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Running
on
L4
| 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): | |
| 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"]} | |