import math import torch import swapae.util as util import torch.nn.functional as F from swapae.models.networks import BaseNetwork from swapae.models.networks.stylegan2_layers import ConvLayer, ToRGB, EqualLinear, StyledConv class UpsamplingBlock(torch.nn.Module): def __init__(self, inch, outch, styledim, blur_kernel=[1, 3, 3, 1], use_noise=False): super().__init__() self.inch, self.outch, self.styledim = inch, outch, styledim self.conv1 = StyledConv(inch, outch, 3, styledim, upsample=True, blur_kernel=blur_kernel, use_noise=use_noise) self.conv2 = StyledConv(outch, outch, 3, styledim, upsample=False, use_noise=use_noise) def forward(self, x, style): return self.conv2(self.conv1(x, style), style) class ResolutionPreservingResnetBlock(torch.nn.Module): def __init__(self, opt, inch, outch, styledim): super().__init__() self.conv1 = StyledConv(inch, outch, 3, styledim, upsample=False) self.conv2 = StyledConv(outch, outch, 3, styledim, upsample=False) if inch != outch: self.skip = ConvLayer(inch, outch, 1, activate=False, bias=False) else: self.skip = torch.nn.Identity() def forward(self, x, style): skip = self.skip(x) res = self.conv2(self.conv1(x, style), style) return (skip + res) / math.sqrt(2) class UpsamplingResnetBlock(torch.nn.Module): def __init__(self, inch, outch, styledim, blur_kernel=[1, 3, 3, 1], use_noise=False): super().__init__() self.inch, self.outch, self.styledim = inch, outch, styledim self.conv1 = StyledConv(inch, outch, 3, styledim, upsample=True, blur_kernel=blur_kernel, use_noise=use_noise) self.conv2 = StyledConv(outch, outch, 3, styledim, upsample=False, use_noise=use_noise) if inch != outch: self.skip = ConvLayer(inch, outch, 1, activate=True, bias=True) else: self.skip = torch.nn.Identity() def forward(self, x, style): skip = F.interpolate(self.skip(x), scale_factor=2, mode='bilinear', align_corners=False) res = self.conv2(self.conv1(x, style), style) return (skip + res) / math.sqrt(2) class GeneratorModulation(torch.nn.Module): def __init__(self, styledim, outch): super().__init__() self.scale = EqualLinear(styledim, outch) self.bias = EqualLinear(styledim, outch) def forward(self, x, style): if style.ndimension() <= 2: return x * (1 * self.scale(style)[:, :, None, None]) + self.bias(style)[:, :, None, None] else: style = F.interpolate(style, size=(x.size(2), x.size(3)), mode='bilinear', align_corners=False) return x * (1 * self.scale(style)) + self.bias(style) class StyleGAN2ResnetGenerator(BaseNetwork): """ The Generator (decoder) architecture described in Figure 18 of Swapping Autoencoder (https://arxiv.org/abs/2007.00653). At high level, the architecture consists of regular and upsampling residual blocks to transform the structure code into an RGB image. The global code is applied at each layer as modulation. Here's more detailed architecture: 1. SpatialCodeModulation: First of all, modulate the structure code with the global code. 2. HeadResnetBlock: resnets at the resolution of the structure code, which also incorporates modulation from the global code. 3. UpsamplingResnetBlock: resnets that upsamples by factor of 2 until the resolution of the output RGB image, along with the global code modulation. 4. ToRGB: Final layer that transforms the output into 3 channels (RGB). Each components of the layers borrow heavily from StyleGAN2 code, implemented by Seonghyeon Kim. https://github.com/rosinality/stylegan2-pytorch """ @staticmethod def modify_commandline_options(parser, is_train): parser.add_argument("--netG_scale_capacity", default=1.0, type=float) parser.add_argument( "--netG_num_base_resnet_layers", default=2, type=int, help="The number of resnet layers before the upsampling layers." ) parser.add_argument("--netG_use_noise", type=util.str2bool, nargs='?', const=True, default=True) parser.add_argument("--netG_resnet_ch", type=int, default=256) return parser def __init__(self, opt): super().__init__(opt) num_upsamplings = opt.netE_num_downsampling_sp blur_kernel = [1, 3, 3, 1] if opt.use_antialias else [1] self.global_code_ch = opt.global_code_ch + opt.num_classes self.add_module( "SpatialCodeModulation", GeneratorModulation(self.global_code_ch, opt.spatial_code_ch)) in_channel = opt.spatial_code_ch for i in range(opt.netG_num_base_resnet_layers): # gradually increase the number of channels out_channel = (i + 1) / opt.netG_num_base_resnet_layers * self.nf(0) out_channel = max(opt.spatial_code_ch, round(out_channel)) layer_name = "HeadResnetBlock%d" % i new_layer = ResolutionPreservingResnetBlock( opt, in_channel, out_channel, self.global_code_ch) self.add_module(layer_name, new_layer) in_channel = out_channel for j in range(num_upsamplings): out_channel = self.nf(j + 1) layer_name = "UpsamplingResBlock%d" % (2 ** (4 + j)) new_layer = UpsamplingResnetBlock( in_channel, out_channel, self.global_code_ch, blur_kernel, opt.netG_use_noise) self.add_module(layer_name, new_layer) in_channel = out_channel last_layer = ToRGB(out_channel, self.global_code_ch, blur_kernel=blur_kernel) self.add_module("ToRGB", last_layer) def nf(self, num_up): ch = 128 * (2 ** (self.opt.netE_num_downsampling_sp - num_up)) ch = int(min(512, ch) * self.opt.netG_scale_capacity) return ch def forward(self, spatial_code, global_code): spatial_code = util.normalize(spatial_code) global_code = util.normalize(global_code) x = self.SpatialCodeModulation(spatial_code, global_code) for i in range(self.opt.netG_num_base_resnet_layers): resblock = getattr(self, "HeadResnetBlock%d" % i) x = resblock(x, global_code) for j in range(self.opt.netE_num_downsampling_sp): key_name = 2 ** (4 + j) upsampling_layer = getattr(self, "UpsamplingResBlock%d" % key_name) x = upsampling_layer(x, global_code) rgb = self.ToRGB(x, global_code, None) return rgb