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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from pytorch3d.ops import mesh_face_areas_normals
from pytorch3d.structures.meshes import Meshes
from .common_testing import get_random_cuda_device, TestCaseMixin
class TestFaceAreasNormals(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, requires_grad=True
)
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
@staticmethod
def face_areas_normals_python(verts, faces):
"""
Pytorch implementation for face areas & normals.
"""
# TODO(gkioxari) Change cast to floats once we add support for doubles.
verts = verts.float()
vertices_faces = verts[faces] # (F, 3, 3)
# vector pointing from v0 to v1
v01 = vertices_faces[:, 1] - vertices_faces[:, 0]
# vector pointing from v0 to v2
v02 = vertices_faces[:, 2] - vertices_faces[:, 0]
normals = torch.cross(v01, v02, dim=1) # (F, 3)
face_areas = normals.norm(dim=-1) / 2
face_normals = torch.nn.functional.normalize(normals, p=2, dim=1, eps=1e-6)
return face_areas, face_normals
def _test_face_areas_normals_helper(self, device, dtype=torch.float32):
"""
Check the results from face_areas cuda/cpp and PyTorch implementation are
the same.
"""
meshes = self.init_meshes(10, 200, 400, device=device)
# make them leaf nodes
verts = meshes.verts_packed().detach().clone().to(dtype)
verts.requires_grad = True
faces = meshes.faces_packed().detach().clone()
# forward
areas, normals = mesh_face_areas_normals(verts, faces)
verts_torch = verts.detach().clone().to(dtype)
verts_torch.requires_grad = True
faces_torch = faces.detach().clone()
(areas_torch, normals_torch) = TestFaceAreasNormals.face_areas_normals_python(
verts_torch, faces_torch
)
self.assertClose(areas_torch, areas, atol=1e-7)
# normals get normalized by area thus sensitivity increases as areas
# in our tests can be arbitrarily small. Thus we compare normals after
# multiplying with areas
unnormals = normals * areas.view(-1, 1)
unnormals_torch = normals_torch * areas_torch.view(-1, 1)
self.assertClose(unnormals_torch, unnormals, atol=1e-6)
# backward
grad_areas = torch.rand(areas.shape, device=device, dtype=dtype)
grad_normals = torch.rand(normals.shape, device=device, dtype=dtype)
areas.backward((grad_areas, grad_normals))
grad_verts = verts.grad
areas_torch.backward((grad_areas, grad_normals))
grad_verts_torch = verts_torch.grad
self.assertClose(grad_verts_torch, grad_verts, atol=1e-6)
def test_face_areas_normals_cpu(self):
self._test_face_areas_normals_helper("cpu")
def test_face_areas_normals_cuda(self):
device = get_random_cuda_device()
self._test_face_areas_normals_helper(device)
def test_nonfloats_cpu(self):
self._test_face_areas_normals_helper("cpu", dtype=torch.double)
def test_nonfloats_cuda(self):
device = get_random_cuda_device()
self._test_face_areas_normals_helper(device, dtype=torch.double)
@staticmethod
def face_areas_normals_with_init(
num_meshes: int, num_verts: int, num_faces: int, device: str = "cpu"
):
meshes = TestFaceAreasNormals.init_meshes(
num_meshes, num_verts, num_faces, device
)
verts = meshes.verts_packed()
faces = meshes.faces_packed()
torch.cuda.synchronize()
def face_areas_normals():
mesh_face_areas_normals(verts, faces)
torch.cuda.synchronize()
return face_areas_normals
@staticmethod
def face_areas_normals_with_init_torch(
num_meshes: int, num_verts: int, num_faces: int, device: str = "cpu"
):
meshes = TestFaceAreasNormals.init_meshes(
num_meshes, num_verts, num_faces, device
)
verts = meshes.verts_packed()
faces = meshes.faces_packed()
torch.cuda.synchronize()
def face_areas_normals():
TestFaceAreasNormals.face_areas_normals_python(verts, faces)
torch.cuda.synchronize()
return face_areas_normals
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