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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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25
tests/test_mesh_laplacian_smoothing.py
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@@ -2,8 +2,8 @@
import unittest
import torch
import torch
from pytorch3d.loss.mesh_laplacian_smoothing import mesh_laplacian_smoothing
from pytorch3d.structures.meshes import Meshes
@@ -56,9 +56,7 @@ class TestLaplacianSmoothing(unittest.TestCase):
V = verts_packed.shape[0]
L = torch.zeros((V, V), dtype=torch.float32, device=meshes.device)
inv_areas = torch.zeros(
(V, 1), dtype=torch.float32, device=meshes.device
)
inv_areas = torch.zeros((V, 1), dtype=torch.float32, device=meshes.device)
for f in faces_packed:
v0 = verts_packed[f[0], :]
@@ -69,9 +67,7 @@ class TestLaplacianSmoothing(unittest.TestCase):
C = (v0 - v1).norm()
s = 0.5 * (A + B + C)
face_area = (
(s * (s - A) * (s - B) * (s - C)).clamp_(min=1e-12).sqrt()
)
face_area = (s * (s - A) * (s - B) * (s - C)).clamp_(min=1e-12).sqrt()
inv_areas[f[0]] += face_area
inv_areas[f[1]] += face_area
inv_areas[f[2]] += face_area
@@ -114,16 +110,13 @@ class TestLaplacianSmoothing(unittest.TestCase):
return loss.sum() / len(meshes)
@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")
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]
faces = torch.stack(
@@ -148,9 +141,7 @@ class TestLaplacianSmoothing(unittest.TestCase):
# feats in list
out = mesh_laplacian_smoothing(meshes, method="uniform")
naive_out = TestLaplacianSmoothing.laplacian_smoothing_naive_uniform(
meshes
)
naive_out = TestLaplacianSmoothing.laplacian_smoothing_naive_uniform(meshes)
self.assertTrue(torch.allclose(out, naive_out))
@@ -190,9 +181,7 @@ class TestLaplacianSmoothing(unittest.TestCase):
verts_list = []
faces_list = []
for _ in range(num_meshes):
verts = torch.rand(
(num_verts, 3), dtype=torch.float32, device=device
)
verts = torch.rand((num_verts, 3), dtype=torch.float32, device=device)
faces = torch.randint(
num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
)