mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 03:42:50 +08:00
d57daa6f85
Summary: Address black + isort fbsource linter warnings from D20558374 (previous diff) Reviewed By: nikhilaravi Differential Revision: D20558373 fbshipit-source-id: d3607de4a01fb24c0d5269634563a7914bddf1c8
294 lines
10 KiB
Python
294 lines
10 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
|
|
|
|
|
|
import unittest
|
|
from pathlib import Path
|
|
|
|
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
|
|
|
|
|
|
class TestSamplePoints(TestCaseMixin, unittest.TestCase):
|
|
def setUp(self) -> None:
|
|
super().setUp()
|
|
torch.manual_seed(1)
|
|
|
|
@staticmethod
|
|
def init_meshes(
|
|
num_meshes: int = 10,
|
|
num_verts: int = 1000,
|
|
num_faces: int = 3000,
|
|
device: str = "cpu",
|
|
):
|
|
device = torch.device(device)
|
|
verts_list = []
|
|
faces_list = []
|
|
for _ in range(num_meshes):
|
|
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
|
|
)
|
|
verts_list.append(verts)
|
|
faces_list.append(faces)
|
|
meshes = Meshes(verts_list, faces_list)
|
|
|
|
return meshes
|
|
|
|
def test_all_empty_meshes(self):
|
|
"""
|
|
Check sample_points_from_meshes raises an exception if all meshes are
|
|
invalid.
|
|
"""
|
|
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])
|
|
with self.assertRaises(ValueError) as err:
|
|
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):
|
|
"""
|
|
Check outputs of sampling are correct for different meshes.
|
|
For an ico_sphere, the sampled vertices should lie on a unit sphere.
|
|
For an empty mesh, the samples and normals should be 0.
|
|
"""
|
|
device = torch.device("cuda:0")
|
|
|
|
# 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]],
|
|
dtype=torch.float32,
|
|
device=device,
|
|
)
|
|
faces_pyramid = torch.tensor(
|
|
[[0, 1, 2], [0, 2, 3], [0, 1, 3], [1, 2, 3]],
|
|
dtype=torch.int64,
|
|
device=device,
|
|
)
|
|
sphere_mesh = ico_sphere(9, device)
|
|
verts_sphere, faces_sphere = sphere_mesh.get_mesh_verts_faces(0)
|
|
verts_empty = torch.tensor([], dtype=torch.float32, device=device)
|
|
faces_empty = torch.tensor([], dtype=torch.int64, device=device)
|
|
num_samples = 10
|
|
meshes = Meshes(
|
|
verts=[verts_empty, verts_sphere, verts_pyramid],
|
|
faces=[faces_empty, faces_sphere, faces_pyramid],
|
|
)
|
|
samples, normals = sample_points_from_meshes(
|
|
meshes, num_samples=num_samples, return_normals=True
|
|
)
|
|
samples = samples.cpu()
|
|
normals = normals.cpu()
|
|
|
|
self.assertEqual(samples.shape, (3, num_samples, 3))
|
|
self.assertEqual(normals.shape, (3, num_samples, 3))
|
|
|
|
# Empty meshes: should have all zeros for samples and normals.
|
|
self.assertClose(samples[0, :], torch.zeros((num_samples, 3)))
|
|
self.assertClose(normals[0, :], torch.zeros((num_samples, 3)))
|
|
|
|
# Sphere: points should have radius 1.
|
|
x, y, z = samples[1, :].unbind(1)
|
|
radius = torch.sqrt(x ** 2 + y ** 2 + z ** 2)
|
|
|
|
self.assertClose(radius, torch.ones((num_samples)))
|
|
|
|
# Pyramid: points shoudl lie on one of the faces.
|
|
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))
|
|
|
|
# Face 1: z = 0, x + y <= 1, normals = (0, 0, 1).
|
|
face_1_idxs = pyramid_verts[:, 2] == 0
|
|
face_1_verts, face_1_normals = (
|
|
pyramid_verts[face_1_idxs, :],
|
|
pyramid_normals[face_1_idxs, :],
|
|
)
|
|
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()),
|
|
)
|
|
|
|
# Face 2: x = 0, z + y <= 1, normals = (1, 0, 0).
|
|
face_2_idxs = pyramid_verts[:, 0] == 0
|
|
face_2_verts, face_2_normals = (
|
|
pyramid_verts[face_2_idxs, :],
|
|
pyramid_normals[face_2_idxs, :],
|
|
)
|
|
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()),
|
|
)
|
|
|
|
# Face 3: y = 0, x + z <= 1, normals = (0, -1, 0).
|
|
face_3_idxs = pyramid_verts[:, 1] == 0
|
|
face_3_verts, face_3_normals = (
|
|
pyramid_verts[face_3_idxs, :],
|
|
pyramid_normals[face_3_idxs, :],
|
|
)
|
|
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()),
|
|
)
|
|
|
|
# Face 4: x + y + z = 1, normals = (1, 1, 1)/sqrt(3).
|
|
face_4_idxs = pyramid_verts.gt(0).all(1)
|
|
face_4_verts, face_4_normals = (
|
|
pyramid_verts[face_4_idxs, :],
|
|
pyramid_normals[face_4_idxs, :],
|
|
)
|
|
self.assertClose(face_4_verts.sum(1), torch.ones(face_4_verts.size(0)))
|
|
self.assertClose(
|
|
face_4_normals,
|
|
(
|
|
torch.tensor([1, 1, 1], dtype=torch.float32)
|
|
/ torch.sqrt(torch.tensor(3, dtype=torch.float32))
|
|
).expand(face_4_normals.size()),
|
|
)
|
|
|
|
def test_multinomial(self):
|
|
"""
|
|
Confirm that torch.multinomial does not sample elements which have
|
|
zero probability.
|
|
"""
|
|
freqs = torch.cuda.FloatTensor(
|
|
[
|
|
0.0,
|
|
0.0,
|
|
0.0,
|
|
0.0,
|
|
0.0,
|
|
0.0,
|
|
0.0,
|
|
0.0,
|
|
0.0,
|
|
0.03178183361887932,
|
|
0.027680952101945877,
|
|
0.033176131546497345,
|
|
0.046052902936935425,
|
|
0.07742464542388916,
|
|
0.11543981730937958,
|
|
0.14148041605949402,
|
|
0.15784293413162231,
|
|
0.13180233538150787,
|
|
0.08271478116512299,
|
|
0.049702685326337814,
|
|
0.027557924389839172,
|
|
0.018125897273421288,
|
|
0.011851548217236996,
|
|
0.010252203792333603,
|
|
0.007422595750540495,
|
|
0.005372154992073774,
|
|
0.0045109698548913,
|
|
0.0036087757907807827,
|
|
0.0035267581697553396,
|
|
0.0018864056328311563,
|
|
0.0024605290964245796,
|
|
0.0022964938543736935,
|
|
0.0018453967059031129,
|
|
0.0010662291897460818,
|
|
0.0009842115687206388,
|
|
0.00045109697384759784,
|
|
0.0007791675161570311,
|
|
0.00020504408166743815,
|
|
0.00020504408166743815,
|
|
0.00020504408166743815,
|
|
0.00012302644609007984,
|
|
0.0,
|
|
0.00012302644609007984,
|
|
4.100881778867915e-05,
|
|
0.0,
|
|
0.0,
|
|
0.0,
|
|
0.0,
|
|
0.0,
|
|
0.0,
|
|
]
|
|
)
|
|
|
|
sample = []
|
|
for _ in range(1000):
|
|
torch.cuda.get_rng_state()
|
|
sample = torch.multinomial(freqs, 1000, True)
|
|
if freqs[sample].min() == 0:
|
|
sample_idx = (freqs[sample] == 0).nonzero()[0][0]
|
|
sampled = sample[sample_idx]
|
|
print(
|
|
"%s th element of last sample was %s, which has probability %s"
|
|
% (sample_idx, sampled, freqs[sampled])
|
|
)
|
|
return False
|
|
return True
|
|
|
|
def test_multinomial_weights(self):
|
|
"""
|
|
Confirm that torch.multinomial does not sample elements which have
|
|
zero probability using a real example of input from a training run.
|
|
"""
|
|
weights = torch.load(Path(__file__).resolve().parent / "weights.pt")
|
|
S = 4096
|
|
num_trials = 100
|
|
for _ in range(0, num_trials):
|
|
weights[weights < 0] = 0.0
|
|
samples = weights.multinomial(S, replacement=True)
|
|
sampled_weights = weights[samples]
|
|
assert sampled_weights.min() > 0
|
|
if sampled_weights.min() <= 0:
|
|
return False
|
|
return True
|
|
|
|
def test_verts_nan(self):
|
|
num_verts = 30
|
|
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)
|
|
# 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
|
|
)
|
|
meshes = Meshes(verts=[verts], faces=[faces])
|
|
|
|
with self.assertRaisesRegex(ValueError, "Meshes contain nan or inf."):
|
|
sample_points_from_meshes(
|
|
meshes, num_samples=100, return_normals=True
|
|
)
|
|
|
|
@staticmethod
|
|
def sample_points_with_init(
|
|
num_meshes: int,
|
|
num_verts: int,
|
|
num_faces: int,
|
|
num_samples: int,
|
|
device: str = "cpu",
|
|
):
|
|
verts_list = []
|
|
faces_list = []
|
|
for _ in range(num_meshes):
|
|
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
|
|
)
|
|
verts_list.append(verts)
|
|
faces_list.append(faces)
|
|
meshes = Meshes(verts_list, faces_list)
|
|
torch.cuda.synchronize()
|
|
|
|
def sample_points():
|
|
sample_points_from_meshes(
|
|
meshes, num_samples=num_samples, return_normals=True
|
|
)
|
|
torch.cuda.synchronize()
|
|
|
|
return sample_points
|