import torch import torch.nn as nn from .residual_generator import ResidualBlock from model.registry import MODEL from torchvision.models import vgg19 from model.normalization import select_norm_layer class VGG19StyleEncoder(nn.Module): def __init__(self, in_channels, base_channels=64, style_dim=512, padding_mode='reflect', norm_type="NONE", vgg19_layers=(0, 5, 10, 19)): super().__init__() self.vgg19_layers = vgg19_layers self.vgg19 = vgg19(pretrained=True).features[:vgg19_layers[-1] + 1] self.vgg19.requires_grad_(False) norm_layer = select_norm_layer(norm_type) self.conv0 = nn.Sequential( nn.Conv2d(in_channels, base_channels, kernel_size=7, stride=1, padding=3, padding_mode=padding_mode, bias=True), norm_layer(base_channels), nn.ReLU(True), ) self.conv = nn.ModuleList([ nn.Sequential( nn.Conv2d(base_channels * (2 ** i), base_channels * (2 ** i), kernel_size=4, stride=2, padding=1, padding_mode=padding_mode, bias=True), norm_layer(base_channels), nn.ReLU(True), ) for i in range(1, 4) ]) self.pool = nn.AdaptiveAvgPool2d(1) self.conv1x1 = nn.Conv2d(base_channels * (2 ** 4), style_dim, kernel_size=1, stride=1, padding=0) def fixed_style_features(self, x): features = [] for i in range(len(self.vgg19)): x = self.vgg19[i](x) if i in self.vgg19_layers: features.append(x) return features def forward(self, x): fsf = self.fixed_style_features(x) x = self.conv0(x) for i, l in enumerate(self.conv): x = l(torch.cat([x, fsf[i]], dim=1)) x = self.pool(torch.cat([x, fsf[-1]], dim=1)) x = self.conv1x1(x) return x.view(x.size(0), -1) class ContentEncoder(nn.Module): def __init__(self, in_channels, base_channels=64, num_blocks=8, padding_mode='reflect', norm_type="IN"): super().__init__() norm_layer = select_norm_layer(norm_type) self.start_conv = nn.Sequential( nn.Conv2d(in_channels, base_channels, kernel_size=7, stride=1, padding_mode=padding_mode, padding=3, bias=True), norm_layer(num_features=base_channels), nn.ReLU(inplace=True) ) # down sampling submodules = [] num_down_sampling = 2 for i in range(num_down_sampling): multiple = 2 ** i submodules += [ nn.Conv2d(in_channels=base_channels * multiple, out_channels=base_channels * multiple * 2, kernel_size=4, stride=2, padding=1, bias=True), norm_layer(num_features=base_channels * multiple * 2), nn.ReLU(inplace=True) ] self.encoder = nn.Sequential(*submodules) res_block_channels = num_down_sampling ** 2 * base_channels self.resnet = nn.Sequential( *[ResidualBlock(res_block_channels, padding_mode, norm_type, use_bias=True) for _ in range(num_blocks)]) def forward(self, x): x = self.start_conv(x) x = self.encoder(x) x = self.resnet(x) return x class Decoder(nn.Module): def __init__(self, out_channels, base_channels=64, num_down_sampling=2, padding_mode='reflect', norm_type="LN"): super(Decoder, self).__init__() norm_layer = select_norm_layer(norm_type) use_bias = norm_type == "IN" # up sampling submodules = [] for i in range(num_down_sampling): multiple = 2 ** (num_down_sampling - i) submodules += [ nn.Upsample(scale_factor=2), nn.Conv2d(base_channels * multiple, base_channels * multiple // 2, kernel_size=5, stride=1, padding=2, padding_mode=padding_mode, bias=use_bias), norm_layer(num_features=base_channels * multiple // 2), nn.ReLU(inplace=True), ] self.decoder = nn.Sequential(*submodules) self.end_conv = nn.Sequential( nn.Conv2d(base_channels, out_channels, kernel_size=7, padding=3, padding_mode=padding_mode), nn.Tanh() ) def forward(self, x): x = self.decoder(x) x = self.end_conv(x) return x class Fusion(nn.Module): def __init__(self, in_features, out_features, base_features, n_blocks, norm_type="NONE"): super().__init__() norm_layer = select_norm_layer(norm_type) self.start_fc = nn.Sequential( nn.Linear(in_features, base_features), norm_layer(base_features), nn.ReLU(True), ) self.fcs = nn.Sequential(*[ nn.Sequential( nn.Linear(base_features, base_features), norm_layer(base_features), nn.ReLU(True), ) for _ in range(n_blocks - 2) ]) self.end_fc = nn.Sequential( nn.Linear(base_features, out_features), ) def forward(self, x): x = self.start_fc(x) x = self.fcs(x) return self.end_fc(x) @MODEL.register_module("TAHG-Generator") class Generator(nn.Module): def __init__(self, style_in_channels, content_in_channels, out_channels, style_dim=512, num_blocks=8, base_channels=64, padding_mode="reflect"): super(Generator, self).__init__() self.num_blocks = num_blocks self.style_encoder = VGG19StyleEncoder(style_in_channels, base_channels, style_dim=style_dim, padding_mode=padding_mode, norm_type="NONE") self.content_encoder = ContentEncoder(content_in_channels, base_channels, num_blocks=num_blocks, padding_mode=padding_mode, norm_type="IN") res_block_channels = 2 ** 2 * base_channels self.adain_res = nn.ModuleList([ ResidualBlock(res_block_channels, padding_mode, "AdaIN", use_bias=True) for _ in range(num_blocks) ]) self.decoders = nn.ModuleDict({ "a": Decoder(out_channels, base_channels, norm_type="LN", padding_mode=padding_mode), "b": Decoder(out_channels, base_channels, norm_type="LN", padding_mode=padding_mode) }) self.fc = nn.Sequential( nn.Linear(style_dim, style_dim), nn.ReLU(True), ) self.fusion = Fusion(style_dim, num_blocks * 2 * res_block_channels * 2, base_features=256, n_blocks=3, norm_type="NONE") def forward(self, content_img, style_img, which_decoder: str = "a"): x = self.content_encoder(content_img) styles = self.fusion(self.fc(self.style_encoder(style_img))) styles = torch.chunk(styles, self.num_blocks * 2, dim=1) for i, ar in enumerate(self.adain_res): ar.norm1.set_style(styles[2 * i]) ar.norm2.set_style(styles[2 * i + 1]) x = ar(x) return self.decoders[which_decoder](x)