GitLab

Summary: Adding datasets.rst to modules and update index.rst.

Reviewed By: nikhilaravi

Differential Revision: D22187578

fbshipit-source-id: e70cf49fa276db8a106c67a2edab530b6dba2dee

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 address the issue https://github.com/facebookresearch/pytorch3d/issues/219 of Invalid rotation matrix when number of camera position is equal to 3
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/220

Reviewed By: bottler

Differential Revision: D21877606

Pulled By: nikhilaravi

fbshipit-source-id: f95ae44497cae33f2f0cff1b1718d866cf79bda1

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: Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/204

Reviewed By: gkioxari

Differential Revision: D21621695

Pulled By: nikhilaravi

fbshipit-source-id: 556f297bef4211c331dfde6471e10762a9956f98

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:
a quick fix for the clone issue

fixes https://github.com/facebookresearch/pytorch3d/issues/178
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/179

Reviewed By: bottler

Differential Revision: D21414309

Pulled By: nikhilaravi

fbshipit-source-id: 359d7724aa5d78bc88a0a9ffc05e6041056e3b3f

Summary: Add alignment modes for cubify operation.

Reviewed By: nikhilaravi

Differential Revision: D21393199

fbshipit-source-id: 7022044e591229a6ed5efc361fd3215e65f43f86

Summary: This has been failing intermittently

Reviewed By: nikhilaravi

Differential Revision: D21403157

fbshipit-source-id: 51b74d6c813b52effe72d14b565e250fcabbb463

Summary: Fix documentation of KNN, issue #180

Reviewed By: gkioxari

Differential Revision: D21384761

fbshipit-source-id: 2b36ee496f2060d17827d2fd66c490cdfa766866

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: Fix self assignment of normals when estimating normals

Reviewed By: nikhilaravi

Differential Revision: D21315980

fbshipit-source-id: 2aa5864c3f066e39e07343f192cc6423ce1ae771

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:
Some updates to the issue templates, readme and install.md

Creating an FAQ for installation similar to: https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md#common-installation-issues

Reviewed By: gkioxari

Differential Revision: D21117899

fbshipit-source-id: d287c3a7a99c2e425b4e0cffca55a7b225d79e11

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:
Signed-off-by: Michele Sanna <sanna@arrival.com>

fixes the bin_size calculation with a formula for any image_size > 64. Matches the values chosen so far.

simple test:

```
import numpy as np
import matplotlib.pyplot as plt

image_size = np.arange(64, 2048)
bin_size = np.where(image_size <= 64, 8, (2 ** np.maximum(np.ceil(np.log2(image_size)) - 4, 4)).astype(int))

print(image_size)
print(bin_size)

for ims, bins in zip(image_size, bin_size):
    if ims <= 64:
        assert bins == 8
    elif ims <= 256:
        assert bins == 16
    elif ims <= 512:
        assert bins == 32
    elif ims <= 1024:
        assert bins == 64
    elif ims <= 2048:
        assert bins == 128

    assert (ims + bins - 1) // bins < 22

plt.plot(image_size, bin_size)
plt.grid()
plt.show()
```

![img](https://user-images.githubusercontent.com/54891577/75464693-795bcf00-597f-11ea-9061-26440211691c.png)
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/90

Reviewed By: jcjohnson

Differential Revision: D21160372

Pulled By: nikhilaravi

fbshipit-source-id: 660cf5832f4ca5be243c435a6bed969596fc0188

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