File size: 8,521 Bytes
ff49a48 d08dc68 ff49a48 d08dc68 ff49a48 d08dc68 ff49a48 d08dc68 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 |
from dataclasses import dataclass, field
from typing import Dict, Optional
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 make_step_grid(self,device, resolution: int, chunk_size: int = 32):
coords = torch.linspace(-1.0, 1.0, resolution, device = device)
x, y, z = torch.meshgrid(coords[0:chunk_size], coords, coords, indexing="ij")
x = x.reshape(-1, 1)
y = y.reshape(-1, 1)
z = z.reshape(-1, 1)
verts = torch.cat([x, y, z], dim = -1).view(-1, 3)
indices2D: torch.Tensor = torch.stack(
(verts[..., [0, 1]], verts[..., [0, 2]], verts[..., [1, 2]]),
dim=-3,
)
return indices2D
def query_triplane_volume_density(self, decoder: torch.nn.Module, triplane: torch.Tensor, resolution: int, sample_count: int = 1024 * 1024 * 4) -> torch.Tensor:
layer_count = sample_count // (resolution * resolution)
out_list = self.do_query_triplane_volume_density(decoder, triplane, resolution, layer_count)
return get_activation(self.cfg.density_activation)(
out_list.view([resolution * resolution * resolution, 1]) + self.cfg.density_bias
)
def do_query_triplane_volume_density(self, decoder: torch.nn.Module, triplane: torch.Tensor, resolution: int, layer_count: int) -> torch.Tensor:
step = 2.0 * layer_count / (resolution - 1)
indices2D = self.make_step_grid(triplane.device, resolution, layer_count)
out_list = torch.zeros([resolution, resolution * resolution, 1], device = triplane.device
)
for i in range(0, resolution, layer_count):
if i + layer_count > resolution:
layer_count = resolution - i
indices2D = indices2D[..., :resolution * resolution * layer_count, :]
density_step = self.sample_step_triplane_volume_density(decoder, triplane, indices2D)
# todo directly march cube
out_list[i:i + layer_count] = density_step.view([layer_count, resolution * resolution, 1])
#out_list.append(net_out['density'])
indices2D.transpose(1, 2)[0, 0] += step
indices2D.transpose(1, 2)[1, 0] += step
return out_list
def sample_step_triplane_volume_density(self, decoder, triplane, indices2D):
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['density']
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()
|