mirror of
https://github.com/facebookresearch/pytorch3d.git
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474 lines
16 KiB
Python
474 lines
16 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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import unittest
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from pathlib import Path
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import torch
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from pytorch3d import _C
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from pytorch3d.ops.sample_points_from_meshes import sample_points_from_meshes
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from pytorch3d.structures.meshes import Meshes
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from pytorch3d.utils.ico_sphere import ico_sphere
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class TestSamplePoints(unittest.TestCase):
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def setUp(self) -> None:
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super().setUp()
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torch.manual_seed(1)
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@staticmethod
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def init_meshes(
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num_meshes: int = 10,
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num_verts: int = 1000,
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num_faces: int = 3000,
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device: str = "cpu",
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):
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device = torch.device(device)
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verts_list = []
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faces_list = []
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for _ in range(num_meshes):
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verts = torch.rand(
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(num_verts, 3), dtype=torch.float32, device=device
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)
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faces = torch.randint(
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num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
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)
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verts_list.append(verts)
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faces_list.append(faces)
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meshes = Meshes(verts_list, faces_list)
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return meshes
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def test_all_empty_meshes(self):
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"""
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Check sample_points_from_meshes raises an exception if all meshes are
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invalid.
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"""
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device = torch.device("cuda:0")
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verts1 = torch.tensor([], dtype=torch.float32, device=device)
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faces1 = torch.tensor([], dtype=torch.int64, device=device)
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meshes = Meshes(
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verts=[verts1, verts1, verts1], faces=[faces1, faces1, faces1]
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)
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with self.assertRaises(ValueError) as err:
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sample_points_from_meshes(
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meshes, num_samples=100, return_normals=True
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)
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self.assertTrue("Meshes are empty." in str(err.exception))
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def test_sampling_output(self):
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"""
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Check outputs of sampling are correct for different meshes.
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For an ico_sphere, the sampled vertices should lie on a unit sphere.
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For an empty mesh, the samples and normals should be 0.
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"""
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device = torch.device("cuda:0")
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# Unit simplex.
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verts_pyramid = torch.tensor(
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[
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[0.0, 0.0, 0.0],
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[1.0, 0.0, 0.0],
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[0.0, 1.0, 0.0],
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[0.0, 0.0, 1.0],
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],
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dtype=torch.float32,
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device=device,
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)
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faces_pyramid = torch.tensor(
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[[0, 1, 2], [0, 2, 3], [0, 1, 3], [1, 2, 3]],
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dtype=torch.int64,
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device=device,
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)
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sphere_mesh = ico_sphere(9, device)
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verts_sphere, faces_sphere = sphere_mesh.get_mesh_verts_faces(0)
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verts_empty = torch.tensor([], dtype=torch.float32, device=device)
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faces_empty = torch.tensor([], dtype=torch.int64, device=device)
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num_samples = 10
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meshes = Meshes(
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verts=[verts_empty, verts_sphere, verts_pyramid],
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faces=[faces_empty, faces_sphere, faces_pyramid],
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)
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samples, normals = sample_points_from_meshes(
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meshes, num_samples=num_samples, return_normals=True
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)
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samples = samples.cpu()
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normals = normals.cpu()
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self.assertEqual(samples.shape, (3, num_samples, 3))
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self.assertEqual(normals.shape, (3, num_samples, 3))
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# Empty meshes: should have all zeros for samples and normals.
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self.assertTrue(
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torch.allclose(samples[0, :], torch.zeros((1, num_samples, 3)))
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)
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self.assertTrue(
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torch.allclose(normals[0, :], torch.zeros((1, num_samples, 3)))
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)
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# Sphere: points should have radius 1.
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x, y, z = samples[1, :].unbind(1)
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radius = torch.sqrt(x ** 2 + y ** 2 + z ** 2)
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self.assertTrue(torch.allclose(radius, torch.ones((num_samples))))
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# Pyramid: points shoudl lie on one of the faces.
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pyramid_verts = samples[2, :]
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pyramid_normals = normals[2, :]
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self.assertTrue(
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torch.allclose(
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pyramid_verts.lt(1).float(), torch.ones_like(pyramid_verts)
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)
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)
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self.assertTrue(
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torch.allclose(
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(pyramid_verts >= 0).float(), torch.ones_like(pyramid_verts)
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)
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)
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# Face 1: z = 0, x + y <= 1, normals = (0, 0, 1).
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face_1_idxs = pyramid_verts[:, 2] == 0
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face_1_verts, face_1_normals = (
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pyramid_verts[face_1_idxs, :],
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pyramid_normals[face_1_idxs, :],
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)
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self.assertTrue(
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torch.all((face_1_verts[:, 0] + face_1_verts[:, 1]) <= 1)
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)
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self.assertTrue(
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torch.allclose(
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face_1_normals,
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torch.tensor([0, 0, 1], dtype=torch.float32).expand(
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face_1_normals.size()
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),
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)
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)
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# Face 2: x = 0, z + y <= 1, normals = (1, 0, 0).
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face_2_idxs = pyramid_verts[:, 0] == 0
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face_2_verts, face_2_normals = (
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pyramid_verts[face_2_idxs, :],
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pyramid_normals[face_2_idxs, :],
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)
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self.assertTrue(
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torch.all((face_2_verts[:, 1] + face_2_verts[:, 2]) <= 1)
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)
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self.assertTrue(
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torch.allclose(
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face_2_normals,
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torch.tensor([1, 0, 0], dtype=torch.float32).expand(
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face_2_normals.size()
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),
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)
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)
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# Face 3: y = 0, x + z <= 1, normals = (0, -1, 0).
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face_3_idxs = pyramid_verts[:, 1] == 0
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face_3_verts, face_3_normals = (
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pyramid_verts[face_3_idxs, :],
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pyramid_normals[face_3_idxs, :],
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)
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self.assertTrue(
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torch.all((face_3_verts[:, 0] + face_3_verts[:, 2]) <= 1)
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)
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self.assertTrue(
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torch.allclose(
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face_3_normals,
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torch.tensor([0, -1, 0], dtype=torch.float32).expand(
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face_3_normals.size()
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),
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)
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)
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# Face 4: x + y + z = 1, normals = (1, 1, 1)/sqrt(3).
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face_4_idxs = pyramid_verts.gt(0).all(1)
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face_4_verts, face_4_normals = (
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pyramid_verts[face_4_idxs, :],
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pyramid_normals[face_4_idxs, :],
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)
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self.assertTrue(
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torch.allclose(
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face_4_verts.sum(1), torch.ones(face_4_verts.size(0))
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)
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)
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self.assertTrue(
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torch.allclose(
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face_4_normals,
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(
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torch.tensor([1, 1, 1], dtype=torch.float32)
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/ torch.sqrt(torch.tensor(3, dtype=torch.float32))
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).expand(face_4_normals.size()),
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)
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)
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def test_mutinomial(self):
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"""
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Confirm that torch.multinomial does not sample elements which have
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zero probability.
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"""
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freqs = torch.cuda.FloatTensor(
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[
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.03178183361887932,
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0.027680952101945877,
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0.033176131546497345,
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0.046052902936935425,
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0.07742464542388916,
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0.11543981730937958,
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0.14148041605949402,
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0.15784293413162231,
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0.13180233538150787,
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0.08271478116512299,
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0.049702685326337814,
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0.027557924389839172,
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0.018125897273421288,
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0.011851548217236996,
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0.010252203792333603,
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0.007422595750540495,
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0.005372154992073774,
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0.0045109698548913,
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0.0036087757907807827,
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0.0035267581697553396,
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0.0018864056328311563,
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0.0024605290964245796,
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0.0022964938543736935,
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0.0018453967059031129,
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0.0010662291897460818,
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0.0009842115687206388,
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0.00045109697384759784,
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0.0007791675161570311,
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0.00020504408166743815,
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0.00020504408166743815,
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0.00020504408166743815,
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0.00012302644609007984,
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0.0,
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0.00012302644609007984,
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4.100881778867915e-05,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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0.0,
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]
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)
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sample = []
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for _ in range(1000):
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torch.cuda.get_rng_state()
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sample = torch.multinomial(freqs, 1000, True)
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if freqs[sample].min() == 0:
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sample_idx = (freqs[sample] == 0).nonzero()[0][0]
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sampled = sample[sample_idx]
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print(
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"%s th element of last sample was %s, which has probability %s"
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% (sample_idx, sampled, freqs[sampled])
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)
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return False
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return True
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def test_multinomial_weights(self):
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"""
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Confirm that torch.multinomial does not sample elements which have
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zero probability using a real example of input from a training run.
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"""
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weights = torch.load(Path(__file__).resolve().parent / "weights.pt")
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S = 4096
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num_trials = 100
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for _ in range(0, num_trials):
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weights[weights < 0] = 0.0
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samples = weights.multinomial(S, replacement=True)
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sampled_weights = weights[samples]
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assert sampled_weights.min() > 0
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if sampled_weights.min() <= 0:
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return False
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return True
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@staticmethod
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def face_areas(verts, faces):
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"""
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Vectorized PyTorch implementation of triangle face area function.
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"""
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verts_faces = verts[faces]
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v0x = verts_faces[:, 0::3, 0]
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v0y = verts_faces[:, 0::3, 1]
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v0z = verts_faces[:, 0::3, 2]
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v1x = verts_faces[:, 1::3, 0]
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v1y = verts_faces[:, 1::3, 1]
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v1z = verts_faces[:, 1::3, 2]
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v2x = verts_faces[:, 2::3, 0]
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v2y = verts_faces[:, 2::3, 1]
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v2z = verts_faces[:, 2::3, 2]
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ax = v0x - v2x
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ay = v0y - v2y
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az = v0z - v2z
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bx = v1x - v2x
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by = v1y - v2y
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bz = v1z - v2z
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cx = ay * bz - az * by
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cy = az * bx - ax * bz
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cz = ax * by - ay * bx
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# this gives the area of the parallelogram with sides a and b
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area_sqr = cx * cx + cy * cy + cz * cz
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# the area of the triangle is half
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return torch.sqrt(area_sqr) / 2.0
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def test_face_areas(self):
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"""
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Check the results from face_areas cuda and PyTorch implementions are
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the same. Check that face_areas throws an error if cpu tensors are
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given as input.
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"""
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meshes = self.init_meshes(10, 1000, 3000, device="cuda:0")
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verts = meshes.verts_packed()
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faces = meshes.faces_packed()
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areas_torch = self.face_areas(verts, faces).squeeze()
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areas_cuda, _ = _C.face_areas_normals(verts, faces)
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self.assertTrue(torch.allclose(areas_torch, areas_cuda, atol=5e-8))
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with self.assertRaises(Exception) as err:
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_C.face_areas_normals(verts.cpu(), faces.cpu())
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self.assertTrue("Not implemented on the CPU" in str(err.exception))
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@staticmethod
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def packed_to_padded_tensor(inputs, first_idxs, max_size):
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"""
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PyTorch implementation of cuda packed_to_padded_tensor function.
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"""
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num_meshes = first_idxs.size(0)
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inputs_padded = torch.zeros((num_meshes, max_size))
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for m in range(num_meshes):
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s = first_idxs[m]
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if m == num_meshes - 1:
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f = inputs.size(0)
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else:
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f = first_idxs[m + 1]
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inputs_padded[m, :f] = inputs[s:f]
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return inputs_padded
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def test_packed_to_padded_tensor(self):
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"""
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Check the results from packed_to_padded cuda and PyTorch implementions
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are the same.
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"""
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meshes = self.init_meshes(1, 3, 5, device="cuda:0")
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verts = meshes.verts_packed()
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faces = meshes.faces_packed()
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mesh_to_faces_packed_first_idx = meshes.mesh_to_faces_packed_first_idx()
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max_faces = meshes.num_faces_per_mesh().max().item()
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areas, _ = _C.face_areas_normals(verts, faces)
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areas_padded = _C.packed_to_padded_tensor(
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areas, mesh_to_faces_packed_first_idx, max_faces
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).cpu()
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areas_padded_cpu = TestSamplePoints.packed_to_padded_tensor(
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areas, mesh_to_faces_packed_first_idx, max_faces
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)
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self.assertTrue(torch.allclose(areas_padded, areas_padded_cpu))
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with self.assertRaises(Exception) as err:
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_C.packed_to_padded_tensor(
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areas.cpu(), mesh_to_faces_packed_first_idx, max_faces
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)
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self.assertTrue("Not implemented on the CPU" in str(err.exception))
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@staticmethod
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def sample_points_with_init(
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num_meshes: int,
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num_verts: int,
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num_faces: int,
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num_samples: int,
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device: str = "cpu",
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):
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device = torch.device(device)
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verts_list = []
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faces_list = []
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for _ in range(num_meshes):
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verts = torch.rand(
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(num_verts, 3), dtype=torch.float32, device=device
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)
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faces = torch.randint(
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num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
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)
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verts_list.append(verts)
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faces_list.append(faces)
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meshes = Meshes(verts_list, faces_list)
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torch.cuda.synchronize()
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def sample_points():
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sample_points_from_meshes(
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meshes, num_samples=num_samples, return_normals=True
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)
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torch.cuda.synchronize()
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return sample_points
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@staticmethod
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def face_areas_with_init(
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num_meshes: int, num_verts: int, num_faces: int, cuda: str = True
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):
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device = "cuda" if cuda else "cpu"
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meshes = TestSamplePoints.init_meshes(
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num_meshes, num_verts, num_faces, device
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)
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verts = meshes.verts_packed()
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faces = meshes.faces_packed()
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torch.cuda.synchronize()
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def face_areas():
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if cuda:
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_C.face_areas_normals(verts, faces)
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else:
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TestSamplePoints.face_areas(verts, faces)
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torch.cuda.synchronize()
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return face_areas
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@staticmethod
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def packed_to_padded_with_init(
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num_meshes: int, num_verts: int, num_faces: int, cuda: str = True
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):
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device = "cuda" if cuda else "cpu"
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meshes = TestSamplePoints.init_meshes(
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num_meshes, num_verts, num_faces, device
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)
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verts = meshes.verts_packed()
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faces = meshes.faces_packed()
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mesh_to_faces_packed_first_idx = meshes.mesh_to_faces_packed_first_idx()
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max_faces = meshes.num_faces_per_mesh().max().item()
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if cuda:
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areas, _ = _C.face_areas_normals(verts, faces)
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else:
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areas = TestSamplePoints.face_areas(verts, faces)
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torch.cuda.synchronize()
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def packed_to_padded():
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if cuda:
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_C.packed_to_padded_tensor(
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areas, mesh_to_faces_packed_first_idx, max_faces
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)
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else:
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TestSamplePoints.packed_to_padded_tensor(
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areas, mesh_to_faces_packed_first_idx, max_faces
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)
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torch.cuda.synchronize()
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return packed_to_padded
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