| import math |
| import random |
| import functools |
| import operator |
|
|
| import torch |
| from torch import nn |
| from torch.nn import functional as F |
| from torch.autograd import Function |
|
|
| from op import conv2d_gradfix |
| if torch.cuda.is_available(): |
| from op.fused_act import FusedLeakyReLU, fused_leaky_relu |
| from op.upfirdn2d import upfirdn2d |
| else: |
| from op.fused_act_cpu import FusedLeakyReLU, fused_leaky_relu |
| from op.upfirdn2d_cpu import upfirdn2d |
|
|
|
|
| class PixelNorm(nn.Module): |
| def __init__(self): |
| super().__init__() |
|
|
| def forward(self, input): |
| return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8) |
|
|
|
|
| def make_kernel(k): |
| k = torch.tensor(k, dtype=torch.float32) |
|
|
| if k.ndim == 1: |
| k = k[None, :] * k[:, None] |
|
|
| k /= k.sum() |
|
|
| return k |
|
|
|
|
| class Upsample(nn.Module): |
| def __init__(self, kernel, factor=2): |
| super().__init__() |
|
|
| self.factor = factor |
| kernel = make_kernel(kernel) * (factor ** 2) |
| self.register_buffer("kernel", kernel) |
|
|
| p = kernel.shape[0] - factor |
|
|
| pad0 = (p + 1) // 2 + factor - 1 |
| pad1 = p // 2 |
|
|
| self.pad = (pad0, pad1) |
|
|
| def forward(self, input): |
| out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad) |
|
|
| return out |
|
|
|
|
| class Downsample(nn.Module): |
| def __init__(self, kernel, factor=2): |
| super().__init__() |
|
|
| self.factor = factor |
| kernel = make_kernel(kernel) |
| self.register_buffer("kernel", kernel) |
|
|
| p = kernel.shape[0] - factor |
|
|
| pad0 = (p + 1) // 2 |
| pad1 = p // 2 |
|
|
| self.pad = (pad0, pad1) |
|
|
| def forward(self, input): |
| out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad) |
|
|
| return out |
|
|
|
|
| class Blur(nn.Module): |
| def __init__(self, kernel, pad, upsample_factor=1): |
| super().__init__() |
|
|
| kernel = make_kernel(kernel) |
|
|
| if upsample_factor > 1: |
| kernel = kernel * (upsample_factor ** 2) |
|
|
| self.register_buffer("kernel", kernel) |
|
|
| self.pad = pad |
|
|
| def forward(self, input): |
| out = upfirdn2d(input, self.kernel, pad=self.pad) |
|
|
| return out |
|
|
|
|
| class EqualConv2d(nn.Module): |
| def __init__( |
| self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True |
| ): |
| super().__init__() |
|
|
| self.weight = nn.Parameter( |
| torch.randn(out_channel, in_channel, kernel_size, kernel_size) |
| ) |
| self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2) |
|
|
| self.stride = stride |
| self.padding = padding |
|
|
| if bias: |
| self.bias = nn.Parameter(torch.zeros(out_channel)) |
|
|
| else: |
| self.bias = None |
|
|
| def forward(self, input): |
| out = conv2d_gradfix.conv2d( |
| input, |
| self.weight * self.scale, |
| bias=self.bias, |
| stride=self.stride, |
| padding=self.padding, |
| ) |
|
|
| return out |
|
|
| def __repr__(self): |
| return ( |
| f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]}," |
| f" {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})" |
| ) |
|
|
|
|
| class EqualLinear(nn.Module): |
| def __init__( |
| self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None |
| ): |
| super().__init__() |
|
|
| self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul)) |
|
|
| if bias: |
| self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init)) |
|
|
| else: |
| self.bias = None |
|
|
| self.activation = activation |
|
|
| self.scale = (1 / math.sqrt(in_dim)) * lr_mul |
| self.lr_mul = lr_mul |
|
|
| def forward(self, input): |
| if self.activation: |
| out = F.linear(input, self.weight * self.scale) |
| out = fused_leaky_relu(out, self.bias * self.lr_mul) |
|
|
| else: |
| out = F.linear( |
| input, self.weight * self.scale, bias=self.bias * self.lr_mul |
| ) |
|
|
| return out |
|
|
| def __repr__(self): |
| return ( |
| f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})" |
| ) |
|
|
|
|
| class ModulatedConv2d(nn.Module): |
| def __init__( |
| self, |
| in_channel, |
| out_channel, |
| kernel_size, |
| style_dim, |
| demodulate=True, |
| upsample=False, |
| downsample=False, |
| blur_kernel=[1, 3, 3, 1], |
| fused=True, |
| ): |
| super().__init__() |
|
|
| self.eps = 1e-8 |
| self.kernel_size = kernel_size |
| self.in_channel = in_channel |
| self.out_channel = out_channel |
| self.upsample = upsample |
| self.downsample = downsample |
|
|
| if upsample: |
| factor = 2 |
| p = (len(blur_kernel) - factor) - (kernel_size - 1) |
| pad0 = (p + 1) // 2 + factor - 1 |
| pad1 = p // 2 + 1 |
|
|
| self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor) |
|
|
| if downsample: |
| factor = 2 |
| p = (len(blur_kernel) - factor) + (kernel_size - 1) |
| pad0 = (p + 1) // 2 |
| pad1 = p // 2 |
|
|
| self.blur = Blur(blur_kernel, pad=(pad0, pad1)) |
|
|
| fan_in = in_channel * kernel_size ** 2 |
| self.scale = 1 / math.sqrt(fan_in) |
| self.padding = kernel_size // 2 |
|
|
| self.weight = nn.Parameter( |
| torch.randn(1, out_channel, in_channel, kernel_size, kernel_size) |
| ) |
|
|
| self.modulation = EqualLinear(style_dim, in_channel, bias_init=1) |
|
|
| self.demodulate = demodulate |
| self.fused = fused |
|
|
| def __repr__(self): |
| return ( |
| f"{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, " |
| f"upsample={self.upsample}, downsample={self.downsample})" |
| ) |
|
|
| def forward(self, input, style): |
| batch, in_channel, height, width = input.shape |
|
|
| if not self.fused: |
| weight = self.scale * self.weight.squeeze(0) |
| style = self.modulation(style) |
|
|
| if self.demodulate: |
| w = weight.unsqueeze(0) * style.view(batch, 1, in_channel, 1, 1) |
| dcoefs = (w.square().sum((2, 3, 4)) + 1e-8).rsqrt() |
|
|
| input = input * style.reshape(batch, in_channel, 1, 1) |
|
|
| if self.upsample: |
| weight = weight.transpose(0, 1) |
| out = conv2d_gradfix.conv_transpose2d( |
| input, weight, padding=0, stride=2 |
| ) |
| out = self.blur(out) |
|
|
| elif self.downsample: |
| input = self.blur(input) |
| out = conv2d_gradfix.conv2d(input, weight, padding=0, stride=2) |
|
|
| else: |
| out = conv2d_gradfix.conv2d(input, weight, padding=self.padding) |
|
|
| if self.demodulate: |
| out = out * dcoefs.view(batch, -1, 1, 1) |
|
|
| return out |
|
|
| style = self.modulation(style).view(batch, 1, in_channel, 1, 1) |
| weight = self.scale * self.weight * style |
|
|
| if self.demodulate: |
| demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8) |
| weight = weight * demod.view(batch, self.out_channel, 1, 1, 1) |
|
|
| weight = weight.view( |
| batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size |
| ) |
|
|
| if self.upsample: |
| input = input.view(1, batch * in_channel, height, width) |
| weight = weight.view( |
| batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size |
| ) |
| weight = weight.transpose(1, 2).reshape( |
| batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size |
| ) |
| out = conv2d_gradfix.conv_transpose2d( |
| input, weight, padding=0, stride=2, groups=batch |
| ) |
| _, _, height, width = out.shape |
| out = out.view(batch, self.out_channel, height, width) |
| out = self.blur(out) |
|
|
| elif self.downsample: |
| input = self.blur(input) |
| _, _, height, width = input.shape |
| input = input.view(1, batch * in_channel, height, width) |
| out = conv2d_gradfix.conv2d( |
| input, weight, padding=0, stride=2, groups=batch |
| ) |
| _, _, height, width = out.shape |
| out = out.view(batch, self.out_channel, height, width) |
|
|
| else: |
| input = input.view(1, batch * in_channel, height, width) |
| out = conv2d_gradfix.conv2d( |
| input, weight, padding=self.padding, groups=batch |
| ) |
| _, _, height, width = out.shape |
| out = out.view(batch, self.out_channel, height, width) |
|
|
| return out |
|
|
|
|
| class NoiseInjection(nn.Module): |
| def __init__(self): |
| super().__init__() |
|
|
| self.weight = nn.Parameter(torch.zeros(1)) |
|
|
| def forward(self, image, noise=None): |
| if noise is None: |
| batch, _, height, width = image.shape |
| noise = image.new_empty(batch, 1, height, width).normal_() |
|
|
| return image + self.weight * noise |
|
|
|
|
| class ConstantInput(nn.Module): |
| def __init__(self, channel, size=4): |
| super().__init__() |
|
|
| self.input = nn.Parameter(torch.randn(1, channel, size, size)) |
|
|
| def forward(self, input): |
| batch = input.shape[0] |
| out = self.input.repeat(batch, 1, 1, 1) |
|
|
| return out |
|
|
|
|
| class StyledConv(nn.Module): |
| def __init__( |
| self, |
| in_channel, |
| out_channel, |
| kernel_size, |
| style_dim, |
| upsample=False, |
| blur_kernel=[1, 3, 3, 1], |
| demodulate=True, |
| ): |
| super().__init__() |
|
|
| self.conv = ModulatedConv2d( |
| in_channel, |
| out_channel, |
| kernel_size, |
| style_dim, |
| upsample=upsample, |
| blur_kernel=blur_kernel, |
| demodulate=demodulate, |
| ) |
|
|
| self.noise = NoiseInjection() |
| |
| |
| self.activate = FusedLeakyReLU(out_channel) |
|
|
| def forward(self, input, style, noise=None): |
| out = self.conv(input, style) |
| out = self.noise(out, noise=noise) |
| |
| out = self.activate(out) |
|
|
| return out |
|
|
|
|
| class ToRGB(nn.Module): |
| def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]): |
| super().__init__() |
|
|
| if upsample: |
| self.upsample = Upsample(blur_kernel) |
|
|
| self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False) |
| self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) |
|
|
| def forward(self, input, style, skip=None): |
| out = self.conv(input, style) |
| out = out + self.bias |
|
|
| if skip is not None: |
| skip = self.upsample(skip) |
|
|
| out = out + skip |
|
|
| return out |
|
|
|
|
| class Generator(nn.Module): |
| def __init__( |
| self, |
| size, |
| style_dim, |
| n_mlp, |
| channel_multiplier=2, |
| blur_kernel=[1, 3, 3, 1], |
| lr_mlp=0.01, |
| ): |
| super().__init__() |
|
|
| self.size = size |
|
|
| self.style_dim = style_dim |
|
|
| layers = [PixelNorm()] |
|
|
| for i in range(n_mlp): |
| layers.append( |
| EqualLinear( |
| style_dim, style_dim, lr_mul=lr_mlp, activation="fused_lrelu" |
| ) |
| ) |
|
|
| self.style = nn.Sequential(*layers) |
|
|
| self.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, |
| } |
|
|
| self.input = ConstantInput(self.channels[4]) |
| self.conv1 = StyledConv( |
| self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel |
| ) |
| self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False) |
|
|
| self.log_size = int(math.log(size, 2)) |
| self.num_layers = (self.log_size - 2) * 2 + 1 |
|
|
| self.convs = nn.ModuleList() |
| self.upsamples = nn.ModuleList() |
| self.to_rgbs = nn.ModuleList() |
| self.noises = nn.Module() |
|
|
| in_channel = self.channels[4] |
|
|
| for layer_idx in range(self.num_layers): |
| res = (layer_idx + 5) // 2 |
| shape = [1, 1, 2 ** res, 2 ** res] |
| self.noises.register_buffer(f"noise_{layer_idx}", torch.randn(*shape)) |
|
|
| for i in range(3, self.log_size + 1): |
| out_channel = self.channels[2 ** i] |
|
|
| self.convs.append( |
| StyledConv( |
| in_channel, |
| out_channel, |
| 3, |
| style_dim, |
| upsample=True, |
| blur_kernel=blur_kernel, |
| ) |
| ) |
|
|
| self.convs.append( |
| StyledConv( |
| out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel |
| ) |
| ) |
|
|
| self.to_rgbs.append(ToRGB(out_channel, style_dim)) |
|
|
| in_channel = out_channel |
|
|
| self.n_latent = self.log_size * 2 - 2 |
|
|
| def make_noise(self): |
| device = self.input.input.device |
|
|
| noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)] |
|
|
| for i in range(3, self.log_size + 1): |
| for _ in range(2): |
| noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device)) |
|
|
| return noises |
|
|
| @torch.no_grad() |
| def mean_latent(self, n_latent): |
| latent_in = torch.randn( |
| n_latent, self.style_dim, device=self.input.input.device |
| ) |
| latent = self.style(latent_in).mean(0, keepdim=True) |
|
|
| return latent |
|
|
| @torch.no_grad() |
| def get_latent(self, input): |
| return self.style(input) |
|
|
| def forward( |
| self, |
| styles, |
| return_latents=False, |
| inject_index=None, |
| truncation=1, |
| truncation_latent=None, |
| input_is_latent=False, |
| noise=None, |
| randomize_noise=True, |
| ): |
|
|
| if noise is None: |
| if randomize_noise: |
| noise = [None] * self.num_layers |
| else: |
| noise = [ |
| getattr(self.noises, f"noise_{i}") for i in range(self.num_layers) |
| ] |
|
|
| if not input_is_latent: |
| styles = [self.style(s) for s in styles] |
|
|
| if truncation < 1: |
| style_t = [] |
|
|
| for style in styles: |
| style_t.append( |
| truncation_latent + truncation * (style - truncation_latent) |
| ) |
|
|
| styles = style_t |
| latent = styles[0].unsqueeze(1).repeat(1, self.n_latent, 1) |
| else: |
| latent = styles |
|
|
| out = self.input(latent) |
| out = self.conv1(out, latent[:, 0], noise=noise[0]) |
|
|
| skip = self.to_rgb1(out, latent[:, 1]) |
|
|
| i = 1 |
| for conv1, conv2, noise1, noise2, to_rgb in zip( |
| self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs |
| ): |
| out = conv1(out, latent[:, i], noise=noise1) |
| out = conv2(out, latent[:, i + 1], noise=noise2) |
| skip = to_rgb(out, latent[:, i + 2], skip) |
|
|
| i += 2 |
|
|
| image = skip |
|
|
| return image |
|
|
|
|
| class ConvLayer(nn.Sequential): |
| def __init__( |
| self, |
| in_channel, |
| out_channel, |
| kernel_size, |
| downsample=False, |
| blur_kernel=[1, 3, 3, 1], |
| bias=True, |
| activate=True, |
| ): |
| layers = [] |
|
|
| if downsample: |
| factor = 2 |
| p = (len(blur_kernel) - factor) + (kernel_size - 1) |
| pad0 = (p + 1) // 2 |
| pad1 = p // 2 |
|
|
| layers.append(Blur(blur_kernel, pad=(pad0, pad1))) |
|
|
| stride = 2 |
| self.padding = 0 |
|
|
| else: |
| stride = 1 |
| self.padding = kernel_size // 2 |
|
|
| layers.append( |
| EqualConv2d( |
| in_channel, |
| out_channel, |
| kernel_size, |
| padding=self.padding, |
| stride=stride, |
| bias=bias and not activate, |
| ) |
| ) |
|
|
| if activate: |
| layers.append(FusedLeakyReLU(out_channel, bias=bias)) |
|
|
| super().__init__(*layers) |
|
|
|
|
| class ResBlock(nn.Module): |
| def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]): |
| super().__init__() |
|
|
| self.conv1 = ConvLayer(in_channel, in_channel, 3) |
| self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True) |
|
|
| self.skip = ConvLayer( |
| in_channel, out_channel, 1, downsample=True, activate=False, bias=False |
| ) |
|
|
| def forward(self, input): |
| out = self.conv1(input) |
| out = self.conv2(out) |
|
|
| skip = self.skip(input) |
| out = (out + skip) / math.sqrt(2) |
|
|
| return out |
|
|
|
|
| class Discriminator(nn.Module): |
| def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]): |
| 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.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], 1), |
| ) |
|
|
| def forward(self, input): |
| 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) |
| out = self.final_linear(out) |
|
|
| return out |
|
|
|
|
