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from dataclasses import dataclass
from typing import Dict
import torch
import torch.nn.functional as F
from einops import rearrange, reduce
from ..utils import (
BaseModule,
chunk_batch,
get_activation,
rays_intersect_bbox,
scale_tensor,
)
class TriplaneNeRFRenderer(BaseModule):
@dataclass
class Config(BaseModule.Config):
radius: float
feature_reduction: str = "concat"
density_activation: str = "trunc_exp"
density_bias: float = -1.0
color_activation: str = "sigmoid"
num_samples_per_ray: int = 128
randomized: bool = False
cfg: Config
def configure(self) -> None:
assert self.cfg.feature_reduction in ["concat", "mean"]
self.chunk_size = 0
def set_chunk_size(self, chunk_size: int):
assert (
chunk_size >= 0
), "chunk_size must be a non-negative integer (0 for no chunking)."
self.chunk_size = chunk_size
def query_triplane(
self,
decoder: torch.nn.Module,
positions: torch.Tensor,
triplane: torch.Tensor,
) -> Dict[str, torch.Tensor]:
input_shape = positions.shape[:-1]
positions = positions.view(-1, 3)
# positions in (-radius, radius)
# normalized to (-1, 1) for grid sample
positions = scale_tensor(
positions, (-self.cfg.radius, self.cfg.radius), (-1, 1)
)
def _query_chunk(x):
indices2D: torch.Tensor = torch.stack(
(x[..., [0, 1]], x[..., [0, 2]], x[..., [1, 2]]),
dim=-3,
)
out: torch.Tensor = F.grid_sample(
rearrange(triplane, "Np Cp Hp Wp -> Np Cp Hp Wp", Np=3),
rearrange(indices2D, "Np N Nd -> Np () N Nd", Np=3),
align_corners=False,
mode="bilinear",
)
if self.cfg.feature_reduction == "concat":
out = rearrange(out, "Np Cp () N -> N (Np Cp)", Np=3)
elif self.cfg.feature_reduction == "mean":
out = reduce(out, "Np Cp () N -> N Cp", Np=3, reduction="mean")
else:
raise NotImplementedError
net_out: Dict[str, torch.Tensor] = decoder(out)
return net_out
if self.chunk_size > 0:
net_out = chunk_batch(_query_chunk, self.chunk_size, positions)
else:
net_out = _query_chunk(positions)
net_out["density_act"] = get_activation(self.cfg.density_activation)(
net_out["density"] + self.cfg.density_bias
)
net_out["color"] = get_activation(self.cfg.color_activation)(
net_out["features"]
)
net_out = {k: v.view(*input_shape, -1) for k, v in net_out.items()}
return net_out
def _forward(
self,
decoder: torch.nn.Module,
triplane: torch.Tensor,
rays_o: torch.Tensor,
rays_d: torch.Tensor,
**kwargs,
):
rays_shape = rays_o.shape[:-1]
rays_o = rays_o.view(-1, 3)
rays_d = rays_d.view(-1, 3)
n_rays = rays_o.shape[0]
t_near, t_far, rays_valid = rays_intersect_bbox(rays_o, rays_d, self.cfg.radius)
t_near, t_far = t_near[rays_valid], t_far[rays_valid]
t_vals = torch.linspace(
0, 1, self.cfg.num_samples_per_ray + 1, device=triplane.device
)
t_mid = (t_vals[:-1] + t_vals[1:]) / 2.0
z_vals = t_near * (1 - t_mid[None]) + t_far * t_mid[None] # (N_rays, N_samples)
xyz = (
rays_o[:, None, :] + z_vals[..., None] * rays_d[..., None, :]
) # (N_rays, N_sample, 3)
mlp_out = self.query_triplane(
decoder=decoder,
positions=xyz,
triplane=triplane,
)
eps = 1e-10
# deltas = z_vals[:, 1:] - z_vals[:, :-1] # (N_rays, N_samples)
deltas = t_vals[1:] - t_vals[:-1] # (N_rays, N_samples)
alpha = 1 - torch.exp(
-deltas * mlp_out["density_act"][..., 0]
) # (N_rays, N_samples)
accum_prod = torch.cat(
[
torch.ones_like(alpha[:, :1]),
torch.cumprod(1 - alpha[:, :-1] + eps, dim=-1),
],
dim=-1,
)
weights = alpha * accum_prod # (N_rays, N_samples)
comp_rgb_ = (weights[..., None] * mlp_out["color"]).sum(dim=-2) # (N_rays, 3)
opacity_ = weights.sum(dim=-1) # (N_rays)
comp_rgb = torch.zeros(
n_rays, 3, dtype=comp_rgb_.dtype, device=comp_rgb_.device
)
opacity = torch.zeros(n_rays, dtype=opacity_.dtype, device=opacity_.device)
comp_rgb[rays_valid] = comp_rgb_
opacity[rays_valid] = opacity_
comp_rgb += 1 - opacity[..., None]
comp_rgb = comp_rgb.view(*rays_shape, 3)
return comp_rgb
def forward(
self,
decoder: torch.nn.Module,
triplane: torch.Tensor,
rays_o: torch.Tensor,
rays_d: torch.Tensor,
) -> Dict[str, torch.Tensor]:
if triplane.ndim == 4:
comp_rgb = self._forward(decoder, triplane, rays_o, rays_d)
else:
comp_rgb = torch.stack(
[
self._forward(decoder, triplane[i], rays_o[i], rays_d[i])
for i in range(triplane.shape[0])
],
dim=0,
)
return comp_rgb
def train(self, mode=True):
self.randomized = mode and self.cfg.randomized
return super().train(mode=mode)
def eval(self):
self.randomized = False
return super().eval()
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