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Texturing API updates
Summary: A fairly big refactor of the texturing API with some breaking changes to how textures are defined. Main changes: - There are now 3 types of texture classes: `TexturesUV`, `TexturesAtlas` and `TexturesVertex`. Each class: - has a `sample_textures` function which accepts the `fragments` from rasterization and returns `texels`. This means that the shaders will not need to know the type of the mesh texture which will resolve several issues people were reporting on GitHub. - has a `join_batch` method for joining multiple textures of the same type into a batch Reviewed By: gkioxari Differential Revision: D21067427 fbshipit-source-id: 4b346500a60181e72fdd1b0dd89b5505c7a33926
This commit is contained in:
Nikhila Ravi
committed by
Facebook GitHub Bot
Facebook GitHub Bot
parent
b73d3d6ed9
commit
a3932960b3
@@ -7,14 +7,376 @@ import torch
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import torch.nn.functional as F
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from common_testing import TestCaseMixin
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from pytorch3d.renderer.mesh.rasterizer import Fragments
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from pytorch3d.renderer.mesh.texturing import interpolate_texture_map
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from pytorch3d.structures import Meshes, Textures
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from pytorch3d.structures.utils import list_to_padded
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from pytorch3d.renderer.mesh.textures import (
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TexturesAtlas,
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TexturesUV,
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TexturesVertex,
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_list_to_padded_wrapper,
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)
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from pytorch3d.structures import Meshes, list_to_packed, packed_to_list
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from test_meshes import TestMeshes
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class TestTexturing(TestCaseMixin, unittest.TestCase):
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def test_interpolate_texture_map(self):
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def tryindex(self, index, tex, meshes, source):
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tex2 = tex[index]
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meshes2 = meshes[index]
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tex_from_meshes = meshes2.textures
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for item in source:
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basic = source[item][index]
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from_texture = getattr(tex2, item + "_padded")()
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from_meshes = getattr(tex_from_meshes, item + "_padded")()
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if isinstance(index, int):
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basic = basic[None]
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if len(basic) == 0:
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self.assertEquals(len(from_texture), 0)
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self.assertEquals(len(from_meshes), 0)
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else:
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self.assertClose(basic, from_texture)
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self.assertClose(basic, from_meshes)
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self.assertEqual(from_texture.ndim, getattr(tex, item + "_padded")().ndim)
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item_list = getattr(tex_from_meshes, item + "_list")()
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self.assertEqual(basic.shape[0], len(item_list))
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for i, elem in enumerate(item_list):
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self.assertClose(elem, basic[i])
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class TestTexturesVertex(TestCaseMixin, unittest.TestCase):
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def test_sample_vertex_textures(self):
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"""
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This tests both interpolate_vertex_colors as well as
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interpolate_face_attributes.
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"""
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verts = torch.randn((4, 3), dtype=torch.float32)
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faces = torch.tensor([[2, 1, 0], [3, 1, 0]], dtype=torch.int64)
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vert_tex = torch.tensor(
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[[0, 1, 0], [0, 1, 1], [1, 1, 0], [1, 1, 1]], dtype=torch.float32
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)
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verts_features = vert_tex
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tex = TexturesVertex(verts_features=[verts_features])
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mesh = Meshes(verts=[verts], faces=[faces], textures=tex)
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pix_to_face = torch.tensor([0, 1], dtype=torch.int64).view(1, 1, 1, 2)
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barycentric_coords = torch.tensor(
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[[0.5, 0.3, 0.2], [0.3, 0.6, 0.1]], dtype=torch.float32
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).view(1, 1, 1, 2, -1)
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expected_vals = torch.tensor(
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[[0.5, 1.0, 0.3], [0.3, 1.0, 0.9]], dtype=torch.float32
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).view(1, 1, 1, 2, -1)
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fragments = Fragments(
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pix_to_face=pix_to_face,
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bary_coords=barycentric_coords,
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zbuf=torch.ones_like(pix_to_face),
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dists=torch.ones_like(pix_to_face),
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)
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# sample_textures calls interpolate_vertex_colors
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texels = mesh.sample_textures(fragments)
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self.assertTrue(torch.allclose(texels, expected_vals[None, :]))
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def test_sample_vertex_textures_grad(self):
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verts = torch.randn((4, 3), dtype=torch.float32)
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faces = torch.tensor([[2, 1, 0], [3, 1, 0]], dtype=torch.int64)
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vert_tex = torch.tensor(
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[[0, 1, 0], [0, 1, 1], [1, 1, 0], [1, 1, 1]],
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dtype=torch.float32,
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requires_grad=True,
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)
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verts_features = vert_tex
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tex = TexturesVertex(verts_features=[verts_features])
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mesh = Meshes(verts=[verts], faces=[faces], textures=tex)
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pix_to_face = torch.tensor([0, 1], dtype=torch.int64).view(1, 1, 1, 2)
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barycentric_coords = torch.tensor(
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[[0.5, 0.3, 0.2], [0.3, 0.6, 0.1]], dtype=torch.float32
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).view(1, 1, 1, 2, -1)
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fragments = Fragments(
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pix_to_face=pix_to_face,
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bary_coords=barycentric_coords,
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zbuf=torch.ones_like(pix_to_face),
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dists=torch.ones_like(pix_to_face),
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)
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grad_vert_tex = torch.tensor(
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[[0.3, 0.3, 0.3], [0.9, 0.9, 0.9], [0.5, 0.5, 0.5], [0.3, 0.3, 0.3]],
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dtype=torch.float32,
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)
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texels = mesh.sample_textures(fragments)
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texels.sum().backward()
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self.assertTrue(hasattr(vert_tex, "grad"))
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self.assertTrue(torch.allclose(vert_tex.grad, grad_vert_tex[None, :]))
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def test_textures_vertex_init_fail(self):
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# Incorrect sized tensors
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with self.assertRaisesRegex(ValueError, "verts_features"):
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TexturesVertex(verts_features=torch.rand(size=(5, 10)))
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# Not a list or a tensor
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with self.assertRaisesRegex(ValueError, "verts_features"):
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TexturesVertex(verts_features=(1, 1, 1))
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def test_clone(self):
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tex = TexturesVertex(verts_features=torch.rand(size=(10, 100, 128)))
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tex_cloned = tex.clone()
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self.assertSeparate(
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tex._verts_features_padded, tex_cloned._verts_features_padded
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)
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self.assertSeparate(tex.valid, tex_cloned.valid)
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def test_extend(self):
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B = 10
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mesh = TestMeshes.init_mesh(B, 30, 50)
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V = mesh._V
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tex_uv = TexturesVertex(verts_features=torch.randn((B, V, 3)))
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tex_mesh = Meshes(
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verts=mesh.verts_padded(), faces=mesh.faces_padded(), textures=tex_uv
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)
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N = 20
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new_mesh = tex_mesh.extend(N)
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self.assertEqual(len(tex_mesh) * N, len(new_mesh))
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tex_init = tex_mesh.textures
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new_tex = new_mesh.textures
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for i in range(len(tex_mesh)):
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for n in range(N):
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self.assertClose(
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tex_init.verts_features_list()[i],
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new_tex.verts_features_list()[i * N + n],
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)
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self.assertClose(
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tex_init._num_faces_per_mesh[i],
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new_tex._num_faces_per_mesh[i * N + n],
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)
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self.assertAllSeparate(
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[tex_init.verts_features_padded(), new_tex.verts_features_padded()]
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)
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with self.assertRaises(ValueError):
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tex_mesh.extend(N=-1)
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def test_padded_to_packed(self):
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# Case where each face in the mesh has 3 unique uv vertex indices
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# - i.e. even if a vertex is shared between multiple faces it will
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# have a unique uv coordinate for each face.
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num_verts_per_mesh = [9, 6]
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D = 10
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verts_features_list = [torch.rand(v, D) for v in num_verts_per_mesh]
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verts_features_packed = list_to_packed(verts_features_list)[0]
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verts_features_list = packed_to_list(verts_features_packed, num_verts_per_mesh)
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tex = TexturesVertex(verts_features=verts_features_list)
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# This is set inside Meshes when textures is passed as an input.
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# Here we set _num_faces_per_mesh and _num_verts_per_mesh explicity.
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tex1 = tex.clone()
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tex1._num_verts_per_mesh = num_verts_per_mesh
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verts_packed = tex1.verts_features_packed()
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verts_verts_list = tex1.verts_features_list()
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verts_padded = tex1.verts_features_padded()
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for f1, f2 in zip(verts_verts_list, verts_features_list):
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self.assertTrue((f1 == f2).all().item())
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self.assertTrue(verts_packed.shape == (sum(num_verts_per_mesh), D))
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self.assertTrue(verts_padded.shape == (2, 9, D))
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# Case where num_verts_per_mesh is not set and textures
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# are initialized with a padded tensor.
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tex2 = TexturesVertex(verts_features=verts_padded)
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verts_packed = tex2.verts_features_packed()
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verts_list = tex2.verts_features_list()
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# Packed is just flattened padded as num_verts_per_mesh
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# has not been provided.
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self.assertTrue(verts_packed.shape == (9 * 2, D))
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for i, (f1, f2) in enumerate(zip(verts_list, verts_features_list)):
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n = num_verts_per_mesh[i]
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self.assertTrue((f1[:n] == f2).all().item())
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def test_getitem(self):
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N = 5
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V = 20
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source = {"verts_features": torch.randn(size=(N, 10, 128))}
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tex = TexturesVertex(verts_features=source["verts_features"])
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verts = torch.rand(size=(N, V, 3))
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faces = torch.randint(size=(N, 10, 3), high=V)
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meshes = Meshes(verts=verts, faces=faces, textures=tex)
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tryindex(self, 2, tex, meshes, source)
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tryindex(self, slice(0, 2, 1), tex, meshes, source)
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index = torch.tensor([1, 0, 1, 0, 0], dtype=torch.bool)
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tryindex(self, index, tex, meshes, source)
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index = torch.tensor([0, 0, 0, 0, 0], dtype=torch.bool)
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tryindex(self, index, tex, meshes, source)
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index = torch.tensor([1, 2], dtype=torch.int64)
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tryindex(self, index, tex, meshes, source)
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tryindex(self, [2, 4], tex, meshes, source)
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class TestTexturesAtlas(TestCaseMixin, unittest.TestCase):
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def test_sample_texture_atlas(self):
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N, F, R = 1, 2, 2
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verts = torch.randn((4, 3), dtype=torch.float32)
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faces = torch.tensor([[2, 1, 0], [3, 1, 0]], dtype=torch.int64)
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faces_atlas = torch.rand(size=(N, F, R, R, 3))
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tex = TexturesAtlas(atlas=faces_atlas)
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mesh = Meshes(verts=[verts], faces=[faces], textures=tex)
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pix_to_face = torch.tensor([0, 1], dtype=torch.int64).view(1, 1, 1, 2)
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barycentric_coords = torch.tensor(
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[[0.5, 0.3, 0.2], [0.3, 0.6, 0.1]], dtype=torch.float32
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).view(1, 1, 1, 2, -1)
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expected_vals = torch.tensor(
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[[0.5, 1.0, 0.3], [0.3, 1.0, 0.9]], dtype=torch.float32
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)
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expected_vals = torch.zeros((1, 1, 1, 2, 3), dtype=torch.float32)
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expected_vals[..., 0, :] = faces_atlas[0, 0, 0, 1, ...]
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expected_vals[..., 1, :] = faces_atlas[0, 1, 1, 0, ...]
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fragments = Fragments(
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pix_to_face=pix_to_face,
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bary_coords=barycentric_coords,
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zbuf=torch.ones_like(pix_to_face),
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dists=torch.ones_like(pix_to_face),
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)
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texels = mesh.textures.sample_textures(fragments)
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self.assertTrue(torch.allclose(texels, expected_vals))
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def test_textures_atlas_grad(self):
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N, F, R = 1, 2, 2
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verts = torch.randn((4, 3), dtype=torch.float32)
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faces = torch.tensor([[2, 1, 0], [3, 1, 0]], dtype=torch.int64)
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faces_atlas = torch.rand(size=(N, F, R, R, 3), requires_grad=True)
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tex = TexturesAtlas(atlas=faces_atlas)
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mesh = Meshes(verts=[verts], faces=[faces], textures=tex)
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pix_to_face = torch.tensor([0, 1], dtype=torch.int64).view(1, 1, 1, 2)
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barycentric_coords = torch.tensor(
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[[0.5, 0.3, 0.2], [0.3, 0.6, 0.1]], dtype=torch.float32
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).view(1, 1, 1, 2, -1)
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fragments = Fragments(
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pix_to_face=pix_to_face,
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bary_coords=barycentric_coords,
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zbuf=torch.ones_like(pix_to_face),
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dists=torch.ones_like(pix_to_face),
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)
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texels = mesh.textures.sample_textures(fragments)
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grad_tex = torch.rand_like(texels)
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grad_expected = torch.zeros_like(faces_atlas)
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grad_expected[0, 0, 0, 1, :] = grad_tex[..., 0:1, :]
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grad_expected[0, 1, 1, 0, :] = grad_tex[..., 1:2, :]
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texels.backward(grad_tex)
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self.assertTrue(hasattr(faces_atlas, "grad"))
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self.assertTrue(torch.allclose(faces_atlas.grad, grad_expected))
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def test_textures_atlas_init_fail(self):
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# Incorrect sized tensors
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with self.assertRaisesRegex(ValueError, "atlas"):
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TexturesAtlas(atlas=torch.rand(size=(5, 10, 3)))
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# Not a list or a tensor
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with self.assertRaisesRegex(ValueError, "atlas"):
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TexturesAtlas(atlas=(1, 1, 1))
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def test_clone(self):
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tex = TexturesAtlas(atlas=torch.rand(size=(1, 10, 2, 2, 3)))
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tex_cloned = tex.clone()
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self.assertSeparate(tex._atlas_padded, tex_cloned._atlas_padded)
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self.assertSeparate(tex.valid, tex_cloned.valid)
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def test_extend(self):
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B = 10
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mesh = TestMeshes.init_mesh(B, 30, 50)
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F = mesh._F
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tex_uv = TexturesAtlas(atlas=torch.randn((B, F, 2, 2, 3)))
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tex_mesh = Meshes(
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verts=mesh.verts_padded(), faces=mesh.faces_padded(), textures=tex_uv
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)
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N = 20
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new_mesh = tex_mesh.extend(N)
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self.assertEqual(len(tex_mesh) * N, len(new_mesh))
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tex_init = tex_mesh.textures
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new_tex = new_mesh.textures
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for i in range(len(tex_mesh)):
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for n in range(N):
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self.assertClose(
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tex_init.atlas_list()[i], new_tex.atlas_list()[i * N + n]
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)
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self.assertClose(
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tex_init._num_faces_per_mesh[i],
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new_tex._num_faces_per_mesh[i * N + n],
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)
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self.assertAllSeparate([tex_init.atlas_padded(), new_tex.atlas_padded()])
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with self.assertRaises(ValueError):
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tex_mesh.extend(N=-1)
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def test_padded_to_packed(self):
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# Case where each face in the mesh has 3 unique uv vertex indices
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# - i.e. even if a vertex is shared between multiple faces it will
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# have a unique uv coordinate for each face.
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R = 2
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N = 20
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num_faces_per_mesh = torch.randint(size=(N,), low=0, high=30)
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atlas_list = [torch.rand(f, R, R, 3) for f in num_faces_per_mesh]
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tex = TexturesAtlas(atlas=atlas_list)
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# This is set inside Meshes when textures is passed as an input.
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# Here we set _num_faces_per_mesh explicity.
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tex1 = tex.clone()
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tex1._num_faces_per_mesh = num_faces_per_mesh.tolist()
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atlas_packed = tex1.atlas_packed()
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atlas_list_new = tex1.atlas_list()
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atlas_padded = tex1.atlas_padded()
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for f1, f2 in zip(atlas_list_new, atlas_list):
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self.assertTrue((f1 == f2).all().item())
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sum_F = num_faces_per_mesh.sum()
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max_F = num_faces_per_mesh.max().item()
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self.assertTrue(atlas_packed.shape == (sum_F, R, R, 3))
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self.assertTrue(atlas_padded.shape == (N, max_F, R, R, 3))
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# Case where num_faces_per_mesh is not set and textures
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# are initialized with a padded tensor.
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atlas_list_padded = _list_to_padded_wrapper(atlas_list)
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tex2 = TexturesAtlas(atlas=atlas_list_padded)
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atlas_packed = tex2.atlas_packed()
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atlas_list_new = tex2.atlas_list()
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# Packed is just flattened padded as num_faces_per_mesh
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# has not been provided.
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self.assertTrue(atlas_packed.shape == (N * max_F, R, R, 3))
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for i, (f1, f2) in enumerate(zip(atlas_list_new, atlas_list)):
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n = num_faces_per_mesh[i]
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self.assertTrue((f1[:n] == f2).all().item())
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def test_getitem(self):
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N = 5
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V = 20
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source = {"atlas": torch.randn(size=(N, 10, 4, 4, 3))}
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tex = TexturesAtlas(atlas=source["atlas"])
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verts = torch.rand(size=(N, V, 3))
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faces = torch.randint(size=(N, 10, 3), high=V)
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meshes = Meshes(verts=verts, faces=faces, textures=tex)
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tryindex(self, 2, tex, meshes, source)
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tryindex(self, slice(0, 2, 1), tex, meshes, source)
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index = torch.tensor([1, 0, 1, 0, 0], dtype=torch.bool)
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tryindex(self, index, tex, meshes, source)
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index = torch.tensor([0, 0, 0, 0, 0], dtype=torch.bool)
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tryindex(self, index, tex, meshes, source)
|
||||
index = torch.tensor([1, 2], dtype=torch.int64)
|
||||
tryindex(self, index, tex, meshes, source)
|
||||
tryindex(self, [2, 4], tex, meshes, source)
|
||||
|
||||
|
||||
class TestTexturesUV(TestCaseMixin, unittest.TestCase):
|
||||
def test_sample_textures_uv(self):
|
||||
barycentric_coords = torch.tensor(
|
||||
[[0.5, 0.3, 0.2], [0.3, 0.6, 0.1]], dtype=torch.float32
|
||||
).view(1, 1, 1, 2, -1)
|
||||
@@ -38,11 +400,11 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
|
||||
zbuf=pix_to_face,
|
||||
dists=pix_to_face,
|
||||
)
|
||||
tex = Textures(
|
||||
maps=tex_map, faces_uvs=face_uvs[None, ...], verts_uvs=vert_uvs[None, ...]
|
||||
)
|
||||
|
||||
tex = TexturesUV(maps=tex_map, faces_uvs=[face_uvs], verts_uvs=[vert_uvs])
|
||||
meshes = Meshes(verts=[dummy_verts], faces=[face_uvs], textures=tex)
|
||||
texels = interpolate_texture_map(fragments, meshes)
|
||||
mesh_textures = meshes.textures
|
||||
texels = mesh_textures.sample_textures(fragments)
|
||||
|
||||
# Expected output
|
||||
pixel_uvs = interpolated_uvs * 2.0 - 1.0
|
||||
@@ -53,190 +415,92 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
|
||||
expected_out = F.grid_sample(tex_map, pixel_uvs, align_corners=False)
|
||||
self.assertTrue(torch.allclose(texels.squeeze(), expected_out.squeeze()))
|
||||
|
||||
def test_init_rgb_uv_fail(self):
|
||||
V = 20
|
||||
def test_textures_uv_init_fail(self):
|
||||
# Maps has wrong shape
|
||||
with self.assertRaisesRegex(ValueError, "maps"):
|
||||
Textures(
|
||||
TexturesUV(
|
||||
maps=torch.ones((5, 16, 16, 3, 4)),
|
||||
faces_uvs=torch.randint(size=(5, 10, 3), low=0, high=V),
|
||||
verts_uvs=torch.ones((5, V, 2)),
|
||||
faces_uvs=torch.rand(size=(5, 10, 3)),
|
||||
verts_uvs=torch.rand(size=(5, 15, 2)),
|
||||
)
|
||||
|
||||
# faces_uvs has wrong shape
|
||||
with self.assertRaisesRegex(ValueError, "faces_uvs"):
|
||||
Textures(
|
||||
TexturesUV(
|
||||
maps=torch.ones((5, 16, 16, 3)),
|
||||
faces_uvs=torch.randint(size=(5, 10, 3, 3), low=0, high=V),
|
||||
verts_uvs=torch.ones((5, V, 2)),
|
||||
faces_uvs=torch.rand(size=(5, 10, 3, 3)),
|
||||
verts_uvs=torch.rand(size=(5, 15, 2)),
|
||||
)
|
||||
|
||||
# verts_uvs has wrong shape
|
||||
with self.assertRaisesRegex(ValueError, "verts_uvs"):
|
||||
Textures(
|
||||
TexturesUV(
|
||||
maps=torch.ones((5, 16, 16, 3)),
|
||||
faces_uvs=torch.randint(size=(5, 10, 3), low=0, high=V),
|
||||
verts_uvs=torch.ones((5, V, 2, 3)),
|
||||
)
|
||||
# verts_rgb has wrong shape
|
||||
with self.assertRaisesRegex(ValueError, "verts_rgb"):
|
||||
Textures(verts_rgb=torch.ones((5, 16, 16, 3)))
|
||||
|
||||
# maps provided without verts/faces uvs
|
||||
with self.assertRaisesRegex(ValueError, "faces_uvs and verts_uvs are required"):
|
||||
Textures(maps=torch.ones((5, 16, 16, 3)))
|
||||
|
||||
def test_padded_to_packed(self):
|
||||
N = 2
|
||||
# Case where each face in the mesh has 3 unique uv vertex indices
|
||||
# - i.e. even if a vertex is shared between multiple faces it will
|
||||
# have a unique uv coordinate for each face.
|
||||
faces_uvs_list = [
|
||||
torch.tensor([[0, 1, 2], [3, 5, 4], [7, 6, 8]]),
|
||||
torch.tensor([[0, 1, 2], [3, 4, 5]]),
|
||||
] # (N, 3, 3)
|
||||
verts_uvs_list = [torch.ones(9, 2), torch.ones(6, 2)]
|
||||
faces_uvs_padded = list_to_padded(faces_uvs_list, pad_value=-1)
|
||||
verts_uvs_padded = list_to_padded(verts_uvs_list)
|
||||
tex = Textures(
|
||||
maps=torch.ones((N, 16, 16, 3)),
|
||||
faces_uvs=faces_uvs_padded,
|
||||
verts_uvs=verts_uvs_padded,
|
||||
)
|
||||
|
||||
# This is set inside Meshes when textures is passed as an input.
|
||||
# Here we set _num_faces_per_mesh and _num_verts_per_mesh explicity.
|
||||
tex1 = tex.clone()
|
||||
tex1._num_faces_per_mesh = faces_uvs_padded.gt(-1).all(-1).sum(-1).tolist()
|
||||
tex1._num_verts_per_mesh = torch.tensor([5, 4])
|
||||
faces_packed = tex1.faces_uvs_packed()
|
||||
verts_packed = tex1.verts_uvs_packed()
|
||||
faces_list = tex1.faces_uvs_list()
|
||||
verts_list = tex1.verts_uvs_list()
|
||||
|
||||
for f1, f2 in zip(faces_uvs_list, faces_list):
|
||||
self.assertTrue((f1 == f2).all().item())
|
||||
|
||||
for f, v1, v2 in zip(faces_list, verts_list, verts_uvs_list):
|
||||
idx = f.unique()
|
||||
self.assertTrue((v1[idx] == v2).all().item())
|
||||
|
||||
self.assertTrue(faces_packed.shape == (3 + 2, 3))
|
||||
|
||||
# verts_packed is just flattened verts_padded.
|
||||
# split sizes are not used for verts_uvs.
|
||||
self.assertTrue(verts_packed.shape == (9 * 2, 2))
|
||||
|
||||
# Case where num_faces_per_mesh is not set
|
||||
tex2 = tex.clone()
|
||||
faces_packed = tex2.faces_uvs_packed()
|
||||
verts_packed = tex2.verts_uvs_packed()
|
||||
faces_list = tex2.faces_uvs_list()
|
||||
verts_list = tex2.verts_uvs_list()
|
||||
|
||||
# Packed is just flattened padded as num_faces_per_mesh
|
||||
# has not been provided.
|
||||
self.assertTrue(verts_packed.shape == (9 * 2, 2))
|
||||
self.assertTrue(faces_packed.shape == (3 * 2, 3))
|
||||
|
||||
for i in range(N):
|
||||
self.assertTrue(
|
||||
(faces_list[i] == faces_uvs_padded[i, ...].squeeze()).all().item()
|
||||
faces_uvs=torch.rand(size=(5, 10, 3)),
|
||||
verts_uvs=torch.rand(size=(5, 15, 2, 3)),
|
||||
)
|
||||
|
||||
for i in range(N):
|
||||
self.assertTrue(
|
||||
(verts_list[i] == verts_uvs_padded[i, ...].squeeze()).all().item()
|
||||
# verts has different batch dim to faces
|
||||
with self.assertRaisesRegex(ValueError, "verts_uvs"):
|
||||
TexturesUV(
|
||||
maps=torch.ones((5, 16, 16, 3)),
|
||||
faces_uvs=torch.rand(size=(5, 10, 3)),
|
||||
verts_uvs=torch.rand(size=(8, 15, 2)),
|
||||
)
|
||||
|
||||
# maps has different batch dim to faces
|
||||
with self.assertRaisesRegex(ValueError, "maps"):
|
||||
TexturesUV(
|
||||
maps=torch.ones((8, 16, 16, 3)),
|
||||
faces_uvs=torch.rand(size=(5, 10, 3)),
|
||||
verts_uvs=torch.rand(size=(5, 15, 2)),
|
||||
)
|
||||
|
||||
# verts on different device to faces
|
||||
with self.assertRaisesRegex(ValueError, "verts_uvs"):
|
||||
TexturesUV(
|
||||
maps=torch.ones((5, 16, 16, 3)),
|
||||
faces_uvs=torch.rand(size=(5, 10, 3)),
|
||||
verts_uvs=torch.rand(size=(5, 15, 2, 3), device="cuda"),
|
||||
)
|
||||
|
||||
# maps on different device to faces
|
||||
with self.assertRaisesRegex(ValueError, "map"):
|
||||
TexturesUV(
|
||||
maps=torch.ones((5, 16, 16, 3), device="cuda"),
|
||||
faces_uvs=torch.rand(size=(5, 10, 3)),
|
||||
verts_uvs=torch.rand(size=(5, 15, 2)),
|
||||
)
|
||||
|
||||
def test_clone(self):
|
||||
V = 20
|
||||
tex = Textures(
|
||||
tex = TexturesUV(
|
||||
maps=torch.ones((5, 16, 16, 3)),
|
||||
faces_uvs=torch.randint(size=(5, 10, 3), low=0, high=V),
|
||||
verts_uvs=torch.ones((5, V, 2)),
|
||||
faces_uvs=torch.rand(size=(5, 10, 3)),
|
||||
verts_uvs=torch.rand(size=(5, 15, 2)),
|
||||
)
|
||||
tex_cloned = tex.clone()
|
||||
self.assertSeparate(tex._faces_uvs_padded, tex_cloned._faces_uvs_padded)
|
||||
self.assertSeparate(tex._verts_uvs_padded, tex_cloned._verts_uvs_padded)
|
||||
self.assertSeparate(tex._maps_padded, tex_cloned._maps_padded)
|
||||
|
||||
def test_getitem(self):
|
||||
N = 5
|
||||
V = 20
|
||||
source = {
|
||||
"maps": torch.rand(size=(N, 16, 16, 3)),
|
||||
"faces_uvs": torch.randint(size=(N, 10, 3), low=0, high=V),
|
||||
"verts_uvs": torch.rand((N, V, 2)),
|
||||
}
|
||||
tex = Textures(
|
||||
maps=source["maps"],
|
||||
faces_uvs=source["faces_uvs"],
|
||||
verts_uvs=source["verts_uvs"],
|
||||
)
|
||||
|
||||
verts = torch.rand(size=(N, V, 3))
|
||||
faces = torch.randint(size=(N, 10, 3), high=V)
|
||||
meshes = Meshes(verts=verts, faces=faces, textures=tex)
|
||||
|
||||
def tryindex(index):
|
||||
tex2 = tex[index]
|
||||
meshes2 = meshes[index]
|
||||
tex_from_meshes = meshes2.textures
|
||||
for item in source:
|
||||
basic = source[item][index]
|
||||
from_texture = getattr(tex2, item + "_padded")()
|
||||
from_meshes = getattr(tex_from_meshes, item + "_padded")()
|
||||
if isinstance(index, int):
|
||||
basic = basic[None]
|
||||
self.assertClose(basic, from_texture)
|
||||
self.assertClose(basic, from_meshes)
|
||||
self.assertEqual(
|
||||
from_texture.ndim, getattr(tex, item + "_padded")().ndim
|
||||
)
|
||||
if item == "faces_uvs":
|
||||
faces_uvs_list = tex_from_meshes.faces_uvs_list()
|
||||
self.assertEqual(basic.shape[0], len(faces_uvs_list))
|
||||
for i, faces_uvs in enumerate(faces_uvs_list):
|
||||
self.assertClose(faces_uvs, basic[i])
|
||||
|
||||
tryindex(2)
|
||||
tryindex(slice(0, 2, 1))
|
||||
index = torch.tensor([1, 0, 1, 0, 0], dtype=torch.bool)
|
||||
tryindex(index)
|
||||
index = torch.tensor([0, 0, 0, 0, 0], dtype=torch.bool)
|
||||
tryindex(index)
|
||||
index = torch.tensor([1, 2], dtype=torch.int64)
|
||||
tryindex(index)
|
||||
tryindex([2, 4])
|
||||
|
||||
def test_to(self):
|
||||
V = 20
|
||||
tex = Textures(
|
||||
maps=torch.ones((5, 16, 16, 3)),
|
||||
faces_uvs=torch.randint(size=(5, 10, 3), low=0, high=V),
|
||||
verts_uvs=torch.ones((5, V, 2)),
|
||||
)
|
||||
device = torch.device("cuda:0")
|
||||
tex = tex.to(device)
|
||||
self.assertTrue(tex._faces_uvs_padded.device == device)
|
||||
self.assertTrue(tex._verts_uvs_padded.device == device)
|
||||
self.assertTrue(tex._maps_padded.device == device)
|
||||
self.assertSeparate(tex.valid, tex_cloned.valid)
|
||||
|
||||
def test_extend(self):
|
||||
B = 10
|
||||
B = 5
|
||||
mesh = TestMeshes.init_mesh(B, 30, 50)
|
||||
V = mesh._V
|
||||
F = mesh._F
|
||||
|
||||
# 1. Texture uvs
|
||||
tex_uv = Textures(
|
||||
maps=torch.randn((B, 16, 16, 3)),
|
||||
faces_uvs=torch.randint(size=(B, F, 3), low=0, high=V),
|
||||
verts_uvs=torch.randn((B, V, 2)),
|
||||
num_faces = mesh.num_faces_per_mesh()
|
||||
num_verts = mesh.num_verts_per_mesh()
|
||||
faces_uvs_list = [torch.randint(size=(f, 3), low=0, high=V) for f in num_faces]
|
||||
verts_uvs_list = [torch.rand(v, 2) for v in num_verts]
|
||||
tex_uv = TexturesUV(
|
||||
maps=torch.ones((B, 16, 16, 3)),
|
||||
faces_uvs=faces_uvs_list,
|
||||
verts_uvs=verts_uvs_list,
|
||||
)
|
||||
tex_mesh = Meshes(
|
||||
verts=mesh.verts_padded(), faces=mesh.faces_padded(), textures=tex_uv
|
||||
verts=mesh.verts_list(), faces=mesh.faces_list(), textures=tex_uv
|
||||
)
|
||||
N = 20
|
||||
N = 2
|
||||
new_mesh = tex_mesh.extend(N)
|
||||
|
||||
self.assertEqual(len(tex_mesh) * N, len(new_mesh))
|
||||
@@ -246,56 +510,142 @@ class TestTexturing(TestCaseMixin, unittest.TestCase):
|
||||
|
||||
for i in range(len(tex_mesh)):
|
||||
for n in range(N):
|
||||
self.assertClose(
|
||||
tex_init.verts_uvs_list()[i], new_tex.verts_uvs_list()[i * N + n]
|
||||
)
|
||||
self.assertClose(
|
||||
tex_init.faces_uvs_list()[i], new_tex.faces_uvs_list()[i * N + n]
|
||||
)
|
||||
self.assertClose(
|
||||
tex_init.verts_uvs_list()[i], new_tex.verts_uvs_list()[i * N + n]
|
||||
tex_init.maps_padded()[i, ...], new_tex.maps_padded()[i * N + n]
|
||||
)
|
||||
self.assertClose(
|
||||
tex_init._num_faces_per_mesh[i],
|
||||
new_tex._num_faces_per_mesh[i * N + n],
|
||||
)
|
||||
|
||||
self.assertAllSeparate(
|
||||
[
|
||||
tex_init.faces_uvs_padded(),
|
||||
new_tex.faces_uvs_padded(),
|
||||
tex_init.faces_uvs_packed(),
|
||||
new_tex.faces_uvs_packed(),
|
||||
tex_init.verts_uvs_padded(),
|
||||
new_tex.verts_uvs_padded(),
|
||||
tex_init.verts_uvs_packed(),
|
||||
new_tex.verts_uvs_packed(),
|
||||
tex_init.maps_padded(),
|
||||
new_tex.maps_padded(),
|
||||
]
|
||||
)
|
||||
|
||||
self.assertIsNone(new_tex.verts_rgb_list())
|
||||
self.assertIsNone(new_tex.verts_rgb_padded())
|
||||
self.assertIsNone(new_tex.verts_rgb_packed())
|
||||
|
||||
# 2. Texture vertex RGB
|
||||
tex_rgb = Textures(verts_rgb=torch.randn((B, V, 3)))
|
||||
tex_mesh_rgb = Meshes(
|
||||
verts=mesh.verts_padded(), faces=mesh.faces_padded(), textures=tex_rgb
|
||||
)
|
||||
N = 20
|
||||
new_mesh_rgb = tex_mesh_rgb.extend(N)
|
||||
|
||||
self.assertEqual(len(tex_mesh_rgb) * N, len(new_mesh_rgb))
|
||||
|
||||
tex_init = tex_mesh_rgb.textures
|
||||
new_tex = new_mesh_rgb.textures
|
||||
|
||||
for i in range(len(tex_mesh_rgb)):
|
||||
for n in range(N):
|
||||
self.assertClose(
|
||||
tex_init.verts_rgb_list()[i], new_tex.verts_rgb_list()[i * N + n]
|
||||
)
|
||||
self.assertAllSeparate(
|
||||
[tex_init.verts_rgb_padded(), new_tex.verts_rgb_padded()]
|
||||
)
|
||||
|
||||
self.assertIsNone(new_tex.verts_uvs_padded())
|
||||
self.assertIsNone(new_tex.verts_uvs_list())
|
||||
self.assertIsNone(new_tex.verts_uvs_packed())
|
||||
self.assertIsNone(new_tex.faces_uvs_padded())
|
||||
self.assertIsNone(new_tex.faces_uvs_list())
|
||||
self.assertIsNone(new_tex.faces_uvs_packed())
|
||||
|
||||
# 3. Error
|
||||
with self.assertRaises(ValueError):
|
||||
tex_mesh.extend(N=-1)
|
||||
|
||||
def test_padded_to_packed(self):
|
||||
# Case where each face in the mesh has 3 unique uv vertex indices
|
||||
# - i.e. even if a vertex is shared between multiple faces it will
|
||||
# have a unique uv coordinate for each face.
|
||||
N = 2
|
||||
faces_uvs_list = [
|
||||
torch.tensor([[0, 1, 2], [3, 5, 4], [7, 6, 8]]),
|
||||
torch.tensor([[0, 1, 2], [3, 4, 5]]),
|
||||
] # (N, 3, 3)
|
||||
verts_uvs_list = [torch.ones(9, 2), torch.ones(6, 2)]
|
||||
|
||||
num_faces_per_mesh = [f.shape[0] for f in faces_uvs_list]
|
||||
num_verts_per_mesh = [v.shape[0] for v in verts_uvs_list]
|
||||
tex = TexturesUV(
|
||||
maps=torch.ones((N, 16, 16, 3)),
|
||||
faces_uvs=faces_uvs_list,
|
||||
verts_uvs=verts_uvs_list,
|
||||
)
|
||||
|
||||
# This is set inside Meshes when textures is passed as an input.
|
||||
# Here we set _num_faces_per_mesh and _num_verts_per_mesh explicity.
|
||||
tex1 = tex.clone()
|
||||
tex1._num_faces_per_mesh = num_faces_per_mesh
|
||||
tex1._num_verts_per_mesh = num_verts_per_mesh
|
||||
verts_packed = tex1.verts_uvs_packed()
|
||||
verts_list = tex1.verts_uvs_list()
|
||||
verts_padded = tex1.verts_uvs_padded()
|
||||
|
||||
faces_packed = tex1.faces_uvs_packed()
|
||||
faces_list = tex1.faces_uvs_list()
|
||||
faces_padded = tex1.faces_uvs_padded()
|
||||
|
||||
for f1, f2 in zip(faces_list, faces_uvs_list):
|
||||
self.assertTrue((f1 == f2).all().item())
|
||||
|
||||
for f1, f2 in zip(verts_list, verts_uvs_list):
|
||||
self.assertTrue((f1 == f2).all().item())
|
||||
|
||||
self.assertTrue(faces_packed.shape == (3 + 2, 3))
|
||||
self.assertTrue(faces_padded.shape == (2, 3, 3))
|
||||
self.assertTrue(verts_packed.shape == (9 + 6, 2))
|
||||
self.assertTrue(verts_padded.shape == (2, 9, 2))
|
||||
|
||||
# Case where num_faces_per_mesh is not set and faces_verts_uvs
|
||||
# are initialized with a padded tensor.
|
||||
tex2 = TexturesUV(
|
||||
maps=torch.ones((N, 16, 16, 3)),
|
||||
verts_uvs=verts_padded,
|
||||
faces_uvs=faces_padded,
|
||||
)
|
||||
faces_packed = tex2.faces_uvs_packed()
|
||||
faces_list = tex2.faces_uvs_list()
|
||||
verts_packed = tex2.verts_uvs_packed()
|
||||
verts_list = tex2.verts_uvs_list()
|
||||
|
||||
# Packed is just flattened padded as num_faces_per_mesh
|
||||
# has not been provided.
|
||||
self.assertTrue(faces_packed.shape == (3 * 2, 3))
|
||||
self.assertTrue(verts_packed.shape == (9 * 2, 2))
|
||||
|
||||
for i, (f1, f2) in enumerate(zip(faces_list, faces_uvs_list)):
|
||||
n = num_faces_per_mesh[i]
|
||||
self.assertTrue((f1[:n] == f2).all().item())
|
||||
|
||||
for i, (f1, f2) in enumerate(zip(verts_list, verts_uvs_list)):
|
||||
n = num_verts_per_mesh[i]
|
||||
self.assertTrue((f1[:n] == f2).all().item())
|
||||
|
||||
def test_to(self):
|
||||
tex = TexturesUV(
|
||||
maps=torch.ones((5, 16, 16, 3)),
|
||||
faces_uvs=torch.randint(size=(5, 10, 3), high=15),
|
||||
verts_uvs=torch.rand(size=(5, 15, 2)),
|
||||
)
|
||||
device = torch.device("cuda:0")
|
||||
tex = tex.to(device)
|
||||
self.assertTrue(tex._faces_uvs_padded.device == device)
|
||||
self.assertTrue(tex._verts_uvs_padded.device == device)
|
||||
self.assertTrue(tex._maps_padded.device == device)
|
||||
|
||||
def test_getitem(self):
|
||||
N = 5
|
||||
V = 20
|
||||
source = {
|
||||
"maps": torch.rand(size=(N, 1, 1, 3)),
|
||||
"faces_uvs": torch.randint(size=(N, 10, 3), high=V),
|
||||
"verts_uvs": torch.randn(size=(N, V, 2)),
|
||||
}
|
||||
tex = TexturesUV(
|
||||
maps=source["maps"],
|
||||
faces_uvs=source["faces_uvs"],
|
||||
verts_uvs=source["verts_uvs"],
|
||||
)
|
||||
|
||||
verts = torch.rand(size=(N, V, 3))
|
||||
faces = torch.randint(size=(N, 10, 3), high=V)
|
||||
meshes = Meshes(verts=verts, faces=faces, textures=tex)
|
||||
|
||||
tryindex(self, 2, tex, meshes, source)
|
||||
tryindex(self, slice(0, 2, 1), tex, meshes, source)
|
||||
index = torch.tensor([1, 0, 1, 0, 0], dtype=torch.bool)
|
||||
tryindex(self, index, tex, meshes, source)
|
||||
index = torch.tensor([0, 0, 0, 0, 0], dtype=torch.bool)
|
||||
tryindex(self, index, tex, meshes, source)
|
||||
index = torch.tensor([1, 2], dtype=torch.int64)
|
||||
tryindex(self, index, tex, meshes, source)
|
||||
tryindex(self, [2, 4], tex, meshes, source)
|
||||
|
||||
Reference in New Issue
Block a user