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network_basis.py
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network_basis.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from encoding import get_encoder
from activation import trunc_exp
from .renderer import NeRFRenderer
class NeRFNetwork(NeRFRenderer):
def __init__(self,
encoding="tiledgrid",
encoding_dir="sphere_harmonics",
encoding_time="frequency",
encoding_bg="hashgrid",
num_layers=2,
hidden_dim=64,
geo_feat_dim=32,
num_layers_color=3,
hidden_dim_color=64,
num_layers_bg=2,
hidden_dim_bg=64,
sigma_basis_dim=32,
color_basis_dim=8,
num_layers_basis=5,
hidden_dim_basis=128,
bound=1,
**kwargs,
):
super().__init__(bound, **kwargs)
# basis network
self.num_layers_basis = num_layers_basis
self.hidden_dim_basis = hidden_dim_basis
self.sigma_basis_dim = sigma_basis_dim
self.color_basis_dim = color_basis_dim
self.encoder_time, self.in_dim_time = get_encoder(encoding_time, input_dim=1, multires=6)
basis_net = []
for l in range(num_layers_basis):
if l == 0:
in_dim = self.in_dim_time
else:
in_dim = hidden_dim_basis
if l == num_layers_basis - 1:
out_dim = self.sigma_basis_dim + self.color_basis_dim
else:
out_dim = hidden_dim_basis
basis_net.append(nn.Linear(in_dim, out_dim, bias=False))
self.basis_net = nn.ModuleList(basis_net)
# sigma network
self.num_layers = num_layers
self.hidden_dim = hidden_dim
self.geo_feat_dim = geo_feat_dim
self.encoder, self.in_dim = get_encoder(encoding, desired_resolution=2048 * bound)
sigma_net = []
for l in range(num_layers):
if l == 0:
in_dim = self.in_dim
else:
in_dim = hidden_dim
if l == num_layers - 1:
out_dim = self.sigma_basis_dim + self.geo_feat_dim # SB sigma + features for color
else:
out_dim = hidden_dim
sigma_net.append(nn.Linear(in_dim, out_dim, bias=False))
self.sigma_net = nn.ModuleList(sigma_net)
# color network
self.num_layers_color = num_layers_color
self.hidden_dim_color = hidden_dim_color
self.encoder_dir, self.in_dim_dir = get_encoder(encoding_dir)
color_net = []
for l in range(num_layers_color):
if l == 0:
in_dim = self.in_dim_dir + self.geo_feat_dim
else:
in_dim = hidden_dim_color
if l == num_layers_color - 1:
out_dim = 3 * self.color_basis_dim # 3 * CB rgb
else:
out_dim = hidden_dim_color
color_net.append(nn.Linear(in_dim, out_dim, bias=False))
self.color_net = nn.ModuleList(color_net)
# background network
if self.bg_radius > 0:
self.num_layers_bg = num_layers_bg
self.hidden_dim_bg = hidden_dim_bg
self.encoder_bg, self.in_dim_bg = get_encoder(encoding_bg, input_dim=2, num_levels=4, log2_hashmap_size=19, desired_resolution=2048) # much smaller hashgrid
bg_net = []
for l in range(num_layers_bg):
if l == 0:
in_dim = self.in_dim_bg + self.in_dim_dir
else:
in_dim = hidden_dim_bg
if l == num_layers_bg - 1:
out_dim = 3 # 3 rgb
else:
out_dim = hidden_dim_bg
bg_net.append(nn.Linear(in_dim, out_dim, bias=False))
self.bg_net = nn.ModuleList(bg_net)
else:
self.bg_net = None
def forward(self, x, d, t):
# x: [N, 3], in [-bound, bound]
# d: [N, 3], nomalized in [-1, 1]
# t: [1, 1], in [0, 1]
# time --> basis
enc_t = self.encoder_time(t) # [1, 1] --> [1, C']
h = enc_t
for l in range(self.num_layers_basis):
h = self.basis_net[l](h)
if l != self.num_layers_basis - 1:
h = F.relu(h, inplace=True)
sigma_basis = h[0, :self.sigma_basis_dim]
color_basis = h[0, self.sigma_basis_dim:]
# sigma
x = self.encoder(x, bound=self.bound)
h = x
for l in range(self.num_layers):
h = self.sigma_net[l](h)
if l != self.num_layers - 1:
h = F.relu(h, inplace=True)
sigma = trunc_exp(h[..., :self.sigma_basis_dim] @ sigma_basis)
geo_feat = h[..., self.sigma_basis_dim:]
# color
d = self.encoder_dir(d)
h = torch.cat([d, geo_feat], dim=-1)
for l in range(self.num_layers_color):
h = self.color_net[l](h)
if l != self.num_layers_color - 1:
h = F.relu(h, inplace=True)
# sigmoid activation for rgb
rgbs = torch.sigmoid(h.view(-1, 3, self.color_basis_dim) @ color_basis)
return sigma, rgbs, None
def density(self, x, t):
# x: [N, 3], in [-bound, bound]
# t: [1, 1], in [0, 1]
results = {}
# time --> basis
enc_t = self.encoder_time(t) # [1, 1] --> [1, C']
h = enc_t
for l in range(self.num_layers_basis):
h = self.basis_net[l](h)
if l != self.num_layers_basis - 1:
h = F.relu(h, inplace=True)
sigma_basis = h[0, :self.sigma_basis_dim]
color_basis = h[0, self.sigma_basis_dim:]
# sigma
x = self.encoder(x, bound=self.bound)
h = x
for l in range(self.num_layers):
h = self.sigma_net[l](h)
if l != self.num_layers - 1:
h = F.relu(h, inplace=True)
sigma = trunc_exp(h[..., :self.sigma_basis_dim] @ sigma_basis)
geo_feat = h[..., self.sigma_basis_dim:]
results['sigma'] = sigma
results['geo_feat'] = geo_feat
# results['color_basis'] = color_basis
return results
def background(self, x, d):
# x: [N, 2], in [-1, 1]
h = self.encoder_bg(x) # [N, C]
d = self.encoder_dir(d)
h = torch.cat([d, h], dim=-1)
for l in range(self.num_layers_bg):
h = self.bg_net[l](h)
if l != self.num_layers_bg - 1:
h = F.relu(h, inplace=True)
# sigmoid activation for rgb
rgbs = torch.sigmoid(h)
return rgbs
# TODO: non cuda-ray mode is broken for now... (how to pass color_basis to self.color())
# # allow masked inference
# def color(self, x, d, mask=None, geo_feat=None, **kwargs):
# # x: [N, 3] in [-bound, bound]
# # t: [1, 1], in [0, 1]
# # mask: [N,], bool, indicates where we actually needs to compute rgb.
# if mask is not None:
# rgbs = torch.zeros(mask.shape[0], 3, dtype=x.dtype, device=x.device) # [N, 3]
# # in case of empty mask
# if not mask.any():
# return rgbs
# x = x[mask]
# d = d[mask]
# geo_feat = geo_feat[mask]
# d = self.encoder_dir(d)
# h = torch.cat([d, geo_feat], dim=-1)
# for l in range(self.num_layers_color):
# h = self.color_net[l](h)
# if l != self.num_layers_color - 1:
# h = F.relu(h, inplace=True)
# # sigmoid activation for rgb
# h = torch.sigmoid(h)
# if mask is not None:
# rgbs[mask] = h.to(rgbs.dtype) # fp16 --> fp32
# else:
# rgbs = h
# return rgbs
# optimizer utils
def get_params(self, lr, lr_net):
params = [
{'params': self.encoder.parameters(), 'lr': lr},
{'params': self.sigma_net.parameters(), 'lr': lr_net},
{'params': self.encoder_dir.parameters(), 'lr': lr},
{'params': self.color_net.parameters(), 'lr': lr_net},
{'params': self.encoder_time.parameters(), 'lr': lr},
{'params': self.basis_net.parameters(), 'lr': lr_net},
]
if self.bg_radius > 0:
params.append({'params': self.encoder_bg.parameters(), 'lr': lr})
params.append({'params': self.bg_net.parameters(), 'lr': lr_net})
return params