mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 03:42:50 +08:00
34bbb3ad32
Summary: Applies new import merging and sorting from µsort v1.0. When merging imports, µsort will make a best-effort to move associated comments to match merged elements, but there are known limitations due to the diynamic nature of Python and developer tooling. These changes should not produce any dangerous runtime changes, but may require touch-ups to satisfy linters and other tooling. Note that µsort uses case-insensitive, lexicographical sorting, which results in a different ordering compared to isort. This provides a more consistent sorting order, matching the case-insensitive order used when sorting import statements by module name, and ensures that "frog", "FROG", and "Frog" always sort next to each other. For details on µsort's sorting and merging semantics, see the user guide: https://usort.readthedocs.io/en/stable/guide.html#sorting Reviewed By: bottler Differential Revision: D35553814 fbshipit-source-id: be49bdb6a4c25264ff8d4db3a601f18736d17be1
120 lines
3.8 KiB
Python
120 lines
3.8 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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import unittest
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import torch
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from common_testing import get_random_cuda_device, TestCaseMixin
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from pytorch3d.ops import cot_laplacian, laplacian, norm_laplacian
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from pytorch3d.structures.meshes import Meshes
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class TestLaplacianMatrices(TestCaseMixin, 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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def init_mesh(self) -> Meshes:
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V, F = 32, 64
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device = get_random_cuda_device()
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# random vertices
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verts = torch.rand((V, 3), dtype=torch.float32, device=device)
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# random valid faces (no self circles, e.g. (v0, v0, v1))
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faces = torch.stack([torch.randperm(V) for f in range(F)], dim=0)[:, :3]
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faces = faces.to(device=device)
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return Meshes(verts=[verts], faces=[faces])
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def test_laplacian(self):
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mesh = self.init_mesh()
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verts = mesh.verts_packed()
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edges = mesh.edges_packed()
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V, E = verts.shape[0], edges.shape[0]
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L = laplacian(verts, edges)
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Lnaive = torch.zeros((V, V), dtype=torch.float32, device=verts.device)
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for e in range(E):
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e0, e1 = edges[e]
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Lnaive[e0, e1] = 1
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# symetric
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Lnaive[e1, e0] = 1
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deg = Lnaive.sum(1).view(-1, 1)
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deg[deg > 0] = 1.0 / deg[deg > 0]
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Lnaive = Lnaive * deg
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diag = torch.eye(V, dtype=torch.float32, device=mesh.device)
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Lnaive.masked_fill_(diag > 0, -1)
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self.assertClose(L.to_dense(), Lnaive)
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def test_cot_laplacian(self):
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mesh = self.init_mesh()
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verts = mesh.verts_packed()
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faces = mesh.faces_packed()
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V = verts.shape[0]
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eps = 1e-12
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L, inv_areas = cot_laplacian(verts, faces, eps=eps)
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Lnaive = torch.zeros((V, V), dtype=torch.float32, device=verts.device)
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inv_areas_naive = torch.zeros((V, 1), dtype=torch.float32, device=verts.device)
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for f in faces:
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v0 = verts[f[0], :]
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v1 = verts[f[1], :]
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v2 = verts[f[2], :]
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A = (v1 - v2).norm()
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B = (v0 - v2).norm()
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C = (v0 - v1).norm()
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s = 0.5 * (A + B + C)
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face_area = (s * (s - A) * (s - B) * (s - C)).clamp_(min=1e-12).sqrt()
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inv_areas_naive[f[0]] += face_area
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inv_areas_naive[f[1]] += face_area
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inv_areas_naive[f[2]] += face_area
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A2, B2, C2 = A * A, B * B, C * C
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cota = (B2 + C2 - A2) / face_area / 4.0
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cotb = (A2 + C2 - B2) / face_area / 4.0
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cotc = (A2 + B2 - C2) / face_area / 4.0
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Lnaive[f[1], f[2]] += cota
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Lnaive[f[2], f[0]] += cotb
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Lnaive[f[0], f[1]] += cotc
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# symetric
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Lnaive[f[2], f[1]] += cota
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Lnaive[f[0], f[2]] += cotb
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Lnaive[f[1], f[0]] += cotc
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idx = inv_areas_naive > 0
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inv_areas_naive[idx] = 1.0 / inv_areas_naive[idx]
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self.assertClose(inv_areas, inv_areas_naive)
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self.assertClose(L.to_dense(), Lnaive)
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def test_norm_laplacian(self):
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mesh = self.init_mesh()
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verts = mesh.verts_packed()
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edges = mesh.edges_packed()
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V, E = verts.shape[0], edges.shape[0]
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eps = 1e-12
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L = norm_laplacian(verts, edges, eps=eps)
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Lnaive = torch.zeros((V, V), dtype=torch.float32, device=verts.device)
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for e in range(E):
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e0, e1 = edges[e]
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v0 = verts[e0]
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v1 = verts[e1]
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w01 = 1.0 / ((v0 - v1).norm() + eps)
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Lnaive[e0, e1] += w01
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Lnaive[e1, e0] += w01
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self.assertClose(L.to_dense(), Lnaive)
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