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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) |