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add align modes for cubify
Summary: Add alignment modes for cubify operation. Reviewed By: nikhilaravi Differential Revision: D21393199 fbshipit-source-id: 7022044e591229a6ed5efc361fd3215e65f43f86
This commit is contained in:
Georgia Gkioxari
Facebook GitHub Bot
parent
8fc28baa27
commit
a61c9376d5
@ -45,7 +45,7 @@ def ravel_index(idx, dims) -> torch.Tensor:
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@torch.no_grad()
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def cubify(voxels, thresh, device=None) -> Meshes:
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def cubify(voxels, thresh, device=None, align: str = "topleft") -> Meshes:
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r"""
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Converts a voxel to a mesh by replacing each occupied voxel with a cube
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consisting of 12 faces and 8 vertices. Shared vertices are merged, and
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@ -54,13 +54,38 @@ def cubify(voxels, thresh, device=None) -> Meshes:
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voxels: A FloatTensor of shape (N, D, H, W) containing occupancy probabilities.
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thresh: A scalar threshold. If a voxel occupancy is larger than
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thresh, the voxel is considered occupied.
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device: The device of the output meshes
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align: Defines the alignment of the mesh vertices and the grid locations.
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Has to be one of {"topleft", "corner", "center"}. See below for explanation.
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Default is "topleft".
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Returns:
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meshes: A Meshes object of the corresponding meshes.
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The alignment between the vertices of the cubified mesh and the voxel locations (or pixels)
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is defined by the choice of `align`. We support three modes, as shown below for a 2x2 grid:
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X---X---- X-------X ---------
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| | | | | | | X | X |
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X---X---- --------- ---------
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| | | | | | | X | X |
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--------- X-------X ---------
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topleft corner center
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In the figure, X denote the grid locations and the squares represent the added cuboids.
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When `align="topleft"`, then the top left corner of each cuboid corresponds to the
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pixel coordinate of the input grid.
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When `align="corner"`, then the corners of the output mesh span the whole grid.
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When `align="center"`, then the grid locations form the center of the cuboids.
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"""
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if device is None:
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device = voxels.device
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if align not in ["topleft", "corner", "center"]:
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raise ValueError("Align mode must be one of (topleft, corner, center).")
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if len(voxels) == 0:
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return Meshes(verts=[], faces=[])
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@ -146,7 +171,7 @@ def cubify(voxels, thresh, device=None) -> Meshes:
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# boolean to linear index
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# NF x 2
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linind = torch.nonzero(faces_idx)
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linind = torch.nonzero(faces_idx, as_tuple=False)
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# NF x 4
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nyxz = unravel_index(linind[:, 0], (N, H, W, D))
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@ -170,11 +195,19 @@ def cubify(voxels, thresh, device=None) -> Meshes:
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torch.arange(H + 1), torch.arange(W + 1), torch.arange(D + 1)
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)
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y = y.to(device=device, dtype=torch.float32)
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y = y * 2.0 / (H - 1.0) - 1.0
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x = x.to(device=device, dtype=torch.float32)
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x = x * 2.0 / (W - 1.0) - 1.0
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z = z.to(device=device, dtype=torch.float32)
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z = z * 2.0 / (D - 1.0) - 1.0
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if align == "center":
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x = x - 0.5
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y = y - 0.5
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z = z - 0.5
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margin = 0.0 if align == "corner" else 1.0
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y = y * 2.0 / (H - margin) - 1.0
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x = x * 2.0 / (W - margin) - 1.0
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z = z * 2.0 / (D - margin) - 1.0
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# ((H+1)(W+1)(D+1)) x 3
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grid_verts = torch.stack((x, y, z), dim=3).view(-1, 3)
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@ -196,7 +229,7 @@ def cubify(voxels, thresh, device=None) -> Meshes:
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idlenum = idleverts.cumsum(1)
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verts_list = [
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grid_verts.index_select(0, (idleverts[n] == 0).nonzero()[:, 0])
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grid_verts.index_select(0, (idleverts[n] == 0).nonzero(as_tuple=False)[:, 0])
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for n in range(N)
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]
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faces_list = [nface - idlenum[n][nface] for n, nface in enumerate(faces_list)]
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@ -3,16 +3,17 @@
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import unittest
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import torch
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from common_testing import TestCaseMixin
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from pytorch3d.ops import cubify
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class TestCubify(unittest.TestCase):
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class TestCubify(TestCaseMixin, unittest.TestCase):
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def test_allempty(self):
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N, V = 32, 14
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device = torch.device("cuda:0")
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voxels = torch.zeros((N, V, V, V), dtype=torch.float32, device=device)
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meshes = cubify(voxels, 0.5, 0)
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self.assertTrue(meshes.isempty)
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meshes = cubify(voxels, 0.5)
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self.assertTrue(meshes.isempty())
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def test_cubify(self):
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N, V = 4, 2
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@ -29,155 +30,143 @@ class TestCubify(unittest.TestCase):
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voxels[3, 1, 1, 0] = 1.0
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# compute cubify
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meshes = cubify(voxels, 0.5, 0)
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meshes = cubify(voxels, 0.5)
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# 1st-check
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verts, faces = meshes.get_mesh_verts_faces(0)
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self.assertTrue(
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torch.allclose(faces.max().cpu(), torch.tensor([verts.size(0) - 1]))
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self.assertClose(faces.max().cpu(), torch.tensor(verts.size(0) - 1))
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self.assertClose(
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verts,
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torch.tensor(
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[
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[-1.0, -1.0, -1.0],
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[-1.0, -1.0, 1.0],
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[1.0, -1.0, -1.0],
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[1.0, -1.0, 1.0],
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[-1.0, 1.0, -1.0],
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[-1.0, 1.0, 1.0],
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[1.0, 1.0, -1.0],
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[1.0, 1.0, 1.0],
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],
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dtype=torch.float32,
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device=device,
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),
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)
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self.assertTrue(
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torch.allclose(
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verts,
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torch.tensor(
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[
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[-1.0, -1.0, -1.0],
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[-1.0, -1.0, 1.0],
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[1.0, -1.0, -1.0],
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[1.0, -1.0, 1.0],
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[-1.0, 1.0, -1.0],
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[-1.0, 1.0, 1.0],
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[1.0, 1.0, -1.0],
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[1.0, 1.0, 1.0],
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],
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dtype=torch.float32,
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device=device,
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),
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)
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)
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self.assertTrue(
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torch.allclose(
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faces,
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torch.tensor(
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[
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[0, 1, 4],
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[1, 5, 4],
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[4, 5, 6],
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[5, 7, 6],
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[0, 4, 6],
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[0, 6, 2],
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[0, 3, 1],
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[0, 2, 3],
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[6, 7, 3],
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[6, 3, 2],
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[1, 7, 5],
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[1, 3, 7],
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],
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dtype=torch.int64,
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device=device,
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),
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)
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self.assertClose(
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faces,
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torch.tensor(
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[
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[0, 1, 4],
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[1, 5, 4],
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[4, 5, 6],
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[5, 7, 6],
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[0, 4, 6],
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[0, 6, 2],
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[0, 3, 1],
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[0, 2, 3],
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[6, 7, 3],
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[6, 3, 2],
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[1, 7, 5],
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[1, 3, 7],
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],
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dtype=torch.int64,
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device=device,
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),
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)
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# 2nd-check
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verts, faces = meshes.get_mesh_verts_faces(1)
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self.assertTrue(
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torch.allclose(faces.max().cpu(), torch.tensor([verts.size(0) - 1]))
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self.assertClose(faces.max().cpu(), torch.tensor(verts.size(0) - 1))
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self.assertClose(
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verts,
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torch.tensor(
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[
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[-1.0, -1.0, -1.0],
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[-1.0, -1.0, 1.0],
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[-1.0, -1.0, 3.0],
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[1.0, -1.0, -1.0],
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[1.0, -1.0, 1.0],
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[1.0, -1.0, 3.0],
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[3.0, -1.0, -1.0],
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[3.0, -1.0, 1.0],
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[3.0, -1.0, 3.0],
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[-1.0, 1.0, -1.0],
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[-1.0, 1.0, 1.0],
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[-1.0, 1.0, 3.0],
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[1.0, 1.0, -1.0],
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[1.0, 1.0, 3.0],
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[3.0, 1.0, -1.0],
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[3.0, 1.0, 1.0],
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[3.0, 1.0, 3.0],
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[-1.0, 3.0, -1.0],
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[-1.0, 3.0, 1.0],
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[-1.0, 3.0, 3.0],
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[1.0, 3.0, -1.0],
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[1.0, 3.0, 1.0],
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[1.0, 3.0, 3.0],
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[3.0, 3.0, -1.0],
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[3.0, 3.0, 1.0],
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[3.0, 3.0, 3.0],
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],
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dtype=torch.float32,
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device=device,
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),
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)
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self.assertTrue(
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torch.allclose(
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verts,
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torch.tensor(
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[
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[-1.0, -1.0, -1.0],
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[-1.0, -1.0, 1.0],
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[-1.0, -1.0, 3.0],
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[1.0, -1.0, -1.0],
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[1.0, -1.0, 1.0],
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[1.0, -1.0, 3.0],
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[3.0, -1.0, -1.0],
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[3.0, -1.0, 1.0],
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[3.0, -1.0, 3.0],
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[-1.0, 1.0, -1.0],
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[-1.0, 1.0, 1.0],
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[-1.0, 1.0, 3.0],
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[1.0, 1.0, -1.0],
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[1.0, 1.0, 3.0],
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[3.0, 1.0, -1.0],
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[3.0, 1.0, 1.0],
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[3.0, 1.0, 3.0],
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[-1.0, 3.0, -1.0],
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[-1.0, 3.0, 1.0],
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[-1.0, 3.0, 3.0],
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[1.0, 3.0, -1.0],
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[1.0, 3.0, 1.0],
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[1.0, 3.0, 3.0],
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[3.0, 3.0, -1.0],
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[3.0, 3.0, 1.0],
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[3.0, 3.0, 3.0],
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],
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dtype=torch.float32,
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device=device,
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),
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)
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)
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self.assertTrue(
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torch.allclose(
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faces,
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torch.tensor(
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[
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[0, 1, 9],
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[1, 10, 9],
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[0, 9, 12],
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[0, 12, 3],
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[0, 4, 1],
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[0, 3, 4],
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[1, 2, 10],
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[2, 11, 10],
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[1, 5, 2],
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[1, 4, 5],
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[2, 13, 11],
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[2, 5, 13],
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[3, 12, 14],
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[3, 14, 6],
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[3, 7, 4],
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[3, 6, 7],
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[14, 15, 7],
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[14, 7, 6],
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[4, 8, 5],
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[4, 7, 8],
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[15, 16, 8],
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[15, 8, 7],
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[5, 16, 13],
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[5, 8, 16],
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[9, 10, 17],
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[10, 18, 17],
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[17, 18, 20],
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[18, 21, 20],
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[9, 17, 20],
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[9, 20, 12],
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[10, 11, 18],
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[11, 19, 18],
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[18, 19, 21],
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[19, 22, 21],
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[11, 22, 19],
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[11, 13, 22],
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[20, 21, 23],
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[21, 24, 23],
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[12, 20, 23],
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[12, 23, 14],
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[23, 24, 15],
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[23, 15, 14],
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[21, 22, 24],
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[22, 25, 24],
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[24, 25, 16],
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[24, 16, 15],
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[13, 25, 22],
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[13, 16, 25],
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],
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dtype=torch.int64,
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device=device,
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),
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)
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self.assertClose(
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faces,
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torch.tensor(
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[
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[0, 1, 9],
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[1, 10, 9],
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[0, 9, 12],
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[0, 12, 3],
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[0, 4, 1],
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[0, 3, 4],
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[1, 2, 10],
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[2, 11, 10],
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[1, 5, 2],
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[1, 4, 5],
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[2, 13, 11],
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[2, 5, 13],
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[3, 12, 14],
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[3, 14, 6],
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[3, 7, 4],
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[3, 6, 7],
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[14, 15, 7],
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[14, 7, 6],
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[4, 8, 5],
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[4, 7, 8],
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[15, 16, 8],
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[15, 8, 7],
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[5, 16, 13],
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[5, 8, 16],
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[9, 10, 17],
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[10, 18, 17],
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[17, 18, 20],
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[18, 21, 20],
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[9, 17, 20],
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[9, 20, 12],
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[10, 11, 18],
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[11, 19, 18],
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[18, 19, 21],
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[19, 22, 21],
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[11, 22, 19],
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[11, 13, 22],
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[20, 21, 23],
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[21, 24, 23],
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[12, 20, 23],
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[12, 23, 14],
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[23, 24, 15],
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[23, 15, 14],
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[21, 22, 24],
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[22, 25, 24],
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[24, 25, 16],
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[24, 16, 15],
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[13, 25, 22],
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[13, 16, 25],
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],
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dtype=torch.int64,
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device=device,
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),
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)
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# 3rd-check
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@ -187,91 +176,127 @@ class TestCubify(unittest.TestCase):
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# 4th-check
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verts, faces = meshes.get_mesh_verts_faces(3)
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self.assertTrue(
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torch.allclose(
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verts,
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torch.tensor(
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[
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[1.0, -1.0, -1.0],
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[1.0, -1.0, 1.0],
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[1.0, -1.0, 3.0],
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[3.0, -1.0, -1.0],
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[3.0, -1.0, 1.0],
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[3.0, -1.0, 3.0],
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[-1.0, 1.0, 1.0],
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[-1.0, 1.0, 3.0],
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[1.0, 1.0, -1.0],
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[1.0, 1.0, 1.0],
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[1.0, 1.0, 3.0],
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[3.0, 1.0, -1.0],
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[3.0, 1.0, 1.0],
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[3.0, 1.0, 3.0],
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[-1.0, 3.0, 1.0],
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[-1.0, 3.0, 3.0],
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[1.0, 3.0, -1.0],
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[1.0, 3.0, 1.0],
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[1.0, 3.0, 3.0],
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[3.0, 3.0, -1.0],
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[3.0, 3.0, 1.0],
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[3.0, 3.0, 3.0],
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],
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dtype=torch.float32,
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device=device,
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),
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)
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self.assertClose(
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verts,
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torch.tensor(
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[
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[1.0, -1.0, -1.0],
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[1.0, -1.0, 1.0],
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[1.0, -1.0, 3.0],
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[3.0, -1.0, -1.0],
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[3.0, -1.0, 1.0],
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[3.0, -1.0, 3.0],
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[-1.0, 1.0, 1.0],
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[-1.0, 1.0, 3.0],
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[1.0, 1.0, -1.0],
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[1.0, 1.0, 1.0],
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[1.0, 1.0, 3.0],
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[3.0, 1.0, -1.0],
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[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.assertTrue(
|
||||
torch.allclose(
|
||||
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,
|
||||
),
|
||||
)
|
||||
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")
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user