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Address black + isort fbsource linter warnings

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Summary: Address black + isort fbsource linter warnings from D20558374 (previous diff)

Reviewed By: nikhilaravi

Differential Revision: D20558373

fbshipit-source-id: d3607de4a01fb24c0d5269634563a7914bddf1c8
This commit is contained in:
Patrick Labatut
2020-03-29 14:46:33 -07:00
committed by Facebook GitHub Bot
parent eb512ffde3
commit d57daa6f85
110 changed files with 705 additions and 1850 deletions
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19
tests/test_face_areas_normals.py
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@@ -2,13 +2,12 @@
import unittest
import torch
import torch
from common_testing import TestCaseMixin
from pytorch3d.ops import mesh_face_areas_normals
from pytorch3d.structures.meshes import Meshes
from common_testing import TestCaseMixin
class TestFaceAreasNormals(TestCaseMixin, unittest.TestCase):
def setUp(self) -> None:
@@ -27,10 +26,7 @@ class TestFaceAreasNormals(TestCaseMixin, unittest.TestCase):
faces_list = []
for _ in range(num_meshes):
verts = torch.rand(
(num_verts, 3),
dtype=torch.float32,
device=device,
requires_grad=True,
(num_verts, 3), dtype=torch.float32, device=device, requires_grad=True
)
faces = torch.randint(
num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
@@ -55,9 +51,7 @@ class TestFaceAreasNormals(TestCaseMixin, unittest.TestCase):
v02 = vertices_faces[:, 2] - vertices_faces[:, 0]
normals = torch.cross(v01, v02, dim=1) # (F, 3)
face_areas = normals.norm(dim=-1) / 2
face_normals = torch.nn.functional.normalize(
normals, p=2, dim=1, eps=1e-6
)
face_normals = torch.nn.functional.normalize(normals, p=2, dim=1, eps=1e-6)
return face_areas, face_normals
def _test_face_areas_normals_helper(self, device, dtype=torch.float32):
@@ -76,10 +70,7 @@ class TestFaceAreasNormals(TestCaseMixin, unittest.TestCase):
verts_torch = verts.detach().clone().to(dtype)
verts_torch.requires_grad = True
faces_torch = faces.detach().clone()
(
areas_torch,
normals_torch,
) = TestFaceAreasNormals.face_areas_normals_python(
(areas_torch, normals_torch) = TestFaceAreasNormals.face_areas_normals_python(
verts_torch, faces_torch
)
self.assertClose(areas_torch, areas, atol=1e-7)