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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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