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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from pytorch3d.ops import cubify
from pytorch3d.renderer.mesh.textures import TexturesAtlas
from .common_testing import TestCaseMixin
class TestCubify(TestCaseMixin, unittest.TestCase):
def test_allempty(self):
N, V = 32, 14
device = torch.device("cuda:0")
voxels = torch.zeros((N, V, V, V), dtype=torch.float32, device=device)
meshes = cubify(voxels, 0.5)
self.assertTrue(meshes.isempty())
def test_cubify(self):
N, V = 4, 2
device = torch.device("cuda:0")
voxels = torch.zeros((N, V, V, V), dtype=torch.float32, device=device)
# 1st example: (top left corner, znear) is on
voxels[0, 0, 0, 0] = 1.0
# 2nd example: all are on
voxels[1] = 1.0
# 3rd example: empty
# 4th example
voxels[3, :, :, 1] = 1.0
voxels[3, 1, 1, 0] = 1.0
# compute cubify
meshes = cubify(voxels, 0.5)
# 1st-check
verts, faces = meshes.get_mesh_verts_faces(0)
self.assertClose(faces.max().cpu(), torch.tensor(verts.size(0) - 1))
self.assertClose(
verts,
torch.tensor(
[
[-1.0, -1.0, -1.0],
[-1.0, -1.0, 1.0],
[1.0, -1.0, -1.0],
[1.0, -1.0, 1.0],
[-1.0, 1.0, -1.0],
[-1.0, 1.0, 1.0],
[1.0, 1.0, -1.0],
[1.0, 1.0, 1.0],
],
dtype=torch.float32,
device=device,
),
)
self.assertClose(
faces,
torch.tensor(
[
[0, 1, 4],
[1, 5, 4],
[4, 5, 6],
[5, 7, 6],
[0, 4, 6],
[0, 6, 2],
[0, 3, 1],
[0, 2, 3],
[6, 7, 3],
[6, 3, 2],
[1, 7, 5],
[1, 3, 7],
],
dtype=torch.int64,
device=device,
),
)
# 2nd-check
verts, faces = meshes.get_mesh_verts_faces(1)
self.assertClose(faces.max().cpu(), torch.tensor(verts.size(0) - 1))
self.assertClose(
verts,
torch.tensor(
[
[-1.0, -1.0, -1.0],
[-1.0, -1.0, 1.0],
[-1.0, -1.0, 3.0],
[1.0, -1.0, -1.0],
[1.0, -1.0, 1.0],
[1.0, -1.0, 3.0],
[3.0, -1.0, -1.0],
[3.0, -1.0, 1.0],
[3.0, -1.0, 3.0],
[-1.0, 1.0, -1.0],
[-1.0, 1.0, 1.0],
[-1.0, 1.0, 3.0],
[1.0, 1.0, -1.0],
[1.0, 1.0, 3.0],
[3.0, 1.0, -1.0],
[3.0, 1.0, 1.0],
[3.0, 1.0, 3.0],
[-1.0, 3.0, -1.0],
[-1.0, 3.0, 1.0],
[-1.0, 3.0, 3.0],
[1.0, 3.0, -1.0],
[1.0, 3.0, 1.0],
[1.0, 3.0, 3.0],
[3.0, 3.0, -1.0],
[3.0, 3.0, 1.0],
[3.0, 3.0, 3.0],
],
dtype=torch.float32,
device=device,
),
)
self.assertClose(
faces,
torch.tensor(
[
[0, 1, 9],
[1, 10, 9],
[0, 9, 12],
[0, 12, 3],
[0, 4, 1],
[0, 3, 4],
[1, 2, 10],
[2, 11, 10],
[1, 5, 2],
[1, 4, 5],
[2, 13, 11],
[2, 5, 13],
[3, 12, 14],
[3, 14, 6],
[3, 7, 4],
[3, 6, 7],
[14, 15, 7],
[14, 7, 6],
[4, 8, 5],
[4, 7, 8],
[15, 16, 8],
[15, 8, 7],
[5, 16, 13],
[5, 8, 16],
[9, 10, 17],
[10, 18, 17],
[17, 18, 20],
[18, 21, 20],
[9, 17, 20],
[9, 20, 12],
[10, 11, 18],
[11, 19, 18],
[18, 19, 21],
[19, 22, 21],
[11, 22, 19],
[11, 13, 22],
[20, 21, 23],
[21, 24, 23],
[12, 20, 23],
[12, 23, 14],
[23, 24, 15],
[23, 15, 14],
[21, 22, 24],
[22, 25, 24],
[24, 25, 16],
[24, 16, 15],
[13, 25, 22],
[13, 16, 25],
],
dtype=torch.int64,
device=device,
),
)
# 3rd-check
verts, faces = meshes.get_mesh_verts_faces(2)
self.assertTrue(verts.size(0) == 0)
self.assertTrue(faces.size(0) == 0)
# 4th-check
verts, faces = meshes.get_mesh_verts_faces(3)
self.assertClose(
verts,
torch.tensor(
[
[1.0, -1.0, -1.0],
[1.0, -1.0, 1.0],
[1.0, -1.0, 3.0],
[3.0, -1.0, -1.0],
[3.0, -1.0, 1.0],
[3.0, -1.0, 3.0],
[-1.0, 1.0, 1.0],
[-1.0, 1.0, 3.0],
[1.0, 1.0, -1.0],
[1.0, 1.0, 1.0],
[1.0, 1.0, 3.0],
[3.0, 1.0, -1.0],
[3.0, 1.0, 1.0],
[3.0, 1.0, 3.0],
[-1.0, 3.0, 1.0],
[-1.0, 3.0, 3.0],
[1.0, 3.0, -1.0],
[1.0, 3.0, 1.0],
[1.0, 3.0, 3.0],
[3.0, 3.0, -1.0],
[3.0, 3.0, 1.0],
[3.0, 3.0, 3.0],
],
dtype=torch.float32,
device=device,
),
)
self.assertClose(
faces,
torch.tensor(
[
[0, 1, 8],
[1, 9, 8],
[0, 8, 11],
[0, 11, 3],
[0, 4, 1],
[0, 3, 4],
[11, 12, 4],
[11, 4, 3],
[1, 2, 9],
[2, 10, 9],
[1, 5, 2],
[1, 4, 5],
[12, 13, 5],
[12, 5, 4],
[2, 13, 10],
[2, 5, 13],
[6, 7, 14],
[7, 15, 14],
[14, 15, 17],
[15, 18, 17],
[6, 14, 17],
[6, 17, 9],
[6, 10, 7],
[6, 9, 10],
[7, 18, 15],
[7, 10, 18],
[8, 9, 16],
[9, 17, 16],
[16, 17, 19],
[17, 20, 19],
[8, 16, 19],
[8, 19, 11],
[19, 20, 12],
[19, 12, 11],
[17, 18, 20],
[18, 21, 20],
[20, 21, 13],
[20, 13, 12],
[10, 21, 18],
[10, 13, 21],
],
dtype=torch.int64,
device=device,
),
)
def test_align(self):
N, V = 1, 2
device = torch.device("cuda:0")
voxels = torch.ones((N, V, V, V), dtype=torch.float32, device=device)
# topleft align
mesh = cubify(voxels, 0.5)
verts, faces = mesh.get_mesh_verts_faces(0)
self.assertClose(verts.min(), torch.tensor(-1.0, device=device))
self.assertClose(verts.max(), torch.tensor(3.0, device=device))
# corner align
mesh = cubify(voxels, 0.5, align="corner")
verts, faces = mesh.get_mesh_verts_faces(0)
self.assertClose(verts.min(), torch.tensor(-1.0, device=device))
self.assertClose(verts.max(), torch.tensor(1.0, device=device))
# center align
mesh = cubify(voxels, 0.5, align="center")
verts, faces = mesh.get_mesh_verts_faces(0)
self.assertClose(verts.min(), torch.tensor(-2.0, device=device))
self.assertClose(verts.max(), torch.tensor(2.0, device=device))
# invalid align
with self.assertRaisesRegex(ValueError, "Align mode must be one of"):
cubify(voxels, 0.5, align="")
# invalid align
with self.assertRaisesRegex(ValueError, "Align mode must be one of"):
cubify(voxels, 0.5, align="topright")
# inside occupancy, similar to GH#185 use case
N, V = 1, 4
voxels = torch.zeros((N, V, V, V), dtype=torch.float32, device=device)
voxels[0, : V // 2, : V // 2, : V // 2] = 1.0
mesh = cubify(voxels, 0.5, align="corner")
verts, faces = mesh.get_mesh_verts_faces(0)
self.assertClose(verts.min(), torch.tensor(-1.0, device=device))
self.assertClose(verts.max(), torch.tensor(0.0, device=device))
@staticmethod
def cubify_with_init(batch_size: int, V: int):
device = torch.device("cuda:0")
voxels = torch.rand((batch_size, V, V, V), dtype=torch.float32, device=device)
torch.cuda.synchronize()
def convert():
cubify(voxels, 0.5)
torch.cuda.synchronize()
return convert
def test_cubify_with_feats(self):
N, V = 3, 2
device = torch.device("cuda:0")
voxels = torch.zeros((N, V, V, V), dtype=torch.float32, device=device)
feats = torch.zeros((N, 3, V, V, V), dtype=torch.float32, device=device)
# fill the feats with red color
feats[:, 0, :, :, :] = 255
# 1st example: (top left corner, znear) is on
voxels[0, 0, 0, 0] = 1.0
# the color is set to green
feats[0, :, 0, 0, 0] = torch.Tensor([0, 255, 0])
# 2nd example: all are on
voxels[1] = 1.0
# 3rd example
voxels[2, :, :, 1] = 1.0
voxels[2, 1, 1, 0] = 1.0
# the color is set to yellow and blue respectively
feats[2, 1, :, :, 1] = 255
feats[2, :, 1, 1, 0] = torch.Tensor([0, 0, 255])
meshes = cubify(voxels, 0.5, feats=feats, align="center")
textures = meshes.textures
self.assertTrue(textures is not None)
self.assertTrue(isinstance(textures, TexturesAtlas))
faces_textures = textures.faces_verts_textures_packed()
red = faces_textures.new_tensor([255.0, 0.0, 0.0])
green = faces_textures.new_tensor([0.0, 255.0, 0.0])
blue = faces_textures.new_tensor([0.0, 0.0, 255.0])
yellow = faces_textures.new_tensor([255.0, 255.0, 0.0])
self.assertEqual(faces_textures.shape, (100, 3, 3))
faces_textures_ = faces_textures.flatten(end_dim=1)
self.assertClose(faces_textures_[:36], green.expand(36, -1))
self.assertClose(faces_textures_[36:180], red.expand(144, -1))
self.assertClose(faces_textures_[180:228], yellow.expand(48, -1))
self.assertClose(faces_textures_[228:258], blue.expand(30, -1))
self.assertClose(faces_textures_[258:300], yellow.expand(42, -1))
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