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from dataclasses import dataclass, field
from typing import Any, Optional
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
) # Small variance here
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) # Small variance here
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"],
rotation: Optional[Float[Tensor, "B 3 3"]] = None,
) -> 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 = rotation_6d_to_matrix(self.fc_rotations(x))
scale = self.fc_scale(x)
if rotation is not None:
rotations = rotations @ rotation.to(dtype=rotations.dtype)
env_map = self.reni_env_map(latents, rotations, scale)
return {"illumination": env_map["rgb"]}
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