade the Pyre version and suppress errors in vision. The upgrade will affect Pyre local configurations in the following directories:
```
vision/ale/search
vision/fair/fvcore
vision/fair/pytorch3d
vision/ocr/rosetta_hash
vision/vogue/personalization
```
Differential Revision: D21688454
fbshipit-source-id: 1f3c3fee42b6da2e162fd0932742ab8c5c96aa45
Summary:
Fix division by zero when alpha is 1.0
In this case, the nominator is already 0 and we need to make sure division with 0 does not occur which would produce nans
Reviewed By: nikhilaravi
Differential Revision: D21650478
fbshipit-source-id: bc457105b3050fef1c8bd4e58e7d6d15c0c81ffd
Summary: Add ability to decode ply files which use types like int32.
Reviewed By: nikhilaravi
Differential Revision: D21639208
fbshipit-source-id: 0ede7d4aa353a6e940446680a18e7ac0c48fafee
Summary: I was trying to speed up the lighting computations, but my ideas didn't work. Even if that didn't work, we can at least commit the benchmarking script I wrote for diffuse and specular shading.
Reviewed By: nikhilaravi
Differential Revision: D21580171
fbshipit-source-id: 8b60c0284e91ecbe258b6aae839bd5c2bbe788aa
Summary:
Update the cuda kernels to:
- remove contiguous checks for the grad tensors and for cpu functions which use accessors
- for cuda implementations call `.contiguous()` on all tensors in the host function before invoking the kernel
Reviewed By: gkioxari
Differential Revision: D21598008
fbshipit-source-id: 9b97bda4582fd4269c8a00999874d4552a1aea2d
Summary: lg-zhang found the problem with the quadratic part of ePnP implementation: n262385 . It was caused by a coefficient returned from the linear equation solver being equal to exactly 0.0, which caused `sign()` to return 0, something I had not anticipated. I also made sure we avoid division by zero by clamping all relevant denominators.
Reviewed By: nikhilaravi, lg-zhang
Differential Revision: D21531200
fbshipit-source-id: 9eb2fa9d4f4f8f5f411d4cf1cffcc44b365b7e51
Summary:
Make flat shading differentiable again
Currently test fails with P130944403 which looks weird.
Reviewed By: nikhilaravi
Differential Revision: D21567106
fbshipit-source-id: 65995b64739e08397b3d021b65625e3c377cd1a5
Summary: pytorch is adding checks that mean integer tensors with requires_grad=True need to be avoided. Fix accidentally creating them.
Reviewed By: jcjohnson, gkioxari
Differential Revision: D21576712
fbshipit-source-id: 008218997986800a36d93caa1a032ee91f2bffcd
Summary: Use `self.__class__` when creating new instances, to slightly accommodate inheritance.
Reviewed By: nikhilaravi
Differential Revision: D21504476
fbshipit-source-id: b4600d15462fc1985da95a4cf761c7d794cfb0bb
Summary:
Fixes the case where the rotation angle is exactly 0/PI.
Added a test for `so3_log_map(identity_matrix)`.
Reviewed By: nikhilaravi
Differential Revision: D21477078
fbshipit-source-id: adff804da97f6f0d4f50aa1f6904a34832cb8bfe
Summary: Fix to enable a mesh/point rasterizer to be initialized without having to specify the camera.
Reviewed By: jcjohnson, gkioxari
Differential Revision: D21362359
fbshipit-source-id: 4f84ea18ad9f179c7b7c2289ebf9422a2f5e26de
Summary: This has been failing intermittently
Reviewed By: nikhilaravi
Differential Revision: D21403157
fbshipit-source-id: 51b74d6c813b52effe72d14b565e250fcabbb463
Summary:
Ran the linter.
TODO: need to update the linter as per D21353065.
Reviewed By: bottler
Differential Revision: D21362270
fbshipit-source-id: ad0e781de0a29f565ad25c43bc94a19b1828c020
Summary:
Use nn.functional.interpolate instead of a TorchVision transform to resize texture maps to a common value. This works on all devices. This fixes issue #175.
Also fix the condition so it only happens when needed.
Reviewed By: nikhilaravi
Differential Revision: D21324510
fbshipit-source-id: c50eb06514984995bd81f2c44079be6e0b4098e4
Summary: Update version number for version 0.2.0.
Reviewed By: nikhilaravi
Differential Revision: D21157358
fbshipit-source-id: 32a5b93e5dc65a31a806a5ce7231f8603fe02e85
Summary: Bump the nvidia driver used in the conda tests. Add an environment variable (unused) to allow building without ninja. Print relative error on assertClose failure.
Reviewed By: nikhilaravi
Differential Revision: D21227373
fbshipit-source-id: 5dd8eb097151da27d3632daa755a1e7b9ac97845
Summary:
Cuda test failing on circle with the error `random_ expects 'from' to be less than 'to', but got from=0 >= to=0`
This is because the `high` value in `torch.randint` is 1 more than the highest value in the distribution from which a value is drawn. So if there is only 1 cuda device available then the low and high are 0.
Reviewed By: gkioxari
Differential Revision: D21236669
fbshipit-source-id: 46c312d431c474f1f2c50747b1d5e7afbd7df3a9
Summary:
Updates to:
- enable cuda kernel launches on any GPU (not just the default)
- cuda and contiguous checks for all kernels
- checks to ensure all tensors are on the same device
- error reporting in the cuda kernels
- cuda tests now run on a random device not just the default
Reviewed By: jcjohnson, gkioxari
Differential Revision: D21215280
fbshipit-source-id: 1bedc9fe6c35e9e920bdc4d78ed12865b1005519
Summary:
Updated the load obj function to support creating of a per face texture map using the information in an .mtl file. Uses the approach from in SoftRasterizer.
Currently I have ported in the SoftRasterizer code but this is only to help with comparison and will be deleted before landing. The ShapeNet Test data will also be deleted.
Here is the [Design doc](https://docs.google.com/document/d/1AUcLP4QwVSqlfLAUfbjM9ic5vYn9P54Ha8QbcVXW2eI/edit?usp=sharing).
## Added
- texture atlas creation functions in PyTorch based on the SoftRas cuda implementation
- tests to compare SoftRas vs PyTorch3D implementation to verify it matches (using real shapenet data with meshes consisting of multiple textures)
- benchmarks tests
## Remaining todo:
- add more tests for obj io to test the new functions and the two texturing options
- replace the shapenet data with the output from SoftRas saved as a file.
# MAIN FILES TO REVIEW
- `obj_io.py`
- `test_obj_io.py` [still some tests to be added but have comparisons with SoftRas for now]
The reference SoftRas implementations are in `softras_load_obj.py` and `load_textures.cu`.
Reviewed By: gkioxari
Differential Revision: D20754859
fbshipit-source-id: 42ace9dfb73f26e29d800c763f56d5b66c60c5e2
