import torch
import numpy as np
import math
from torch import nn
from model.stylegan.model import ConvLayer, EqualLinear, Generator, ResBlock
from model.dualstylegan import AdaptiveInstanceNorm, AdaResBlock, DualStyleGAN
import torch.nn.functional as F
# IC-GAN: stylegan discriminator
class ConditionalDiscriminator(nn.Module):
def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1], use_condition=False, style_num=None):
super().__init__()
channels = {
4: 512,
8: 512,
16: 512,
32: 512,
64: 256 * channel_multiplier,
128: 128 * channel_multiplier,
256: 64 * channel_multiplier,
512: 32 * channel_multiplier,
1024: 16 * channel_multiplier,
}
convs = [ConvLayer(3, channels[size], 1)]
log_size = int(math.log(size, 2))
in_channel = channels[size]
for i in range(log_size, 2, -1):
out_channel = channels[2 ** (i - 1)]
convs.append(ResBlock(in_channel, out_channel, blur_kernel))
in_channel = out_channel
self.convs = nn.Sequential(*convs)
self.stddev_group = 4
self.stddev_feat = 1
self.use_condition = use_condition
if self.use_condition:
self.condition_dim = 128
# map style degree to 64-dimensional vector
self.label_mapper = nn.Sequential(
nn.Linear(1, 64),
nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Linear(64, 64),
nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Linear(64, self.condition_dim//2),
)
# map style code index to 64-dimensional vector
self.style_mapper = nn.Embedding(style_num, self.condition_dim-self.condition_dim//2)
else:
self.condition_dim = 1
self.final_conv = ConvLayer(in_channel + 1, channels[4], 3)
self.final_linear = nn.Sequential(
EqualLinear(channels[4] * 4 * 4, channels[4], activation="fused_lrelu"),
EqualLinear(channels[4], self.condition_dim),
)
def forward(self, input, degree_label=None, style_ind=None):
out = self.convs(input)
batch, channel, height, width = out.shape
group = min(batch, self.stddev_group)
stddev = out.view(
group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
)
stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
stddev = stddev.repeat(group, 1, height, width)
out = torch.cat([out, stddev], 1)
out = self.final_conv(out)
out = out.view(batch, -1)
if self.use_condition:
h = self.final_linear(out)
condition = torch.cat((self.label_mapper(degree_label), self.style_mapper(style_ind)), dim=1)
out = (h * condition).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.condition_dim))
else:
out = self.final_linear(out)
return out
class VToonifyResBlock(nn.Module):
def __init__(self, fin):
super().__init__()
self.conv = nn.Conv2d(fin, fin, 3, 1, 1)
self.conv2 = nn.Conv2d(fin, fin, 3, 1, 1)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self, x):
out = self.lrelu(self.conv(x))
out = self.lrelu(self.conv2(out))
out = (out + x) / math.sqrt(2)
return out
class Fusion(nn.Module):
def __init__(self, in_channels, skip_channels, out_channels):
super().__init__()
# create conv layers
self.conv = nn.Conv2d(in_channels + skip_channels, out_channels, 3, 1, 1, bias=True)
self.norm = AdaptiveInstanceNorm(in_channels + skip_channels, 128)
self.conv2 = nn.Conv2d(in_channels + skip_channels, 1, 3, 1, 1, bias=True)
#'''
self.linear = nn.Sequential(
nn.Linear(1, 64),
nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Linear(64, 128),
nn.LeakyReLU(negative_slope=0.2, inplace=True)
)
def forward(self, f_G, f_E, d_s=1):
# label of style degree
label = self.linear(torch.zeros(f_G.size(0),1).to(f_G.device) + d_s)
out = torch.cat([f_G, abs(f_G-f_E)], dim=1)
m_E = (F.relu(self.conv2(self.norm(out, label)))).tanh()
f_out = self.conv(torch.cat([f_G, f_E * m_E], dim=1))
return f_out, m_E
class VToonify(nn.Module):
def __init__(self,
in_size=256,
out_size=1024,
img_channels=3,
style_channels=512,
num_mlps=8,
channel_multiplier=2,
num_res_layers=6,
backbone = 'dualstylegan',
):
super().__init__()
self.backbone = backbone
if self.backbone == 'dualstylegan':
# DualStyleGAN, with weights being fixed
self.generator = DualStyleGAN(out_size, style_channels, num_mlps, channel_multiplier)
else:
# StyleGANv2, with weights being fixed
self.generator = Generator(out_size, style_channels, num_mlps, channel_multiplier)
self.in_size = in_size
self.style_channels = style_channels
channels = self.generator.channels
# encoder
num_styles = int(np.log2(out_size)) * 2 - 2
encoder_res = [2**i for i in range(int(np.log2(in_size)), 4, -1)]
self.encoder = nn.ModuleList()
self.encoder.append(
nn.Sequential(
nn.Conv2d(img_channels+19, 32, 3, 1, 1, bias=True),
nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(32, channels[in_size], 3, 1, 1, bias=True),
nn.LeakyReLU(negative_slope=0.2, inplace=True)))
for res in encoder_res:
in_channels = channels[res]
if res > 32:
out_channels = channels[res // 2]
block = nn.Sequential(
nn.Conv2d(in_channels, out_channels, 3, 2, 1, bias=True),
nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=True),
nn.LeakyReLU(negative_slope=0.2, inplace=True))
self.encoder.append(block)
else:
layers = []
for _ in range(num_res_layers):
layers.append(VToonifyResBlock(in_channels))
self.encoder.append(nn.Sequential(*layers))
block = nn.Conv2d(in_channels, img_channels, 1, 1, 0, bias=True)
self.encoder.append(block)
# trainable fusion module
self.fusion_out = nn.ModuleList()
self.fusion_skip = nn.ModuleList()
for res in encoder_res[::-1]:
num_channels = channels[res]
if self.backbone == 'dualstylegan':
self.fusion_out.append(
Fusion(num_channels, num_channels, num_channels))
else:
self.fusion_out.append(
nn.Conv2d(num_channels * 2, num_channels, 3, 1, 1, bias=True))
self.fusion_skip.append(
nn.Conv2d(num_channels + 3, 3, 3, 1, 1, bias=True))
# Modified ModRes blocks in DualStyleGAN, with weights being fixed
if self.backbone == 'dualstylegan':
self.res = nn.ModuleList()
self.res.append(AdaResBlock(self.generator.channels[2 ** 2])) # for conv1, no use in this model
for i in range(3, 6):
out_channel = self.generator.channels[2 ** i]
self.res.append(AdaResBlock(out_channel, dilation=2**(5-i)))
self.res.append(AdaResBlock(out_channel, dilation=2**(5-i)))
def forward(self, x, style, d_s=None, return_mask=False, return_feat=False):
# map style to W+ space
if style is not None and style.ndim < 3:
if self.backbone == 'dualstylegan':
resstyles = self.generator.style(style).unsqueeze(1).repeat(1, self.generator.n_latent, 1)
adastyles = style.unsqueeze(1).repeat(1, self.generator.n_latent, 1)
elif style is not None:
nB, nL, nD = style.shape
if self.backbone == 'dualstylegan':
resstyles = self.generator.style(style.reshape(nB*nL, nD)).reshape(nB, nL, nD)
adastyles = style
if self.backbone == 'dualstylegan':
adastyles = adastyles.clone()
for i in range(7, self.generator.n_latent):
adastyles[:, i] = self.generator.res[i](adastyles[:, i])
# obtain multi-scale content features
feat = x
encoder_features = []
# downsampling conv parts of E
for block in self.encoder[:-2]:
feat = block(feat)
encoder_features.append(feat)
encoder_features = encoder_features[::-1]
# Resblocks in E
for ii, block in enumerate(self.encoder[-2]):
feat = block(feat)
# adjust Resblocks with ModRes blocks
if self.backbone == 'dualstylegan':
feat = self.res[ii+1](feat, resstyles[:, ii+1], d_s)
# the last-layer feature of E (inputs of backbone)
out = feat
skip = self.encoder[-1](feat)
if return_feat:
return out, skip
# 32x32 ---> higher res
_index = 1
m_Es = []
for conv1, conv2, to_rgb in zip(
self.stylegan().convs[6::2], self.stylegan().convs[7::2], self.stylegan().to_rgbs[3:]):
# pass the mid-layer features of E to the corresponding resolution layers of G
if 2 ** (5+((_index-1)//2)) <= self.in_size:
fusion_index = (_index - 1) // 2
f_E = encoder_features[fusion_index]
if self.backbone == 'dualstylegan':
out, m_E = self.fusion_out[fusion_index](out, f_E, d_s)
skip = self.fusion_skip[fusion_index](torch.cat([skip, f_E*m_E], dim=1))
m_Es += [m_E]
else:
out = self.fusion_out[fusion_index](torch.cat([out, f_E], dim=1))
skip = self.fusion_skip[fusion_index](torch.cat([skip, f_E], dim=1))
# remove the noise input
batch, _, height, width = out.shape
noise = x.new_empty(batch, 1, height * 2, width * 2).normal_().detach() * 0.0
out = conv1(out, adastyles[:, _index+6], noise=noise)
out = conv2(out, adastyles[:, _index+7], noise=noise)
skip = to_rgb(out, adastyles[:, _index+8], skip)
_index += 2
image = skip
if return_mask and self.backbone == 'dualstylegan':
return image, m_Es
return image
def stylegan(self):
if self.backbone == 'dualstylegan':
return self.generator.generator
else:
return self.generator
def zplus2wplus(self, zplus):
return self.stylegan().style(zplus.reshape(zplus.shape[0]*zplus.shape[1], zplus.shape[2])).reshape(zplus.shape)