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lint fixes

Browse Source 
Summary: Ran `dev/linter.sh`.

Reviewed By: bottler

Differential Revision: D19761062

fbshipit-source-id: 1a49abe4a5f2bc7641b2b46e254aa77e6a48aa7d
This commit is contained in:
Nikhila Ravi 2020-02-13 20:49:18 -08:00 committed by Facebook Github Bot
parent 29cd181a83
commit 97acf16de2
3 changed files with 41 additions and 35 deletions
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14
scripts/parse_tutorials.py
View File

@ -9,7 +9,6 @@ import nbformat
from bs4 import BeautifulSoup
from nbconvert import HTMLExporter, ScriptExporter
TEMPLATE = """const CWD = process.cwd();
const React = require('react');
@ -43,7 +42,9 @@ def gen_tutorials(repo_dir: str) -> None:
Also create ipynb and py versions of tutorial in Docusaurus site for
download.
"""
with open(os.path.join(repo_dir, "website", "tutorials.json"), "r") as infile:
with open(
os.path.join(repo_dir, "website", "tutorials.json"), "r"
) as infile:
tutorial_config = json.loads(infile.read())
tutorial_ids = {x["id"] for v in tutorial_config.values() for x in v}
@ -52,7 +53,9 @@ def gen_tutorials(repo_dir: str) -> None:
print("Generating {} tutorial".format(tid))
# convert notebook to HTML
ipynb_in_path = os.path.join(repo_dir, "docs", "tutorials", "{}.ipynb".format(tid))
ipynb_in_path = os.path.join(
repo_dir, "docs", "tutorials", "{}.ipynb".format(tid)
)
with open(ipynb_in_path, "r") as infile:
nb_str = infile.read()
nb = nbformat.reads(nb_str, nbformat.NO_CONVERT)
@ -105,7 +108,10 @@ if __name__ == "__main__":
description="Generate JS, HTML, ipynb, and py files for tutorials."
)
parser.add_argument(
"--repo_dir", metavar="path", required=True, help="Pytorch3D repo directory."
"--repo_dir",
metavar="path",
required=True,
help="Pytorch3D repo directory.",
)
args = parser.parse_args()
gen_tutorials(args.repo_dir)

6
tests/test_nearest_neighbor_points.py
View File

@ -43,21 +43,21 @@ class TestNearestNeighborPoints(unittest.TestCase):
"""
Test cuda output vs naive python implementation.
"""
device = torch.device('cuda:0')
device = torch.device("cuda:0")
self._test_nn_helper(device)
def test_nn_cpu(self):
"""
Test cpu output vs naive python implementation
"""
device = torch.device('cpu')
device = torch.device("cpu")
self._test_nn_helper(device)
@staticmethod
def bm_nn_points_cpu_with_init(
N: int = 4, D: int = 4, P1: int = 128, P2: int = 128
):
device = torch.device('cpu')
device = torch.device("cpu")
x = torch.randn(N, P1, D, device=device)
y = torch.randn(N, P2, D, device=device)