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import torch
import torch.nn as nn
import torch.nn.functional as F
from activation import trunc_exp
from .renderer import NeRFRenderer
import numpy as np
import tinycudann as tcnn
class MLP(nn.Module):
def __init__(self, dim_in, dim_out, dim_hidden, num_layers, bias=True):
super().__init__()
self.dim_in = dim_in
self.dim_out = dim_out
self.dim_hidden = dim_hidden
self.num_layers = num_layers
net = []
for l in range(num_layers):
net.append(nn.Linear(self.dim_in if l == 0 else self.dim_hidden, self.dim_out if l == num_layers - 1 else self.dim_hidden, bias=bias))
self.net = nn.ModuleList(net)
def forward(self, x):
for l in range(self.num_layers):
x = self.net[l](x)
if l != self.num_layers - 1:
x = F.relu(x, inplace=True)
return x
class NeRFNetwork(NeRFRenderer):
def __init__(self,
opt,
num_layers=3,
hidden_dim=64,
num_layers_bg=2,
hidden_dim_bg=64,
):
super().__init__(opt)
self.num_layers = num_layers
self.hidden_dim = hidden_dim
per_level_scale = np.exp2(np.log2(2048 * self.bound / 16) / (16 - 1))
self.encoder = tcnn.Encoding(
n_input_dims=3,
encoding_config={
"otype": "HashGrid",
"n_levels": 16,
"n_features_per_level": 2,
"log2_hashmap_size": 19,
"base_resolution": 16,
"per_level_scale": per_level_scale,
},
)
self.sigma_net = MLP(32, 4, hidden_dim, num_layers, bias=True)
# background network
if self.bg_radius > 0:
self.num_layers_bg = num_layers_bg
self.hidden_dim_bg = hidden_dim_bg
self.encoder_bg = tcnn.Encoding(
n_input_dims=2,
encoding_config={
"otype": "HashGrid",
"n_levels": 4,
"n_features_per_level": 2,
"log2_hashmap_size": 16,
"base_resolution": 16,
"per_level_scale": 1.5,
},
)
self.bg_net = MLP(8, 3, hidden_dim_bg, num_layers_bg, bias=True)
else:
self.bg_net = None
def gaussian(self, x):
# x: [B, N, 3]
d = (x ** 2).sum(-1)
g = 5 * torch.exp(-d / (2 * 0.2 ** 2))
return g
def common_forward(self, x):
# x: [N, 3], in [-bound, bound]
# sigma
h = (x + self.bound) / (2 * self.bound) # to [0, 1]
h = self.encoder(h)
h = self.sigma_net(h)
sigma = trunc_exp(h[..., 0] + self.gaussian(x))
albedo = torch.sigmoid(h[..., 1:])
return sigma, albedo
def forward(self, x, d, l=None, ratio=1, shading='albedo'):
# x: [N, 3], in [-bound, bound]
# d: [N, 3], view direction, nomalized in [-1, 1]
# l: [3], plane light direction, nomalized in [-1, 1]
# ratio: scalar, ambient ratio, 1 == no shading (albedo only)
if shading == 'albedo':
# no need to query normal
sigma, color = self.common_forward(x)
normal = None
else:
# query normal
has_grad = torch.is_grad_enabled()
with torch.enable_grad():
x.requires_grad_(True)
sigma, albedo = self.common_forward(x)
# query gradient
normal = torch.autograd.grad(torch.sum(sigma), x, create_graph=True)[0] # [N, 3]
# normalize...
normal = normal / (torch.norm(normal, dim=-1, keepdim=True) + 1e-9)
normal[torch.isnan(normal)] = 0
if not has_grad:
normal = normal.detach()
# light direction (random if not provided)
if l is None:
l = torch.randn(3, device=x.device, dtype=torch.float)
l = l / (torch.norm(l, dim=-1, keepdim=True) + 1e-9)
# lambertian shading
lambertian = ratio + (1 - ratio) * (normal @ l).clamp(min=0) # [N,]
if shading == 'textureless':
color = lambertian.unsqueeze(-1).repeat(1, 3)
elif shading == 'normal':
color = (normal + 1) / 2
else: # 'lambertian'
color = albedo * lambertian.unsqueeze(-1)
return sigma, color, normal
def density(self, x):
# x: [N, 3], in [-bound, bound]
sigma, _ = self.common_forward(x)
return {
'sigma': sigma
}
def background(self, x, d):
# x: [N, 2], in [-1, 1]
h = (x + 1) / (2 * 1) # to [0, 1]
h = self.encoder_bg(h) # [N, C]
h = self.bg_net(h)
# sigmoid activation for rgb
rgbs = torch.sigmoid(h)
return rgbs
# optimizer utils
def get_params(self, lr):
params = [
{'params': self.encoder.parameters(), 'lr': lr * 10},
{'params': self.sigma_net.parameters(), 'lr': lr},
]
if self.bg_radius > 0:
params.append({'params': self.encoder_bg.parameters(), 'lr': lr * 10})
params.append({'params': self.bg_net.parameters(), 'lr': lr})
return params |