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test_texturing.py
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test_texturing.py
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#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import unittest
import torch
import torch.nn.functional as F
from pytorch3d.renderer.mesh.rasterizer import Fragments
from pytorch3d.renderer.mesh.texturing import (
_clip_barycentric_coordinates,
interpolate_face_attributes,
interpolate_texture_map,
interpolate_vertex_colors,
)
from pytorch3d.structures import Meshes, Textures
from common_testing import TestCaseMixin
from test_meshes import TestMeshes
class TestTexturing(TestCaseMixin, unittest.TestCase):
def test_interpolate_attributes(self):
"""
This tests both interpolate_vertex_colors as well as
interpolate_face_attributes.
"""
verts = torch.randn((4, 3), dtype=torch.float32)
faces = torch.tensor([[2, 1, 0], [3, 1, 0]], dtype=torch.int64)
vert_tex = torch.tensor(
[[0, 1, 0], [0, 1, 1], [1, 1, 0], [1, 1, 1]], dtype=torch.float32
)
tex = Textures(verts_rgb=vert_tex[None, :])
mesh = Meshes(verts=[verts], faces=[faces], textures=tex)
pix_to_face = torch.tensor([0, 1], dtype=torch.int64).view(1, 1, 1, 2)
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)
expected_vals = torch.tensor(
[[0.5, 1.0, 0.3], [0.3, 1.0, 0.9]], dtype=torch.float32
).view(1, 1, 1, 2, -1)
fragments = Fragments(
pix_to_face=pix_to_face,
bary_coords=barycentric_coords,
zbuf=torch.ones_like(pix_to_face),
dists=torch.ones_like(pix_to_face),
)
texels = interpolate_vertex_colors(fragments, mesh)
self.assertTrue(torch.allclose(texels, expected_vals[None, :]))
def test_interpolate_attributes_grad(self):
verts = torch.randn((4, 3), dtype=torch.float32)
faces = torch.tensor([[2, 1, 0], [3, 1, 0]], dtype=torch.int64)
vert_tex = torch.tensor(
[[0, 1, 0], [0, 1, 1], [1, 1, 0], [1, 1, 1]],
dtype=torch.float32,
requires_grad=True,
)
tex = Textures(verts_rgb=vert_tex[None, :])
mesh = Meshes(verts=[verts], faces=[faces], textures=tex)
pix_to_face = torch.tensor([0, 1], dtype=torch.int64).view(1, 1, 1, 2)
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)
fragments = Fragments(
pix_to_face=pix_to_face,
bary_coords=barycentric_coords,
zbuf=torch.ones_like(pix_to_face),
dists=torch.ones_like(pix_to_face),
)
grad_vert_tex = torch.tensor(
[
[0.3, 0.3, 0.3],
[0.9, 0.9, 0.9],
[0.5, 0.5, 0.5],
[0.3, 0.3, 0.3],
],
dtype=torch.float32,
)
texels = interpolate_vertex_colors(fragments, mesh)
texels.sum().backward()
self.assertTrue(hasattr(vert_tex, "grad"))
self.assertTrue(torch.allclose(vert_tex.grad, grad_vert_tex[None, :]))
def test_interpolate_face_attributes_fail(self):
# 1. A face can only have 3 verts
# i.e. face_attributes must have shape (F, 3, D)
face_attributes = torch.ones(1, 4, 3)
pix_to_face = torch.ones((1, 1, 1, 1))
fragments = Fragments(
pix_to_face=pix_to_face,
bary_coords=pix_to_face[..., None].expand(-1, -1, -1, -1, 3),
zbuf=pix_to_face,
dists=pix_to_face,
)
with self.assertRaises(ValueError):
interpolate_face_attributes(fragments, face_attributes)
# 2. pix_to_face must have shape (N, H, W, K)
pix_to_face = torch.ones((1, 1, 1, 1, 3))
fragments = Fragments(
pix_to_face=pix_to_face,
bary_coords=pix_to_face,
zbuf=pix_to_face,
dists=pix_to_face,
)
with self.assertRaises(ValueError):
interpolate_face_attributes(fragments, face_attributes)
def test_interpolate_texture_map(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)
dummy_verts = torch.zeros(4, 3)
vert_uvs = torch.tensor(
[[1, 0], [0, 1], [1, 1], [0, 0]], dtype=torch.float32
)
face_uvs = torch.tensor([[0, 1, 2], [1, 2, 3]], dtype=torch.int64)
interpolated_uvs = torch.tensor(
[[0.5 + 0.2, 0.3 + 0.2], [0.6, 0.3 + 0.6]], dtype=torch.float32
)
# Create a dummy texture map
H = 2
W = 2
x = torch.linspace(0, 1, W).view(1, W).expand(H, W)
y = torch.linspace(0, 1, H).view(H, 1).expand(H, W)
tex_map = torch.stack([x, y], dim=2).view(1, H, W, 2)
pix_to_face = torch.tensor([0, 1], dtype=torch.int64).view(1, 1, 1, 2)
fragments = Fragments(
pix_to_face=pix_to_face,
bary_coords=barycentric_coords,
zbuf=pix_to_face,
dists=pix_to_face,
)
tex = Textures(
maps=tex_map,
faces_uvs=face_uvs[None, ...],
verts_uvs=vert_uvs[None, ...],
)
meshes = Meshes(verts=[dummy_verts], faces=[face_uvs], textures=tex)
texels = interpolate_texture_map(fragments, meshes)
# Expected output
pixel_uvs = interpolated_uvs * 2.0 - 1.0
pixel_uvs = pixel_uvs.view(2, 1, 1, 2)
tex_map = torch.flip(tex_map, [1])
tex_map = tex_map.permute(0, 3, 1, 2)
tex_map = torch.cat([tex_map, tex_map], dim=0)
expected_out = F.grid_sample(tex_map, pixel_uvs, align_corners=False)
self.assertTrue(
torch.allclose(texels.squeeze(), expected_out.squeeze())
)
def test_clone(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)),
)
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_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)
def test_extend(self):
B = 10
mesh = TestMeshes.init_mesh(B, 30, 50)
V = mesh._V
F = mesh._F
tex = 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)),
)
tex_mesh = Meshes(
verts=mesh.verts_padded(), faces=mesh.faces_padded(), textures=tex
)
N = 20
new_mesh = tex_mesh.extend(N)
self.assertEqual(len(tex_mesh) * N, len(new_mesh))
tex_init = tex_mesh.textures
new_tex = new_mesh.textures
for i in range(len(tex_mesh)):
for n in range(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],
)
self.assertAllSeparate(
[
tex_init.faces_uvs_padded(),
new_tex.faces_uvs_padded(),
tex_init.verts_uvs_padded(),
new_tex.verts_uvs_padded(),
tex_init.maps_padded(),
new_tex.maps_padded(),
]
)
with self.assertRaises(ValueError):
tex_mesh.extend(N=-1)
def test_clip_barycentric_coords(self):
barycentric_coords = torch.tensor(
[[1.5, -0.3, -0.2], [1.2, 0.3, -0.5]], dtype=torch.float32
)
expected_out = torch.tensor(
[[1.0, 0.0, 0.0], [1.0 / 1.3, 0.3 / 1.3, 0.0]], dtype=torch.float32
)
clipped = _clip_barycentric_coordinates(barycentric_coords)
self.assertTrue(torch.allclose(clipped, expected_out))
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