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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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73
tests/test_sample_points_from_meshes.py
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@@ -3,14 +3,13 @@
import unittest
from pathlib import Path
import torch
import torch
from common_testing import TestCaseMixin
from pytorch3d.ops import sample_points_from_meshes
from pytorch3d.structures.meshes import Meshes
from pytorch3d.utils.ico_sphere import ico_sphere
from common_testing import TestCaseMixin
class TestSamplePoints(TestCaseMixin, unittest.TestCase):
def setUp(self) -> None:
@@ -28,9 +27,7 @@ class TestSamplePoints(TestCaseMixin, 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
)
@@ -48,13 +45,9 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
device = torch.device("cuda:0")
verts1 = torch.tensor([], dtype=torch.float32, device=device)
faces1 = torch.tensor([], dtype=torch.int64, device=device)
meshes = Meshes(
verts=[verts1, verts1, verts1], faces=[faces1, faces1, faces1]
)
meshes = Meshes(verts=[verts1, verts1, verts1], faces=[faces1, faces1, faces1])
with self.assertRaises(ValueError) as err:
sample_points_from_meshes(
meshes, num_samples=100, return_normals=True
)
sample_points_from_meshes(meshes, num_samples=100, return_normals=True)
self.assertTrue("Meshes are empty." in str(err.exception))
def test_sampling_output(self):
@@ -67,12 +60,7 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
# Unit simplex.
verts_pyramid = torch.tensor(
[
[0.0, 0.0, 0.0],
[1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0],
],
[[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]],
dtype=torch.float32,
device=device,
)
@@ -113,12 +101,8 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
pyramid_verts = samples[2, :]
pyramid_normals = normals[2, :]
self.assertClose(
pyramid_verts.lt(1).float(), torch.ones_like(pyramid_verts)
)
self.assertClose(
(pyramid_verts >= 0).float(), torch.ones_like(pyramid_verts)
)
self.assertClose(pyramid_verts.lt(1).float(), torch.ones_like(pyramid_verts))
self.assertClose((pyramid_verts >= 0).float(), torch.ones_like(pyramid_verts))
# Face 1: z = 0, x + y <= 1, normals = (0, 0, 1).
face_1_idxs = pyramid_verts[:, 2] == 0
@@ -126,14 +110,10 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
pyramid_verts[face_1_idxs, :],
pyramid_normals[face_1_idxs, :],
)
self.assertTrue(
torch.all((face_1_verts[:, 0] + face_1_verts[:, 1]) <= 1)
)
self.assertTrue(torch.all((face_1_verts[:, 0] + face_1_verts[:, 1]) <= 1))
self.assertClose(
face_1_normals,
torch.tensor([0, 0, 1], dtype=torch.float32).expand(
face_1_normals.size()
),
torch.tensor([0, 0, 1], dtype=torch.float32).expand(face_1_normals.size()),
)
# Face 2: x = 0, z + y <= 1, normals = (1, 0, 0).
@@ -142,14 +122,10 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
pyramid_verts[face_2_idxs, :],
pyramid_normals[face_2_idxs, :],
)
self.assertTrue(
torch.all((face_2_verts[:, 1] + face_2_verts[:, 2]) <= 1)
)
self.assertTrue(torch.all((face_2_verts[:, 1] + face_2_verts[:, 2]) <= 1))
self.assertClose(
face_2_normals,
torch.tensor([1, 0, 0], dtype=torch.float32).expand(
face_2_normals.size()
),
torch.tensor([1, 0, 0], dtype=torch.float32).expand(face_2_normals.size()),
)
# Face 3: y = 0, x + z <= 1, normals = (0, -1, 0).
@@ -158,14 +134,10 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
pyramid_verts[face_3_idxs, :],
pyramid_normals[face_3_idxs, :],
)
self.assertTrue(
torch.all((face_3_verts[:, 0] + face_3_verts[:, 2]) <= 1)
)
self.assertTrue(torch.all((face_3_verts[:, 0] + face_3_verts[:, 2]) <= 1))
self.assertClose(
face_3_normals,
torch.tensor([0, -1, 0], dtype=torch.float32).expand(
face_3_normals.size()
),
torch.tensor([0, -1, 0], dtype=torch.float32).expand(face_3_normals.size()),
)
# Face 4: x + y + z = 1, normals = (1, 1, 1)/sqrt(3).
@@ -279,22 +251,15 @@ class TestSamplePoints(TestCaseMixin, unittest.TestCase):
num_faces = 50
for device in ["cpu", "cuda:0"]:
for invalid in ["nan", "inf"]:
verts = torch.rand(
(num_verts, 3), dtype=torch.float32, device=device
)
verts = torch.rand((num_verts, 3), dtype=torch.float32, device=device)
# randomly assign an invalid type
verts[torch.randperm(num_verts)[:10]] = float(invalid)
faces = torch.randint(
num_verts,
size=(num_faces, 3),
dtype=torch.int64,
device=device,
num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
)
meshes = Meshes(verts=[verts], faces=[faces])
with self.assertRaisesRegex(
ValueError, "Meshes contain nan or inf."
):
with self.assertRaisesRegex(ValueError, "Meshes contain nan or inf."):
sample_points_from_meshes(
meshes, num_samples=100, return_normals=True
)
@@ -310,9 +275,7 @@ class TestSamplePoints(TestCaseMixin, 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
)