Lint codebase

Summary: Lint codebase

Reviewed By: bottler

Differential Revision: D29263057

fbshipit-source-id: ac97f01d2a79fead3b09c2cbb21b50ce688a577d

.github/CONTRIBUTING.md

@@ -43,7 +43,7 @@ Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe
 disclosure of security bugs. In those cases, please go through the process
 outlined on that page and do not file a public issue.
 
-## Coding Style  
+## Coding Style
 We follow these [python](http://google.github.io/styleguide/pyguide.html) and [C++](https://google.github.io/styleguide/cppguide.html) style guides.
 
 For the linter to work, you will need to install `black`, `flake`, `isort` and `clang-format`, and

.github/ISSUE_TEMPLATE/bugs.md

@@ -27,4 +27,4 @@ Please include the following (depending on what the issue is):
 ```
 
 Please also simplify the steps as much as possible so they do not require additional resources to
-	 run, such as a private dataset.
+   run, such as a private dataset.

.github/ISSUE_TEMPLATE/questions-help.md

@@ -17,5 +17,5 @@ Also note the following:
    please use the "Bugs / Unexpected behaviors" issue template.
 
 2. We do not answer general machine learning / computer vision questions that are not specific to
-	 PyTorch3D, such as how a model works or what algorithm/methods can be
-	 used to achieve X.
+   PyTorch3D, such as how a model works or what algorithm/methods can be
+   used to achieve X.

docs/README.md

@@ -1,4 +1,3 @@
-
 ## Setup
 
 ### Install dependencies
@@ -12,7 +11,7 @@ pip install -U recommonmark mock sphinx sphinx_rtd_theme sphinx_markdown_tables
 We want to include the root readme as an overview. Before generating the docs create a symlink to the root readme.
 
 ```
-cd docs        
+cd docs
 ln -s ../README.md  overview.md
 ```
 

docs/notes/why_pytorch3d.md

@@ -7,7 +7,6 @@ sidebar_label: Why PyTorch3D
 # Why PyTorch3D
 
 
-Our goal with PyTorch3D is to help accelerate research at the intersection of deep learning and 3D. 3D data is more complex than 2D images and while working on projects such as [Mesh R-CNN](https://github.com/facebookresearch/meshrcnn) and [C3DPO](https://github.com/facebookresearch/c3dpo_nrsfm), we encountered several challenges including 3D data representation, batching, and speed. We have developed many useful operators and abstractions for working on 3D deep learning and want to share this with the community to drive novel research in this area. 
+Our goal with PyTorch3D is to help accelerate research at the intersection of deep learning and 3D. 3D data is more complex than 2D images and while working on projects such as [Mesh R-CNN](https://github.com/facebookresearch/meshrcnn) and [C3DPO](https://github.com/facebookresearch/c3dpo_nrsfm), we encountered several challenges including 3D data representation, batching, and speed. We have developed many useful operators and abstractions for working on 3D deep learning and want to share this with the community to drive novel research in this area.
 
 In PyTorch3D we have included efficient 3D operators, heterogeneous batching capabilities, and a modular differentiable rendering API, to equip researchers in this field with a much needed toolkit to implement cutting-edge research with complex 3D inputs.
-

docs/tutorials/data/cow_mesh/README.md

@@ -1,4 +1,3 @@
-
 # Acknowledgements
 
 Thank you to Keenen Crane for allowing the cow mesh model to be used freely in the public domain.

tests/common_testing.py

@@ -110,7 +110,7 @@ class TestCaseMixin(unittest.TestCase):
         diff = norm_fn(input - other)
         other_ = norm_fn(other)
 
-        # We want to generalise allclose(input, output), which is essentially
+        # We want to generalize allclose(input, output), which is essentially
         #  all(diff <= atol + rtol * other)
         # but with a sophisticated handling non-finite values.
         # We work that around by calling allclose() with the following arguments: