Lint codebase

Summary: Lint codebase

Reviewed By: bottler

Differential Revision: D29263057

fbshipit-source-id: ac97f01d2a79fead3b09c2cbb21b50ce688a577d

docs/README.md

-
 ## Setup
 
 ### Install dependencies

docs/notes/why_pytorch3d.md

@@ -10,4 +10,3 @@ sidebar_label: 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.
 
 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

-
 # 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: