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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 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) | |