|
from dataclasses import dataclass, field |
|
from typing import Any |
|
|
|
import torch |
|
import torch.nn as nn |
|
import torch.nn.functional as F |
|
from jaxtyping import Float |
|
from torch import Tensor |
|
|
|
from spar3d.models.illumination.reni.env_map import RENIEnvMap |
|
from spar3d.models.utils import BaseModule |
|
|
|
|
|
def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor: |
|
assert d6.shape[-1] == 6, "Input tensor must have shape (..., 6)" |
|
|
|
def proj_u2a(u, a): |
|
r""" |
|
u: batch x 3 |
|
a: batch x 3 |
|
""" |
|
inner_prod = torch.sum(u * a, dim=-1, keepdim=True) |
|
norm2 = torch.sum(u**2, dim=-1, keepdim=True) |
|
norm2 = torch.clamp(norm2, min=1e-8) |
|
factor = inner_prod / (norm2 + 1e-10) |
|
return factor * u |
|
|
|
x_raw, y_raw = d6[..., :3], d6[..., 3:] |
|
|
|
x = F.normalize(x_raw, dim=-1) |
|
y = F.normalize(y_raw - proj_u2a(x, y_raw), dim=-1) |
|
z = torch.cross(x, y, dim=-1) |
|
|
|
return torch.stack((x, y, z), dim=-1) |
|
|
|
|
|
class ReniLatentCodeEstimator(BaseModule): |
|
@dataclass |
|
class Config(BaseModule.Config): |
|
triplane_features: int = 40 |
|
|
|
n_layers: int = 5 |
|
hidden_features: int = 512 |
|
activation: str = "relu" |
|
|
|
pool: str = "mean" |
|
|
|
reni_env_config: dict = field(default_factory=dict) |
|
|
|
cfg: Config |
|
|
|
def configure(self): |
|
layers = [] |
|
cur_features = self.cfg.triplane_features * 3 |
|
for _ in range(self.cfg.n_layers): |
|
layers.append( |
|
nn.Conv2d( |
|
cur_features, |
|
self.cfg.hidden_features, |
|
kernel_size=3, |
|
padding=0, |
|
stride=2, |
|
) |
|
) |
|
layers.append(self.make_activation(self.cfg.activation)) |
|
|
|
cur_features = self.cfg.hidden_features |
|
|
|
self.layers = nn.Sequential(*layers) |
|
|
|
self.reni_env_map = RENIEnvMap(self.cfg.reni_env_config) |
|
self.latent_dim = self.reni_env_map.field.latent_dim |
|
|
|
self.fc_latents = nn.Linear(self.cfg.hidden_features, self.latent_dim * 3) |
|
nn.init.normal_(self.fc_latents.weight, mean=0.0, std=0.3) |
|
|
|
self.fc_rotations = nn.Linear(self.cfg.hidden_features, 6) |
|
nn.init.constant_(self.fc_rotations.bias, 0.0) |
|
nn.init.normal_( |
|
self.fc_rotations.weight, mean=0.0, std=0.01 |
|
) |
|
|
|
self.fc_scale = nn.Linear(self.cfg.hidden_features, 1) |
|
nn.init.constant_(self.fc_scale.bias, 0.0) |
|
nn.init.normal_(self.fc_scale.weight, mean=0.0, std=0.01) |
|
|
|
def make_activation(self, activation): |
|
if activation == "relu": |
|
return nn.ReLU(inplace=True) |
|
elif activation == "silu": |
|
return nn.SiLU(inplace=True) |
|
else: |
|
raise NotImplementedError |
|
|
|
def forward( |
|
self, |
|
triplane: Float[Tensor, "B 3 F Ht Wt"], |
|
) -> dict[str, Any]: |
|
x = self.layers( |
|
triplane.reshape( |
|
triplane.shape[0], -1, triplane.shape[-2], triplane.shape[-1] |
|
) |
|
) |
|
x = x.mean(dim=[-2, -1]) |
|
|
|
latents = self.fc_latents(x).reshape(-1, self.latent_dim, 3) |
|
rotations = self.fc_rotations(x) |
|
scale = self.fc_scale(x) |
|
|
|
env_map = self.reni_env_map(latents, rotation_6d_to_matrix(rotations), scale) |
|
|
|
return {"illumination": env_map["rgb"]} |
|
|
