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import os
from dataclasses import dataclass, field
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import threestudio
from threestudio.models.geometry.base import BaseImplicitGeometry, contract_to_unisphere
from threestudio.models.mesh import Mesh
from threestudio.models.networks import get_encoding, get_mlp
from threestudio.utils.misc import broadcast, get_rank
from threestudio.utils.typing import *
@threestudio.register("implicit-sdf")
class ImplicitSDF(BaseImplicitGeometry):
@dataclass
class Config(BaseImplicitGeometry.Config):
n_input_dims: int = 3
n_feature_dims: int = 3
pos_encoding_config: dict = field(
default_factory=lambda: {
"otype": "HashGrid",
"n_levels": 16,
"n_features_per_level": 2,
"log2_hashmap_size": 19,
"base_resolution": 16,
"per_level_scale": 1.447269237440378,
}
)
mlp_network_config: dict = field(
default_factory=lambda: {
"otype": "VanillaMLP",
"activation": "ReLU",
"output_activation": "none",
"n_neurons": 64,
"n_hidden_layers": 1,
}
)
normal_type: Optional[
str
] = "finite_difference" # in ['pred', 'finite_difference', 'finite_difference_laplacian']
finite_difference_normal_eps: Union[
float, str
] = 0.01 # in [float, "progressive"]
shape_init: Optional[str] = None
shape_init_params: Optional[Any] = None
shape_init_mesh_up: str = "+z"
shape_init_mesh_front: str = "+x"
force_shape_init: bool = False
sdf_bias: Union[float, str] = 0.0
sdf_bias_params: Optional[Any] = None
# no need to removal outlier for SDF
isosurface_remove_outliers: bool = False
cfg: Config
def configure(self) -> None:
super().configure()
self.encoding = get_encoding(
self.cfg.n_input_dims, self.cfg.pos_encoding_config
)
self.sdf_network = get_mlp(
self.encoding.n_output_dims, 1, self.cfg.mlp_network_config
)
if self.cfg.n_feature_dims > 0:
self.feature_network = get_mlp(
self.encoding.n_output_dims,
self.cfg.n_feature_dims,
self.cfg.mlp_network_config,
)
if self.cfg.normal_type == "pred":
self.normal_network = get_mlp(
self.encoding.n_output_dims, 3, self.cfg.mlp_network_config
)
if self.cfg.isosurface_deformable_grid:
assert (
self.cfg.isosurface_method == "mt"
), "isosurface_deformable_grid only works with mt"
self.deformation_network = get_mlp(
self.encoding.n_output_dims, 3, self.cfg.mlp_network_config
)
self.finite_difference_normal_eps: Optional[float] = None
def initialize_shape(self) -> None:
if self.cfg.shape_init is None and not self.cfg.force_shape_init:
return
# do not initialize shape if weights are provided
if self.cfg.weights is not None and not self.cfg.force_shape_init:
return
if self.cfg.sdf_bias != 0.0:
threestudio.warn(
"shape_init and sdf_bias are both specified, which may lead to unexpected results."
)
get_gt_sdf: Callable[[Float[Tensor, "N 3"]], Float[Tensor, "N 1"]]
assert isinstance(self.cfg.shape_init, str)
if self.cfg.shape_init == "ellipsoid":
assert (
isinstance(self.cfg.shape_init_params, Sized)
and len(self.cfg.shape_init_params) == 3
)
size = torch.as_tensor(self.cfg.shape_init_params).to(self.device)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return ((points_rand / size) ** 2).sum(
dim=-1, keepdim=True
).sqrt() - 1.0 # pseudo signed distance of an ellipsoid
get_gt_sdf = func
elif self.cfg.shape_init == "sphere":
assert isinstance(self.cfg.shape_init_params, float)
radius = self.cfg.shape_init_params
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
return (points_rand**2).sum(dim=-1, keepdim=True).sqrt() - radius
get_gt_sdf = func
elif self.cfg.shape_init.startswith("mesh:"):
assert isinstance(self.cfg.shape_init_params, float)
mesh_path = self.cfg.shape_init[5:]
if not os.path.exists(mesh_path):
raise ValueError(f"Mesh file {mesh_path} does not exist.")
import trimesh
scene = trimesh.load(mesh_path)
if isinstance(scene, trimesh.Trimesh):
mesh = scene
elif isinstance(scene, trimesh.scene.Scene):
mesh = trimesh.Trimesh()
for obj in scene.geometry.values():
mesh = trimesh.util.concatenate([mesh, obj])
else:
raise ValueError(f"Unknown mesh type at {mesh_path}.")
# move to center
centroid = mesh.vertices.mean(0)
mesh.vertices = mesh.vertices - centroid
# align to up-z and front-x
dirs = ["+x", "+y", "+z", "-x", "-y", "-z"]
dir2vec = {
"+x": np.array([1, 0, 0]),
"+y": np.array([0, 1, 0]),
"+z": np.array([0, 0, 1]),
"-x": np.array([-1, 0, 0]),
"-y": np.array([0, -1, 0]),
"-z": np.array([0, 0, -1]),
}
if (
self.cfg.shape_init_mesh_up not in dirs
or self.cfg.shape_init_mesh_front not in dirs
):
raise ValueError(
f"shape_init_mesh_up and shape_init_mesh_front must be one of {dirs}."
)
if self.cfg.shape_init_mesh_up[1] == self.cfg.shape_init_mesh_front[1]:
raise ValueError(
"shape_init_mesh_up and shape_init_mesh_front must be orthogonal."
)
z_, x_ = (
dir2vec[self.cfg.shape_init_mesh_up],
dir2vec[self.cfg.shape_init_mesh_front],
)
y_ = np.cross(z_, x_)
std2mesh = np.stack([x_, y_, z_], axis=0).T
mesh2std = np.linalg.inv(std2mesh)
# scaling
scale = np.abs(mesh.vertices).max()
mesh.vertices = mesh.vertices / scale * self.cfg.shape_init_params
mesh.vertices = np.dot(mesh2std, mesh.vertices.T).T
from pysdf import SDF
sdf = SDF(mesh.vertices, mesh.faces)
def func(points_rand: Float[Tensor, "N 3"]) -> Float[Tensor, "N 1"]:
# add a negative signed here
# as in pysdf the inside of the shape has positive signed distance
return torch.from_numpy(-sdf(points_rand.cpu().numpy())).to(
points_rand
)[..., None]
get_gt_sdf = func
else:
raise ValueError(
f"Unknown shape initialization type: {self.cfg.shape_init}"
)
# Initialize SDF to a given shape when no weights are provided or force_shape_init is True
optim = torch.optim.Adam(self.parameters(), lr=1e-3)
from tqdm import tqdm
for _ in tqdm(
range(1000),
desc=f"Initializing SDF to a(n) {self.cfg.shape_init}:",
disable=get_rank() != 0,
):
points_rand = (
torch.rand((10000, 3), dtype=torch.float32).to(self.device) * 2.0 - 1.0
)
sdf_gt = get_gt_sdf(points_rand)
sdf_pred = self.forward_sdf(points_rand)
loss = F.mse_loss(sdf_pred, sdf_gt)
optim.zero_grad()
loss.backward()
optim.step()
# explicit broadcast to ensure param consistency across ranks
for param in self.parameters():
broadcast(param, src=0)
def get_shifted_sdf(
self, points: Float[Tensor, "*N Di"], sdf: Float[Tensor, "*N 1"]
) -> Float[Tensor, "*N 1"]:
sdf_bias: Union[float, Float[Tensor, "*N 1"]]
if self.cfg.sdf_bias == "ellipsoid":
assert (
isinstance(self.cfg.sdf_bias_params, Sized)
and len(self.cfg.sdf_bias_params) == 3
)
size = torch.as_tensor(self.cfg.sdf_bias_params).to(points)
sdf_bias = ((points / size) ** 2).sum(
dim=-1, keepdim=True
).sqrt() - 1.0 # pseudo signed distance of an ellipsoid
elif self.cfg.sdf_bias == "sphere":
assert isinstance(self.cfg.sdf_bias_params, float)
radius = self.cfg.sdf_bias_params
sdf_bias = (points**2).sum(dim=-1, keepdim=True).sqrt() - radius
elif isinstance(self.cfg.sdf_bias, float):
sdf_bias = self.cfg.sdf_bias
else:
raise ValueError(f"Unknown sdf bias {self.cfg.sdf_bias}")
return sdf + sdf_bias
def forward(
self, points: Float[Tensor, "*N Di"], output_normal: bool = False
) -> Dict[str, Float[Tensor, "..."]]:
grad_enabled = torch.is_grad_enabled()
if output_normal and self.cfg.normal_type == "analytic":
torch.set_grad_enabled(True)
points.requires_grad_(True)
points_unscaled = points # points in the original scale
points = contract_to_unisphere(
points, self.bbox, self.unbounded
) # points normalized to (0, 1)
enc = self.encoding(points.view(-1, self.cfg.n_input_dims))
sdf = self.sdf_network(enc).view(*points.shape[:-1], 1)
sdf = self.get_shifted_sdf(points_unscaled, sdf)
output = {"sdf": sdf}
if self.cfg.n_feature_dims > 0:
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
output.update({"features": features})
if output_normal:
if (
self.cfg.normal_type == "finite_difference"
or self.cfg.normal_type == "finite_difference_laplacian"
):
assert self.finite_difference_normal_eps is not None
eps: float = self.finite_difference_normal_eps
if self.cfg.normal_type == "finite_difference_laplacian":
offsets: Float[Tensor, "6 3"] = torch.as_tensor(
[
[eps, 0.0, 0.0],
[-eps, 0.0, 0.0],
[0.0, eps, 0.0],
[0.0, -eps, 0.0],
[0.0, 0.0, eps],
[0.0, 0.0, -eps],
]
).to(points_unscaled)
points_offset: Float[Tensor, "... 6 3"] = (
points_unscaled[..., None, :] + offsets
).clamp(-self.cfg.radius, self.cfg.radius)
sdf_offset: Float[Tensor, "... 6 1"] = self.forward_sdf(
points_offset
)
sdf_grad = (
0.5
* (sdf_offset[..., 0::2, 0] - sdf_offset[..., 1::2, 0])
/ eps
)
else:
offsets: Float[Tensor, "3 3"] = torch.as_tensor(
[[eps, 0.0, 0.0], [0.0, eps, 0.0], [0.0, 0.0, eps]]
).to(points_unscaled)
points_offset: Float[Tensor, "... 3 3"] = (
points_unscaled[..., None, :] + offsets
).clamp(-self.cfg.radius, self.cfg.radius)
sdf_offset: Float[Tensor, "... 3 1"] = self.forward_sdf(
points_offset
)
sdf_grad = (sdf_offset[..., 0::1, 0] - sdf) / eps
normal = F.normalize(sdf_grad, dim=-1)
elif self.cfg.normal_type == "pred":
normal = self.normal_network(enc).view(*points.shape[:-1], 3)
normal = F.normalize(normal, dim=-1)
sdf_grad = normal
elif self.cfg.normal_type == "analytic":
sdf_grad = -torch.autograd.grad(
sdf,
points_unscaled,
grad_outputs=torch.ones_like(sdf),
create_graph=True,
)[0]
normal = F.normalize(sdf_grad, dim=-1)
if not grad_enabled:
sdf_grad = sdf_grad.detach()
normal = normal.detach()
else:
raise AttributeError(f"Unknown normal type {self.cfg.normal_type}")
output.update(
{"normal": normal, "shading_normal": normal, "sdf_grad": sdf_grad}
)
return output
def forward_sdf(self, points: Float[Tensor, "*N Di"]) -> Float[Tensor, "*N 1"]:
points_unscaled = points
points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)
sdf = self.sdf_network(
self.encoding(points.reshape(-1, self.cfg.n_input_dims))
).reshape(*points.shape[:-1], 1)
sdf = self.get_shifted_sdf(points_unscaled, sdf)
return sdf
def forward_field(
self, points: Float[Tensor, "*N Di"]
) -> Tuple[Float[Tensor, "*N 1"], Optional[Float[Tensor, "*N 3"]]]:
points_unscaled = points
points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)
enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))
sdf = self.sdf_network(enc).reshape(*points.shape[:-1], 1)
sdf = self.get_shifted_sdf(points_unscaled, sdf)
deformation: Optional[Float[Tensor, "*N 3"]] = None
if self.cfg.isosurface_deformable_grid:
deformation = self.deformation_network(enc).reshape(*points.shape[:-1], 3)
return sdf, deformation
def forward_level(
self, field: Float[Tensor, "*N 1"], threshold: float
) -> Float[Tensor, "*N 1"]:
return field - threshold
def export(self, points: Float[Tensor, "*N Di"], **kwargs) -> Dict[str, Any]:
out: Dict[str, Any] = {}
if self.cfg.n_feature_dims == 0:
return out
points_unscaled = points
points = contract_to_unisphere(points_unscaled, self.bbox, self.unbounded)
enc = self.encoding(points.reshape(-1, self.cfg.n_input_dims))
features = self.feature_network(enc).view(
*points.shape[:-1], self.cfg.n_feature_dims
)
out.update(
{
"features": features,
}
)
return out
def update_step(self, epoch: int, global_step: int, on_load_weights: bool = False):
if (
self.cfg.normal_type == "finite_difference"
or self.cfg.normal_type == "finite_difference_laplacian"
):
if isinstance(self.cfg.finite_difference_normal_eps, float):
self.finite_difference_normal_eps = (
self.cfg.finite_difference_normal_eps
)
elif self.cfg.finite_difference_normal_eps == "progressive":
# progressive finite difference eps from Neuralangelo
# https://arxiv.org/abs/2306.03092
hg_conf: Any = self.cfg.pos_encoding_config
assert (
hg_conf.otype == "ProgressiveBandHashGrid"
), "finite_difference_normal_eps=progressive only works with ProgressiveBandHashGrid"
current_level = min(
hg_conf.start_level
+ max(global_step - hg_conf.start_step, 0) // hg_conf.update_steps,
hg_conf.n_levels,
)
grid_res = hg_conf.base_resolution * hg_conf.per_level_scale ** (
current_level - 1
)
grid_size = 2 * self.cfg.radius / grid_res
if grid_size != self.finite_difference_normal_eps:
threestudio.info(
f"Update finite_difference_normal_eps to {grid_size}"
)
self.finite_difference_normal_eps = grid_size
else:
raise ValueError(
f"Unknown finite_difference_normal_eps={self.cfg.finite_difference_normal_eps}"
)
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