Spaces:
Sleeping
Sleeping
File size: 5,680 Bytes
ff49a48 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 |
from dataclasses import dataclass, field
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()
|