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has_verts_normals for Meshes

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Summary: Add ability to ask a Meshes if it already has normals. If it does, then requesting normals will not trigger a calculation. MeshesFormatInterpreters will therefore be able to decide whether to save normals.

Reviewed By: theschnitz, nikhilaravi

Differential Revision: D27765261

fbshipit-source-id: 7c87dbf999d5616d20f5eb2c01039ee5ff65a830
This commit is contained in:
Jeremy Reizenstein 2021-05-04 05:35:24 -07:00 committed by Facebook GitHub Bot
parent 2bbca5f2a7
commit 66b97a0c28
2 changed files with 11 additions and 1 deletions
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6
pytorch3d/structures/meshes.py
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@ -732,6 +732,12 @@ class Meshes(object):
)
return self._verts_padded_to_packed_idx
def has_verts_normals(self) -> bool:
"""
Check whether vertex normals are already present.
"""
return self._verts_normals_packed is not None
def verts_normals_packed(self):
"""
Get the packed representation of the vertex normals.

6
tests/test_meshes.py
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@ -1003,7 +1003,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11]], dtype=torch.int64
)
mesh = Meshes(verts=[verts], faces=[faces])
self.assertFalse(mesh.has_verts_normals())
verts_normals_expected = torch.tensor(
[
[0.0, 0.0, 1.0],
@ -1025,6 +1025,7 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
self.assertTrue(
torch.allclose(mesh.verts_normals_list()[0], verts_normals_expected)
)
self.assertTrue(mesh.has_verts_normals())
self.assertTrue(
torch.allclose(mesh.faces_normals_list()[0], faces_normals_expected)
)
@ -1141,11 +1142,14 @@ class TestMeshes(TestCaseMixin, unittest.TestCase):
def test_assigned_normals(self):
verts = torch.rand(2, 6, 3)
faces = torch.randint(6, size=(2, 4, 3))
no_normals = Meshes(verts=verts, faces=faces)
self.assertFalse(no_normals.has_verts_normals())
for verts_normals in [list(verts.unbind(0)), verts]:
yes_normals = Meshes(
verts=verts.clone(), faces=faces, verts_normals=verts_normals
)
self.assertTrue(yes_normals.has_verts_normals())
self.assertClose(yes_normals.verts_normals_padded(), verts)
yes_normals.offset_verts_(torch.FloatTensor([1, 2, 3]))
self.assertClose(yes_normals.verts_normals_padded(), verts)