255 lines
10 KiB
Python
255 lines
10 KiB
Python
import torch
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import torch.nn as nn
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from torchvision.models import vgg19
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from model.normalization import select_norm_layer
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from model.registry import MODEL
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from .base import ResidualBlock
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class VGG19StyleEncoder(nn.Module):
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def __init__(self, in_channels, base_channels=64, style_dim=512, padding_mode='reflect', norm_type="NONE",
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vgg19_layers=(0, 5, 10, 19)):
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super().__init__()
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self.vgg19_layers = vgg19_layers
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self.vgg19 = vgg19(pretrained=True).features[:vgg19_layers[-1] + 1]
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self.vgg19.requires_grad_(False)
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norm_layer = select_norm_layer(norm_type)
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self.conv0 = nn.Sequential(
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nn.Conv2d(in_channels, base_channels, kernel_size=7, stride=1, padding=3, padding_mode=padding_mode,
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bias=True),
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norm_layer(base_channels),
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nn.ReLU(True),
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)
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self.conv = nn.ModuleList([
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nn.Sequential(
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nn.Conv2d(base_channels * (2 ** i), base_channels * (2 ** i), kernel_size=4, stride=2, padding=1,
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padding_mode=padding_mode, bias=True),
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norm_layer(base_channels),
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nn.ReLU(True),
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) for i in range(1, 4)
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])
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self.pool = nn.AdaptiveAvgPool2d(1)
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self.conv1x1 = nn.Conv2d(base_channels * (2 ** 4), style_dim, kernel_size=1, stride=1, padding=0)
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def fixed_style_features(self, x):
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features = []
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for i in range(len(self.vgg19)):
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x = self.vgg19[i](x)
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if i in self.vgg19_layers:
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features.append(x)
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return features
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def forward(self, x):
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fsf = self.fixed_style_features(x)
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x = self.conv0(x)
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for i, l in enumerate(self.conv):
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x = l(torch.cat([x, fsf[i]], dim=1))
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x = self.pool(torch.cat([x, fsf[-1]], dim=1))
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x = self.conv1x1(x)
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return x.view(x.size(0), -1)
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class ContentEncoder(nn.Module):
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def __init__(self, in_channels, base_channels=64, num_blocks=8, padding_mode='reflect', norm_type="IN"):
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super().__init__()
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norm_layer = select_norm_layer(norm_type)
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self.start_conv = nn.Sequential(
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nn.Conv2d(in_channels, base_channels, kernel_size=7, stride=1, padding_mode=padding_mode, padding=3,
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bias=True),
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norm_layer(num_features=base_channels),
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nn.ReLU(inplace=True)
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)
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# down sampling
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submodules = []
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num_down_sampling = 2
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for i in range(num_down_sampling):
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multiple = 2 ** i
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submodules += [
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nn.Conv2d(in_channels=base_channels * multiple, out_channels=base_channels * multiple * 2,
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kernel_size=4, stride=2, padding=1, bias=True),
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norm_layer(num_features=base_channels * multiple * 2),
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nn.ReLU(inplace=True)
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]
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self.encoder = nn.Sequential(*submodules)
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res_block_channels = num_down_sampling ** 2 * base_channels
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self.resnet = nn.Sequential(
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*[ResidualBlock(res_block_channels, padding_mode, norm_type, use_bias=True) for _ in range(num_blocks)])
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def forward(self, x):
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x = self.start_conv(x)
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x = self.encoder(x)
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x = self.resnet(x)
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return x
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class Decoder(nn.Module):
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def __init__(self, out_channels, base_channels=64, num_blocks=4, num_down_sampling=2, padding_mode='reflect',
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norm_type="LN"):
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super(Decoder, self).__init__()
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norm_layer = select_norm_layer(norm_type)
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use_bias = norm_type == "IN"
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res_block_channels = (2 ** 2) * base_channels
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self.resnet = nn.Sequential(
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*[ResidualBlock(res_block_channels, padding_mode, norm_type, use_bias=True) for _ in range(num_blocks)])
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# up sampling
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submodules = []
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for i in range(num_down_sampling):
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multiple = 2 ** (num_down_sampling - i)
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submodules += [
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nn.Upsample(scale_factor=2),
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nn.Conv2d(base_channels * multiple, base_channels * multiple // 2, kernel_size=5, stride=1,
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padding=2, padding_mode=padding_mode, bias=use_bias),
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norm_layer(num_features=base_channels * multiple // 2),
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nn.ReLU(inplace=True),
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]
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self.decoder = nn.Sequential(*submodules)
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self.end_conv = nn.Sequential(
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nn.Conv2d(base_channels, out_channels, kernel_size=7, padding=3, padding_mode=padding_mode),
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nn.Tanh()
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)
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def forward(self, x):
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x = self.resnet(x)
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x = self.decoder(x)
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x = self.end_conv(x)
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return x
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class Fusion(nn.Module):
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def __init__(self, in_features, out_features, base_features, n_blocks, norm_type="NONE"):
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super().__init__()
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norm_layer = select_norm_layer(norm_type)
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self.start_fc = nn.Sequential(
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nn.Linear(in_features, base_features),
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norm_layer(base_features),
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nn.ReLU(True),
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)
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self.fcs = nn.Sequential(*[
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nn.Sequential(
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nn.Linear(base_features, base_features),
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norm_layer(base_features),
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nn.ReLU(True),
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) for _ in range(n_blocks - 2)
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])
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self.end_fc = nn.Sequential(
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nn.Linear(base_features, out_features),
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)
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def forward(self, x):
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x = self.start_fc(x)
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x = self.fcs(x)
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return self.end_fc(x)
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class StyleGenerator(nn.Module):
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def __init__(self, style_in_channels, style_dim=512, num_blocks=8, base_channels=64, padding_mode="reflect"):
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super().__init__()
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self.num_blocks = num_blocks
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self.style_encoder = VGG19StyleEncoder(
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style_in_channels, base_channels, style_dim=style_dim, padding_mode=padding_mode, norm_type="NONE")
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self.fc = nn.Sequential(
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nn.Linear(style_dim, style_dim),
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nn.ReLU(True),
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)
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res_block_channels = 2 ** 2 * base_channels
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self.fusion = Fusion(style_dim, num_blocks * 2 * res_block_channels * 2, base_features=256, n_blocks=3,
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norm_type="NONE")
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def forward(self, x):
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styles = self.fusion(self.fc(self.style_encoder(x)))
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return styles
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@MODEL.register_module("TAFG-Generator")
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class Generator(nn.Module):
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def __init__(self, style_in_channels, content_in_channels=3, out_channels=3, style_dim=512,
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num_adain_blocks=8, num_res_blocks=4,
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base_channels=64, padding_mode="reflect"):
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super(Generator, self).__init__()
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self.num_adain_blocks=num_adain_blocks
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self.style_encoders = nn.ModuleDict({
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"a": StyleGenerator(style_in_channels, style_dim=style_dim, num_blocks=num_adain_blocks,
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base_channels=base_channels, padding_mode=padding_mode),
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"b": StyleGenerator(style_in_channels, style_dim=style_dim, num_blocks=num_adain_blocks,
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base_channels=base_channels, padding_mode=padding_mode),
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})
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self.content_encoder = ContentEncoder(content_in_channels, base_channels, num_blocks=8,
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padding_mode=padding_mode, norm_type="IN")
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res_block_channels = 2 ** 2 * base_channels
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self.resnet = nn.ModuleDict({
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"a": nn.Sequential(*[
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ResidualBlock(res_block_channels, padding_mode, "IN", use_bias=True) for _ in range(num_res_blocks)
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]),
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"b": nn.Sequential(*[
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ResidualBlock(res_block_channels, padding_mode, "IN", use_bias=True) for _ in range(num_res_blocks)
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])
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})
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self.adain_resnet = nn.ModuleDict({
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"a": nn.ModuleList([
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ResidualBlock(res_block_channels, padding_mode, "AdaIN", use_bias=True) for _ in range(num_adain_blocks)
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]),
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"b": nn.ModuleList([
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ResidualBlock(res_block_channels, padding_mode, "AdaIN", use_bias=True) for _ in range(num_adain_blocks)
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])
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})
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self.decoders = nn.ModuleDict({
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"a": Decoder(out_channels, base_channels, norm_type="LN", num_blocks=0, padding_mode=padding_mode),
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"b": Decoder(out_channels, base_channels, norm_type="LN", num_blocks=0, padding_mode=padding_mode)
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})
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def forward(self, content_img, style_img, which_decoder: str = "a"):
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x = self.content_encoder(content_img)
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x = self.resnet[which_decoder](x)
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styles = self.style_encoders[which_decoder](style_img)
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styles = torch.chunk(styles, self.num_adain_blocks * 2, dim=1)
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for i, ar in enumerate(self.adain_resnet[which_decoder]):
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ar.norm1.set_style(styles[2 * i])
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ar.norm2.set_style(styles[2 * i + 1])
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x = ar(x)
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return self.decoders[which_decoder](x)
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@MODEL.register_module("TAFG-Discriminator")
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class Discriminator(nn.Module):
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def __init__(self, in_channels=3, base_channels=64, num_down_sampling=2, num_blocks=3, norm_type="IN",
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padding_mode="reflect"):
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super(Discriminator, self).__init__()
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norm_layer = select_norm_layer(norm_type)
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use_bias = norm_type == "IN"
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sequence = [nn.Sequential(
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nn.Conv2d(in_channels, base_channels, kernel_size=7, stride=1, padding_mode=padding_mode, padding=3,
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bias=use_bias),
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norm_layer(num_features=base_channels),
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nn.ReLU(inplace=True)
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)]
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# stacked intermediate layers,
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# gradually increasing the number of filters
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multiple_now = 1
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for n in range(1, num_down_sampling + 1):
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multiple_prev = multiple_now
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multiple_now = min(2 ** n, 4)
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sequence += [
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nn.Conv2d(base_channels * multiple_prev, base_channels * multiple_now, kernel_size=3,
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padding=1, stride=2, bias=use_bias),
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norm_layer(base_channels * multiple_now),
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nn.LeakyReLU(0.2, inplace=True)
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]
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for _ in range(num_blocks):
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sequence.append(ResidualBlock(base_channels * multiple_now, padding_mode, norm_type))
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self.model = nn.Sequential(*sequence)
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def forward(self, x):
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return self.model(x)
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