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CUDA kernel for interpolate_face_attributes

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Summary: When rendering meshes with Phong shading, interpolate_face_attributes was taking up a nontrivial fraction of the overall shading time. This diff replaces our Python implementation of this function with a CUDA implementation.

Reviewed By: nikhilaravi

Differential Revision: D21610763

fbshipit-source-id: 2bb362a28f698541812aeab539047264b125ebb8
This commit is contained in:
Justin Johnson
2020-07-13 12:58:07 -07:00
committed by Facebook GitHub Bot
parent 0505e5f4a9
commit 26d2cc24c1
11 changed files with 630 additions and 140 deletions
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92
tests/test_texturing.py
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@@ -7,103 +7,13 @@ import torch
import torch.nn.functional as F
from common_testing import TestCaseMixin
from pytorch3d.renderer.mesh.rasterizer import Fragments
from pytorch3d.renderer.mesh.texturing import (
interpolate_face_attributes,
interpolate_texture_map,
interpolate_vertex_colors,
)
from pytorch3d.renderer.mesh.texturing import interpolate_texture_map
from pytorch3d.structures import Meshes, Textures
from pytorch3d.structures.utils import list_to_padded
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.pix_to_face, fragments.bary_coords, 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.pix_to_face, fragments.bary_coords, 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