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from dataclasses import dataclass, field
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from typing import Any, Optional
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from jaxtyping import Float
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from torch import Tensor
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from spar3d.models.illumination.reni.env_map import RENIEnvMap
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from spar3d.models.utils import BaseModule
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def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor:
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assert d6.shape[-1] == 6, "Input tensor must have shape (..., 6)"
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def proj_u2a(u, a):
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r"""
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u: batch x 3
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a: batch x 3
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"""
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inner_prod = torch.sum(u * a, dim=-1, keepdim=True)
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norm2 = torch.sum(u**2, dim=-1, keepdim=True)
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norm2 = torch.clamp(norm2, min=1e-8)
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factor = inner_prod / (norm2 + 1e-10)
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return factor * u
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x_raw, y_raw = d6[..., :3], d6[..., 3:]
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x = F.normalize(x_raw, dim=-1)
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y = F.normalize(y_raw - proj_u2a(x, y_raw), dim=-1)
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z = torch.cross(x, y, dim=-1)
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return torch.stack((x, y, z), dim=-1)
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class ReniLatentCodeEstimator(BaseModule):
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@dataclass
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class Config(BaseModule.Config):
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triplane_features: int = 40
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n_layers: int = 5
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hidden_features: int = 512
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activation: str = "relu"
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pool: str = "mean"
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reni_env_config: dict = field(default_factory=dict)
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cfg: Config
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def configure(self):
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layers = []
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cur_features = self.cfg.triplane_features * 3
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for _ in range(self.cfg.n_layers):
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layers.append(
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nn.Conv2d(
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cur_features,
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self.cfg.hidden_features,
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kernel_size=3,
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padding=0,
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stride=2,
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)
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)
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layers.append(self.make_activation(self.cfg.activation))
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cur_features = self.cfg.hidden_features
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self.layers = nn.Sequential(*layers)
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self.reni_env_map = RENIEnvMap(self.cfg.reni_env_config)
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self.latent_dim = self.reni_env_map.field.latent_dim
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self.fc_latents = nn.Linear(self.cfg.hidden_features, self.latent_dim * 3)
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nn.init.normal_(self.fc_latents.weight, mean=0.0, std=0.3)
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self.fc_rotations = nn.Linear(self.cfg.hidden_features, 6)
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nn.init.constant_(self.fc_rotations.bias, 0.0)
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nn.init.normal_(
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self.fc_rotations.weight, mean=0.0, std=0.01
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)
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self.fc_scale = nn.Linear(self.cfg.hidden_features, 1)
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nn.init.constant_(self.fc_scale.bias, 0.0)
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nn.init.normal_(self.fc_scale.weight, mean=0.0, std=0.01)
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def make_activation(self, activation):
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if activation == "relu":
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return nn.ReLU(inplace=True)
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elif activation == "silu":
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return nn.SiLU(inplace=True)
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else:
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raise NotImplementedError
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def forward(
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self,
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triplane: Float[Tensor, "B 3 F Ht Wt"],
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rotation: Optional[Float[Tensor, "B 3 3"]] = None,
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) -> dict[str, Any]:
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x = self.layers(
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triplane.reshape(
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triplane.shape[0], -1, triplane.shape[-2], triplane.shape[-1]
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)
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)
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x = x.mean(dim=[-2, -1])
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latents = self.fc_latents(x).reshape(-1, self.latent_dim, 3)
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rotations = rotation_6d_to_matrix(self.fc_rotations(x))
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scale = self.fc_scale(x)
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if rotation is not None:
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rotations = rotations @ rotation.to(dtype=rotations.dtype)
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env_map = self.reni_env_map(latents, rotations, scale)
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return {"illumination": env_map["rgb"]}
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