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import time
from typing import Optional
import warnings
import torch
from tqdm import tqdm
from pathlib import Path
from core.opt import MeshOptimizer
from core.remesh import calc_edge_length, calc_edges, calc_vertex_normals
from util.func import (
laplacian,
load_obj,
make_sphere,
make_star_cameras,
normalize_vertices,
save_images,
to_numpy,
)
from util.render import NormalsRenderer
from util.snapshot import Snapshot, snapshot
import numpy as np
try:
from pyremesh import remesh_botsch
except:
remesh_botsch = None
# suppress warning in torch.cartesian_prod()
warnings.filterwarnings(
"ignore",
message="torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument.",
)
@dataclass
class OptimizeSettings:
# requires target fname or vertices/faces
target_fname: Path = None
target_vertices: torch.Tensor = None # V,3
target_faces: torch.Tensor = None # F,3
# requires steps or timeout
steps: Optional[int] = 500
timeout: Optional[float] = None
outdir: str = "out"
method: str = "ours" # adam,large,ours
image_size: int = 512
sphere_size: float = 0.5
sphere_level: int = 2 # 0->12,42,162,642,2562, 5->10k,40k,160k
sphere_shift: tuple[float, float, float] = None
cameras: tuple[int, int] = (4, 4)
device = "cuda"
# optimizer common
lr: float = 0.5
laplacian_weight: float = 0.1
ramp: float = 3.0
betas: tuple[float, float, float] = (0.8, 0.8, 0)
remesh_interval: int = 1
edge_len_lims: tuple[float, float] = (0.01, 0.15)
# optimizer ours
gammas: tuple[float, float, float] = (0, 0, 0)
nu_ref: float = 0.3
edge_len_tol: float = 0.5
gain: float = 0.2
local_edgelen: bool = True
# optimizer adam remesh
remesh_ratio: float = 0.5
# result
result_interval: int = 5
result_meshes: bool = False
result_snapshots: bool = False
save_images: bool = False
@dataclass
class OptimizeResult:
settings: OptimizeSettings
target_vertices: torch.Tensor = None
target_faces: torch.Tensor = None
snapshots: list[Snapshot] = field(default_factory=list)
def make_optimizer(settings, vertices, faces):
edges, _ = calc_edges(faces)
mean_edge_length = calc_edge_length(vertices, edges).mean().item()
lr = settings.lr * mean_edge_length
Laplacian = None
if settings.method == "adam":
vertices.requires_grad_()
opt = torch.optim.Adam([vertices], lr=lr, betas=settings.betas)
edges, _ = calc_edges(faces)
Laplacian = laplacian(vertices.shape[0], edges)
loss = (vertices * (Laplacian @ vertices)).mean() # warm-up
elif settings.method == "ours":
opt = MeshOptimizer(
vertices,
faces,
lr=settings.lr,
betas=settings.betas,
gammas=settings.gammas,
nu_ref=settings.nu_ref,
edge_len_lims=settings.edge_len_lims,
edge_len_tol=settings.edge_len_tol,
gain=settings.gain,
laplacian_weight=settings.laplacian_weight,
ramp=settings.ramp,
remesh_interval=settings.remesh_interval,
local_edgelen=settings.local_edgelen,
)
vertices = opt.vertices
else:
raise RuntimeError("unknown method")
return opt, lr, vertices, Laplacian
def load_target_mesh(fname, device="cuda"):
vertices, faces = load_obj(fname, device=device)
vertices = normalize_vertices(vertices)
return vertices, faces
def optimize(settings: OptimizeSettings):
result = OptimizeResult(settings=settings)
outdir = Path(settings.outdir)
vertices, faces = make_sphere(
level=settings.sphere_level, radius=settings.sphere_size, device=settings.device
)
if settings.sphere_shift:
vertices += torch.tensor(settings.sphere_shift, device=settings.device)
mv, proj = make_star_cameras(
settings.cameras[0],
settings.cameras[1],
distance=10,
image_size=[settings.image_size, settings.image_size],
device=settings.device,
)
renderer = NormalsRenderer(
mv, proj, image_size=[settings.image_size, settings.image_size]
)
if settings.target_vertices is None:
target_vertices, target_faces = load_target_mesh(settings.target_fname)
else:
target_vertices, target_faces = settings.target_vertices, settings.target_faces
result.target_vertices, result.target_faces = target_vertices, target_faces
target_normals = calc_vertex_normals(target_vertices, target_faces)
target_images = renderer.render(target_vertices, target_normals, target_faces)
if settings.save_images:
save_images(target_images, outdir / "target_images")
opt, lr, vertices, Laplacian = make_optimizer(settings, vertices, faces)
start = time.time()
step = 1
last_remesh_step = 0
with tqdm(
desc="Optimize",
total=settings.steps if settings.timeout is None else settings.timeout,
leave=False,
) as tqdm_:
is_last = False
while not is_last:
is_last = (
step == settings.steps
if settings.steps
else time.time() - start > settings.timeout
)
opt.zero_grad()
normals = calc_vertex_normals(vertices, faces)
images = renderer.render(vertices, normals, faces)
loss = (images - target_images).abs().mean()
if isinstance(opt, torch.optim.Adam):
# laplacian regularization
loss = (
loss
+ (vertices * (Laplacian @ vertices)).mean()
* settings.laplacian_weight
)
loss.backward()
if isinstance(opt, torch.optim.Adam):
# learning ramp
ramped_lr = lr * min(
1,
(step - last_remesh_step) * (1 - settings.betas[0]) / settings.ramp,
)
opt.param_groups[0]["lr"] = ramped_lr
opt.step()
# snapshot
with torch.no_grad():
if (
settings.result_interval and step % settings.result_interval == 1
) or is_last:
if settings.method == "ours":
s = snapshot(opt)
else:
s = Snapshot(
step=step,
time=time.time() - start,
vertices=vertices.clone().requires_grad_(False),
faces=faces.clone(),
)
result.snapshots.append(s)
# remesh
if (
settings.remesh_interval is not None
and (step % settings.remesh_interval) == settings.remesh_interval - 1
and not is_last
):
if isinstance(opt, MeshOptimizer):
vertices, faces = opt.remesh()
else:
with torch.no_grad():
edges, _ = calc_edges(faces)
mean_edge_length = (
calc_edge_length(vertices, edges).mean().item()
)
target_edgelen = mean_edge_length * settings.remesh_ratio
target_edgelen = max(target_edgelen, settings.edge_len_lims[0])
v = to_numpy(vertices).astype(np.double)
f = to_numpy(faces).astype(np.int32)
v, f = remesh_botsch(v, f, 5, target_edgelen, True)
vertices = torch.tensor(
v, dtype=torch.float, device=vertices.device
).contiguous()
faces = torch.tensor(
f, dtype=torch.long, device=vertices.device
).contiguous()
opt, lr, vertices, Laplacian = make_optimizer(
settings, vertices, faces
)
last_remesh_step = step
if vertices.shape[0] == 0:
is_last = True # mesh collapsed
if settings.save_images:
save_images(images, outdir / "images")
step += 1
if settings.steps is not None:
tqdm_.update(1)
else:
tqdm_.update(
min(settings.timeout, round(time.time() - start, 3)) - tqdm_.n
)
return result
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