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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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20
tests/test_mesh_normal_consistency.py
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@@ -2,8 +2,8 @@
import unittest
import torch
import torch
from pytorch3d.loss.mesh_normal_consistency import mesh_normal_consistency
from pytorch3d.structures.meshes import Meshes
from pytorch3d.utils.ico_sphere import ico_sphere
@@ -33,17 +33,14 @@ class TestMeshNormalConsistency(unittest.TestCase):
return faces
@staticmethod
def init_meshes(
num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000
):
def init_meshes(num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000):
device = torch.device("cuda:0")
valid_faces = TestMeshNormalConsistency.init_faces(num_verts).to(device)
verts_list = []
faces_list = []
for _ in range(num_meshes):
verts = (
torch.rand((num_verts, 3), dtype=torch.float32, device=device)
* 2.0
torch.rand((num_verts, 3), dtype=torch.float32, device=device) * 2.0
- 1.0
) # verts in the space of [-1, 1]
"""
@@ -105,8 +102,7 @@ class TestMeshNormalConsistency(unittest.TestCase):
(
1
- torch.cosine_similarity(
normals[i].view(1, 3),
-normals[j].view(1, 3),
normals[i].view(1, 3), -normals[j].view(1, 3)
)
)
)
@@ -137,9 +133,7 @@ class TestMeshNormalConsistency(unittest.TestCase):
device = torch.device("cuda:0")
# mesh1 shown above
verts1 = torch.rand((4, 3), dtype=torch.float32, device=device)
faces1 = torch.tensor(
[[0, 1, 2], [2, 1, 3]], dtype=torch.int64, device=device
)
faces1 = torch.tensor([[0, 1, 2], [2, 1, 3]], dtype=torch.int64, device=device)
# mesh2 is a cuboid with 8 verts, 12 faces and 18 edges
verts2 = torch.tensor(
@@ -181,9 +175,7 @@ class TestMeshNormalConsistency(unittest.TestCase):
[[0, 1, 2], [2, 1, 3], [2, 1, 4]], dtype=torch.int64, device=device
)
meshes = Meshes(
verts=[verts1, verts2, verts3], faces=[faces1, faces2, faces3]
)
meshes = Meshes(verts=[verts1, verts2, verts3], faces=[faces1, faces2, faces3])
# mesh1: normal consistency computation
n0 = (verts1[1] - verts1[2]).cross(verts1[3] - verts1[2])