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# The codes are from NeuS
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from models.embedder import get_embedder
class SDFNetwork(nn.Module):
def __init__(self,
d_in,
d_out,
d_hidden,
n_layers,
skip_in=(4,),
multires=0,
bias=0.5,
scale=1,
geometric_init=True,
weight_norm=True,
activation='softplus',
conditional_type='multiply'):
super(SDFNetwork, self).__init__()
dims = [d_in] + [d_hidden for _ in range(n_layers)] + [d_out]
self.embed_fn_fine = None
if multires > 0:
embed_fn, input_ch = get_embedder(multires, input_dims=d_in, normalize=False)
self.embed_fn_fine = embed_fn
dims[0] = input_ch
self.num_layers = len(dims)
self.skip_in = skip_in
self.scale = scale
for l in range(0, self.num_layers - 1):
if l + 1 in self.skip_in:
out_dim = dims[l + 1] - dims[0]
else:
out_dim = dims[l + 1]
lin = nn.Linear(dims[l], out_dim)
if geometric_init:
if l == self.num_layers - 2:
torch.nn.init.normal_(lin.weight, mean=np.sqrt(np.pi) / np.sqrt(dims[l]), std=0.0001)
torch.nn.init.constant_(lin.bias, -bias)
elif multires > 0 and l == 0:
torch.nn.init.constant_(lin.bias, 0.0)
torch.nn.init.constant_(lin.weight[:, 3:], 0.0)
torch.nn.init.normal_(lin.weight[:, :3], 0.0, np.sqrt(2) / np.sqrt(out_dim))
elif multires > 0 and l in self.skip_in:
torch.nn.init.constant_(lin.bias, 0.0)
torch.nn.init.normal_(lin.weight, 0.0, np.sqrt(2) / np.sqrt(out_dim))
torch.nn.init.constant_(lin.weight[:, -(dims[0] - 3):], 0.0) # ? why dims[0] - 3
else:
torch.nn.init.constant_(lin.bias, 0.0)
torch.nn.init.normal_(lin.weight, 0.0, np.sqrt(2) / np.sqrt(out_dim))
if weight_norm:
lin = nn.utils.weight_norm(lin)
setattr(self, "lin" + str(l), lin)
if activation == 'softplus':
self.activation = nn.Softplus(beta=100)
else:
assert activation == 'relu'
self.activation = nn.ReLU()
def forward(self, inputs):
inputs = inputs * self.scale
if self.embed_fn_fine is not None:
inputs = self.embed_fn_fine(inputs)
x = inputs
for l in range(0, self.num_layers - 1):
lin = getattr(self, "lin" + str(l))
if l in self.skip_in:
x = torch.cat([x, inputs], 1) / np.sqrt(2)
x = lin(x)
if l < self.num_layers - 2:
x = self.activation(x)
return torch.cat([x[:, :1] / self.scale, x[:, 1:]], dim=-1)
def sdf(self, x):
return self.forward(x)[:, :1]
def sdf_hidden_appearance(self, x):
return self.forward(x)
def gradient(self, x):
x.requires_grad_(True)
y = self.sdf(x)
d_output = torch.ones_like(y, requires_grad=False, device=y.device)
gradients = torch.autograd.grad(
outputs=y,
inputs=x,
grad_outputs=d_output,
create_graph=True,
retain_graph=True,
only_inputs=True)[0]
return gradients.unsqueeze(1)
class VarianceNetwork(nn.Module):
def __init__(self, d_in, d_out, d_hidden, n_layers, skip_in=(4,), multires=0):
super(VarianceNetwork, self).__init__()
dims = [d_in] + [d_hidden for _ in range(n_layers)] + [d_out]
self.embed_fn_fine = None
if multires > 0:
embed_fn, input_ch = get_embedder(multires, normalize=False)
self.embed_fn_fine = embed_fn
dims[0] = input_ch
self.num_layers = len(dims)
self.skip_in = skip_in
for l in range(0, self.num_layers - 1):
if l + 1 in self.skip_in:
out_dim = dims[l + 1] - dims[0]
else:
out_dim = dims[l + 1]
lin = nn.Linear(dims[l], out_dim)
setattr(self, "lin" + str(l), lin)
self.relu = nn.ReLU()
self.softplus = nn.Softplus(beta=100)
def forward(self, inputs):
if self.embed_fn_fine is not None:
inputs = self.embed_fn_fine(inputs)
x = inputs
for l in range(0, self.num_layers - 1):
lin = getattr(self, "lin" + str(l))
if l in self.skip_in:
x = torch.cat([x, inputs], 1) / np.sqrt(2)
x = lin(x)
if l < self.num_layers - 2:
x = self.relu(x)
# return torch.exp(x)
return 1.0 / (self.softplus(x + 0.5) + 1e-3)
def coarse(self, inputs):
return self.forward(inputs)[:, :1]
def fine(self, inputs):
return self.forward(inputs)[:, 1:]
class FixVarianceNetwork(nn.Module):
def __init__(self, base):
super(FixVarianceNetwork, self).__init__()
self.base = base
self.iter_step = 0
def set_iter_step(self, iter_step):
self.iter_step = iter_step
def forward(self, x):
return torch.ones([len(x), 1]) * np.exp(-self.iter_step / self.base)
class SingleVarianceNetwork(nn.Module):
def __init__(self, init_val=1.0):
super(SingleVarianceNetwork, self).__init__()
self.register_parameter('variance', nn.Parameter(torch.tensor(init_val)))
def forward(self, x):
return torch.ones([len(x), 1]).to(x.device) * torch.exp(self.variance * 10.0)
class RenderingNetwork(nn.Module):
def __init__(
self,
d_feature,
mode,
d_in,
d_out,
d_hidden,
n_layers,
weight_norm=True,
multires_view=0,
squeeze_out=True,
d_conditional_colors=0
):
super().__init__()
self.mode = mode
self.squeeze_out = squeeze_out
dims = [d_in + d_feature] + [d_hidden for _ in range(n_layers)] + [d_out]
self.embedview_fn = None
if multires_view > 0:
embedview_fn, input_ch = get_embedder(multires_view)
self.embedview_fn = embedview_fn
dims[0] += (input_ch - 3)
self.num_layers = len(dims)
for l in range(0, self.num_layers - 1):
out_dim = dims[l + 1]
lin = nn.Linear(dims[l], out_dim)
if weight_norm:
lin = nn.utils.weight_norm(lin)
setattr(self, "lin" + str(l), lin)
self.relu = nn.ReLU()
def forward(self, points, normals, view_dirs, feature_vectors):
if self.embedview_fn is not None:
view_dirs = self.embedview_fn(view_dirs)
rendering_input = None
if self.mode == 'idr':
rendering_input = torch.cat([points, view_dirs, normals, feature_vectors], dim=-1)
elif self.mode == 'no_view_dir':
rendering_input = torch.cat([points, normals, feature_vectors], dim=-1)
elif self.mode == 'no_normal':
rendering_input = torch.cat([points, view_dirs, feature_vectors], dim=-1)
elif self.mode == 'no_points':
rendering_input = torch.cat([view_dirs, normals, feature_vectors], dim=-1)
elif self.mode == 'no_points_no_view_dir':
rendering_input = torch.cat([normals, feature_vectors], dim=-1)
x = rendering_input
for l in range(0, self.num_layers - 1):
lin = getattr(self, "lin" + str(l))
x = lin(x)
if l < self.num_layers - 2:
x = self.relu(x)
if self.squeeze_out:
x = torch.sigmoid(x)
return x
# Code from nerf-pytorch
class NeRF(nn.Module):
def __init__(self, D=8, W=256, d_in=3, d_in_view=3, multires=0, multires_view=0, output_ch=4, skips=[4],
use_viewdirs=False):
"""
"""
super(NeRF, self).__init__()
self.D = D
self.W = W
self.d_in = d_in
self.d_in_view = d_in_view
self.input_ch = 3
self.input_ch_view = 3
self.embed_fn = None
self.embed_fn_view = None
if multires > 0:
embed_fn, input_ch = get_embedder(multires, input_dims=d_in, normalize=False)
self.embed_fn = embed_fn
self.input_ch = input_ch
if multires_view > 0:
embed_fn_view, input_ch_view = get_embedder(multires_view, input_dims=d_in_view, normalize=False)
self.embed_fn_view = embed_fn_view
self.input_ch_view = input_ch_view
self.skips = skips
self.use_viewdirs = use_viewdirs
self.pts_linears = nn.ModuleList(
[nn.Linear(self.input_ch, W)] + [nn.Linear(W, W) if i not in self.skips else nn.Linear(W + self.input_ch, W)
for i in
range(D - 1)])
### Implementation according to the official code release (https://github.com/bmild/nerf/blob/master/run_nerf_helpers.py#L104-L105)
self.views_linears = nn.ModuleList([nn.Linear(self.input_ch_view + W, W // 2)])
### Implementation according to the paper
# self.views_linears = nn.ModuleList(
# [nn.Linear(input_ch_views + W, W//2)] + [nn.Linear(W//2, W//2) for i in range(D//2)])
if use_viewdirs:
self.feature_linear = nn.Linear(W, W)
self.alpha_linear = nn.Linear(W, 1)
self.rgb_linear = nn.Linear(W // 2, 3)
else:
self.output_linear = nn.Linear(W, output_ch)
def forward(self, input_pts, input_views):
if self.embed_fn is not None:
input_pts = self.embed_fn(input_pts)
if self.embed_fn_view is not None:
input_views = self.embed_fn_view(input_views)
h = input_pts
for i, l in enumerate(self.pts_linears):
h = self.pts_linears[i](h)
h = F.relu(h)
if i in self.skips:
h = torch.cat([input_pts, h], -1)
if self.use_viewdirs:
alpha = self.alpha_linear(h)
feature = self.feature_linear(h)
h = torch.cat([feature, input_views], -1)
for i, l in enumerate(self.views_linears):
h = self.views_linears[i](h)
h = F.relu(h)
rgb = self.rgb_linear(h)
return alpha + 1.0, rgb
else:
assert False
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