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import torch
import torch.nn as nn
import numpy as np
class SRTConvBlock(nn.Module):
def __init__(self, idim, hdim=None, odim=None):
super().__init__()
if hdim is None:
hdim = idim
if odim is None:
odim = 2 * hdim
conv_kwargs = {'bias': False, 'kernel_size': 3, 'padding': 1}
self.layers = nn.Sequential(
nn.Conv2d(idim, hdim, stride=1, **conv_kwargs),
nn.ReLU(),
nn.Conv2d(hdim, odim, stride=2, **conv_kwargs),
nn.ReLU())
def forward(self, x):
return self.layers(x)
class ConditionalHashGrid(nn.Module):
def __init__(self, num_conv_blocks = 6):
super(ConditionalHashGrid, self).__init__()
self.sconv_head = nn.Conv2d(11, 8, kernel_size=3, stride=2, padding=1)
self.hconv_head = nn.Conv2d(1, 8, kernel_size=3, stride=2, padding=1)
conv_blocks = []
cur_hdim = 16
for i in range(1, num_conv_blocks):
conv_blocks.append(SRTConvBlock(idim=cur_hdim, odim=None))
cur_hdim *= 2
self.conv_blocks = nn.Sequential(*conv_blocks)
self.fc1 = nn.Linear(cur_hdim, 16)
self.fc2 = nn.Linear(16, 2)
self.act = nn.LeakyReLU(0.2)
def forward(self, height_map, semantic_map):
h = self.act(self.hconv_head(height_map))
s = self.act(self.sconv_head(semantic_map))
joint = torch.cat([h, s], dim=1)
# interm = []
# interm.append(joint.permute(0, 2, 3, 1).reshape(-1, 8))
for layer in self.conv_blocks:
out = self.act(layer(joint))
# interm.append(out.permute(0, 2, 3, 1).reshape(-1, 8))
joint = out
out = out.permute(0, 2, 3, 1)
out = torch.mean(out.reshape(out.shape[0], -1, out.shape[-1]), dim=1)
cond = self.act(self.fc1(out))
cond = torch.tanh(self.fc2(cond))
return cond
class LightningMLP(nn.Module):
r""" MLP with affine modulation."""
def __init__(self, in_channels, style_dim, viewdir_dim, mask_dim=680,
out_channels_s=1, out_channels_c=3, hidden_channels=256,
use_seg=True):
super(LightningMLP, self).__init__()
self.use_seg = use_seg
if self.use_seg:
self.fc_m_a = nn.Linear(mask_dim, hidden_channels, bias=False)
self.fc_viewdir = None
if viewdir_dim > 0:
self.fc_viewdir = nn.Linear(viewdir_dim, hidden_channels, bias=False)
self.fc_1 = nn.Linear(in_channels, hidden_channels)
self.fc_2 = ModLinear(hidden_channels, hidden_channels, style_dim, bias=False, mod_bias=True, output_mode=True)
self.fc_3 = ModLinear(hidden_channels, hidden_channels, style_dim, bias=False, mod_bias=True, output_mode=True)
self.fc_4 = ModLinear(hidden_channels, hidden_channels, style_dim, bias=False, mod_bias=True, output_mode=True)
self.fc_sigma = nn.Linear(hidden_channels, out_channels_s)
if viewdir_dim > 0:
self.fc_5 = nn.Linear(hidden_channels, hidden_channels, bias=False)
self.mod_5 = AffineMod(hidden_channels, style_dim, mod_bias=True)
else:
self.fc_5 = ModLinear(hidden_channels, hidden_channels, style_dim,
bias=False, mod_bias=True, output_mode=True)
self.fc_6 = ModLinear(hidden_channels, hidden_channels, style_dim, bias=False, mod_bias=True, output_mode=True)
self.fc_out_c = nn.Linear(hidden_channels, out_channels_c)
self.act = nn.LeakyReLU(negative_slope=0.2)
def forward(self, x, raydir, z, m):
r""" Forward network
Args:
x (N x H x W x M x in_channels tensor): Projected features.
raydir (N x H x W x 1 x viewdir_dim tensor): Ray directions.
z (N x style_dim tensor): Style codes.
m (N x H x W x M x mask_dim tensor): One-hot segmentation maps.
"""
b, h, w, n, _ = x.size()
z = z[:, None, None, None, :]
# print('style z', z.shape)
# print('global enc:', global_enc.shape)
f = self.fc_1(x)
if self.use_seg:
f = f + self.fc_m_a(m)
# Common MLP
f = self.act(f)
f = self.act(self.fc_2(f, z))
f = self.act(self.fc_3(f, z))
f = self.act(self.fc_4(f, z))
# Sigma MLP
sigma = self.fc_sigma(f)
# Color MLP
if self.fc_viewdir is not None:
f = self.fc_5(f)
f = f + self.fc_viewdir(raydir)
f = self.act(self.mod_5(f, z))
else:
f = self.act(self.fc_5(f, z))
f = self.act(self.fc_6(f, z))
c = self.fc_out_c(f)
return sigma, c
class AffineMod(nn.Module):
r"""Learning affine modulation of activation.
Args:
in_features (int): Number of input features.
style_features (int): Number of style features.
mod_bias (bool): Whether to modulate bias.
"""
def __init__(self,
in_features,
style_features,
mod_bias=True
):
super().__init__()
self.weight_alpha = nn.Parameter(torch.randn([in_features, style_features]) / np.sqrt(style_features))
self.bias_alpha = nn.Parameter(torch.full([in_features], 1, dtype=torch.float)) # init to 1
self.weight_beta = None
self.bias_beta = None
self.mod_bias = mod_bias
if mod_bias:
self.weight_beta = nn.Parameter(torch.randn([in_features, style_features]) / np.sqrt(style_features))
self.bias_beta = nn.Parameter(torch.full([in_features], 0, dtype=torch.float))
@staticmethod
def _linear_f(x, w, b):
w = w.to(x.dtype)
x_shape = x.shape
x = x.reshape(-1, x_shape[-1])
if b is not None:
b = b.to(x.dtype)
x = torch.addmm(b.unsqueeze(0), x, w.t())
else:
x = x.matmul(w.t())
x = x.reshape(*x_shape[:-1], -1)
return x
# x: B, ... , Cin
# z: B, 1, 1, , Cz
def forward(self, x, z):
x_shape = x.shape
z_shape = z.shape
x = x.reshape(x_shape[0], -1, x_shape[-1])
z = z.reshape(z_shape[0], 1, z_shape[-1])
alpha = self._linear_f(z, self.weight_alpha, self.bias_alpha) # [B, ..., I]
x = x * alpha
if self.mod_bias:
beta = self._linear_f(z, self.weight_beta, self.bias_beta) # [B, ..., I]
x = x + beta
x = x.reshape(*x_shape[:-1], x.shape[-1])
return x
class ModLinear(nn.Module):
r"""Linear layer with affine modulation (Based on StyleGAN2 mod demod).
Equivalent to affine modulation following linear, but faster when the same modulation parameters are shared across
multiple inputs.
Args:
in_features (int): Number of input features.
out_features (int): Number of output features.
style_features (int): Number of style features.
bias (bool): Apply additive bias before the activation function?
mod_bias (bool): Whether to modulate bias.
output_mode (bool): If True, modulate output instead of input.
weight_gain (float): Initialization gain
"""
def __init__(self,
in_features,
out_features,
style_features,
bias=True,
mod_bias=True,
output_mode=False,
weight_gain=1,
bias_init=0
):
super().__init__()
weight_gain = weight_gain / np.sqrt(in_features)
self.weight = nn.Parameter(torch.randn([out_features, in_features]) * weight_gain)
self.bias = nn.Parameter(torch.full([out_features], np.float32(bias_init))) if bias else None
self.weight_alpha = nn.Parameter(torch.randn([in_features, style_features]) / np.sqrt(style_features))
self.bias_alpha = nn.Parameter(torch.full([in_features], 1, dtype=torch.float)) # init to 1
self.weight_beta = None
self.bias_beta = None
self.mod_bias = mod_bias
self.output_mode = output_mode
if mod_bias:
if output_mode:
mod_bias_dims = out_features
else:
mod_bias_dims = in_features
self.weight_beta = nn.Parameter(torch.randn([mod_bias_dims, style_features]) / np.sqrt(style_features))
self.bias_beta = nn.Parameter(torch.full([mod_bias_dims], 0, dtype=torch.float))
@staticmethod
def _linear_f(x, w, b):
w = w.to(x.dtype)
x_shape = x.shape
x = x.reshape(-1, x_shape[-1])
if b is not None:
b = b.to(x.dtype)
x = torch.addmm(b.unsqueeze(0), x, w.t())
else:
x = x.matmul(w.t())
x = x.reshape(*x_shape[:-1], -1)
return x
# x: B, ... , Cin
# z: B, 1, 1, , Cz
def forward(self, x, z):
x_shape = x.shape
z_shape = z.shape
x = x.reshape(x_shape[0], -1, x_shape[-1])
z = z.reshape(z_shape[0], 1, z_shape[-1])
alpha = self._linear_f(z, self.weight_alpha, self.bias_alpha) # [B, ..., I]
w = self.weight.to(x.dtype) # [O I]
w = w.unsqueeze(0) * alpha # [1 O I] * [B 1 I] = [B O I]
if self.mod_bias:
beta = self._linear_f(z, self.weight_beta, self.bias_beta) # [B, ..., I]
if not self.output_mode:
x = x + beta
b = self.bias
if b is not None:
b = b.to(x.dtype)[None, None, :]
if self.mod_bias and self.output_mode:
if b is None:
b = beta
else:
b = b + beta
# [B ? I] @ [B I O] = [B ? O]
if b is not None:
x = torch.baddbmm(b, x, w.transpose(1, 2))
else:
x = x.bmm(w.transpose(1, 2))
x = x.reshape(*x_shape[:-1], x.shape[-1])
return x
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