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| import torchvision | |
| 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 FusedLeakyReLU, fused_leaky_relu, upfirdn2d | |
| n_latent = 11 | |
| channels = { | |
| 4: 512, | |
| 8: 512, | |
| 16: 512, | |
| 32: 512, | |
| 64: 256, | |
| 128: 128, | |
| 256: 64, | |
| 512: 32, | |
| 1024: 16, | |
| } | |
| class LambdaLR(): | |
| def __init__(self, n_epochs, offset, decay_start_epoch): | |
| assert ((n_epochs - decay_start_epoch) > 0), "Decay must start before the training session ends!" | |
| self.n_epochs = n_epochs | |
| self.offset = offset | |
| self.decay_start_epoch = decay_start_epoch | |
| def step(self, epoch): | |
| return 1.0 - max(0, epoch + self.offset - self.decay_start_epoch)/(self.n_epochs - self.decay_start_epoch) | |
| 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 = F.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): | |
| bias = self.bias*self.lr_mul if self.bias is not None else None | |
| if self.activation: | |
| out = F.linear(input, self.weight * self.scale) | |
| out = fused_leaky_relu(out, bias) | |
| else: | |
| out = F.linear( | |
| input, self.weight * self.scale, bias=bias | |
| ) | |
| return out | |
| def __repr__(self): | |
| return ( | |
| f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})' | |
| ) | |
| class ScaledLeakyReLU(nn.Module): | |
| def __init__(self, negative_slope=0.2): | |
| super().__init__() | |
| self.negative_slope = negative_slope | |
| def forward(self, input): | |
| out = F.leaky_relu(input, negative_slope=self.negative_slope) | |
| return out * math.sqrt(2) | |
| class ModulatedConv2d(nn.Module): | |
| def __init__( | |
| self, | |
| in_channel, | |
| out_channel, | |
| kernel_size, | |
| style_dim, | |
| use_style=True, | |
| demodulate=True, | |
| upsample=False, | |
| downsample=False, | |
| blur_kernel=[1, 3, 3, 1], | |
| ): | |
| 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 | |
| self.use_style = use_style | |
| 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) | |
| ) | |
| if use_style: | |
| self.modulation = EqualLinear(style_dim, in_channel, bias_init=1) | |
| else: | |
| self.modulation = nn.Parameter(torch.Tensor(1, 1, in_channel, 1, 1).fill_(1)) | |
| self.demodulate = demodulate | |
| 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 self.use_style: | |
| style = self.modulation(style).view(batch, 1, in_channel, 1, 1) | |
| weight = self.scale * self.weight * style | |
| else: | |
| weight = self.scale * self.weight.expand(batch,-1,-1,-1,-1) * self.modulation | |
| 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 = F.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 = F.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 = F.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, style_dim): | |
| super().__init__() | |
| self.input = nn.Parameter(torch.randn(1, style_dim)) | |
| def forward(self, input): | |
| batch = input.shape[0] | |
| out = self.input.repeat(batch, n_latent) | |
| return out | |
| class StyledConv(nn.Module): | |
| def __init__( | |
| self, | |
| in_channel, | |
| out_channel, | |
| kernel_size, | |
| style_dim, | |
| use_style=True, | |
| upsample=False, | |
| downsample=False, | |
| blur_kernel=[1, 3, 3, 1], | |
| demodulate=True, | |
| ): | |
| super().__init__() | |
| self.use_style = use_style | |
| self.conv = ModulatedConv2d( | |
| in_channel, | |
| out_channel, | |
| kernel_size, | |
| style_dim, | |
| use_style=use_style, | |
| upsample=upsample, | |
| downsample=downsample, | |
| blur_kernel=blur_kernel, | |
| demodulate=demodulate, | |
| ) | |
| #if use_style: | |
| # self.noise = NoiseInjection() | |
| #else: | |
| # self.noise = None | |
| # self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1)) | |
| # self.activate = ScaledLeakyReLU(0.2) | |
| self.activate = FusedLeakyReLU(out_channel) | |
| def forward(self, input, style=None, noise=None): | |
| out = self.conv(input, style) | |
| #if self.use_style: | |
| # out = self.noise(out, noise=noise) | |
| # out = out + self.bias | |
| out = self.activate(out) | |
| return out | |
| class StyledResBlock(nn.Module): | |
| def __init__(self, in_channel, style_dim, blur_kernel=[1, 3, 3, 1], demodulate=True): | |
| super().__init__() | |
| self.conv1 = StyledConv(in_channel, in_channel, 3, style_dim, upsample=False, blur_kernel=blur_kernel, demodulate=demodulate) | |
| self.conv2 = StyledConv(in_channel, in_channel, 3, style_dim, upsample=False, blur_kernel=blur_kernel, demodulate=demodulate) | |
| def forward(self, input, style): | |
| out = self.conv1(input, style) | |
| out = self.conv2(out, style) | |
| out = (out + input) / math.sqrt(2) | |
| 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, | |
| num_down, | |
| latent_dim, | |
| n_mlp, | |
| n_res, | |
| channel_multiplier=1, | |
| blur_kernel=[1, 3, 3, 1], | |
| lr_mlp=0.01, | |
| ): | |
| super().__init__() | |
| self.size = size | |
| style_dim = 512 | |
| mapping = [EqualLinear(latent_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu')] | |
| for i in range(n_mlp-1): | |
| mapping.append(EqualLinear(style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu')) | |
| self.mapping = nn.Sequential(*mapping) | |
| self.encoder = Encoder(size, latent_dim, num_down, n_res, channel_multiplier) | |
| self.log_size = int(math.log(size, 2)) #7 | |
| in_log_size = self.log_size - num_down #7-2 or 7-3 | |
| in_size = 2 ** in_log_size | |
| in_channel = channels[in_size] | |
| self.adain_bottleneck = nn.ModuleList() | |
| for i in range(n_res): | |
| self.adain_bottleneck.append(StyledResBlock(in_channel, style_dim)) | |
| self.conv1 = StyledConv(in_channel, in_channel, 3, style_dim, blur_kernel=blur_kernel) | |
| self.to_rgb1 = ToRGB(in_channel, style_dim, upsample=False) | |
| self.num_layers = (self.log_size - in_log_size) * 2 + 1 #7 | |
| self.convs = nn.ModuleList() | |
| self.upsamples = nn.ModuleList() | |
| self.to_rgbs = nn.ModuleList() | |
| #self.noises = nn.Module() | |
| #for layer_idx in range(self.num_layers): | |
| # res = (layer_idx + (in_log_size*2+1)) // 2 #2,3,3,5 ... -> 4,5,5,6 ... | |
| # shape = [1, 1, 2 ** res, 2 ** res] | |
| # self.noises.register_buffer(f'noise_{layer_idx}', torch.randn(*shape)) | |
| for i in range(in_log_size+1, self.log_size + 1): | |
| out_channel = 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 | |
| def style_encode(self, input): | |
| return self.encoder(input)[1] | |
| def encode(self, input): | |
| return self.encoder(input) | |
| def forward(self, input, z=None): | |
| content, style = self.encode(input) | |
| if z is None: | |
| out = self.decode(content, style) | |
| else: | |
| out = self.decode(content, z) | |
| return out, content, style | |
| def decode(self, input, styles, use_mapping=True): | |
| if use_mapping: | |
| styles = self.mapping(styles) | |
| #styles = styles.repeat(1, n_latent).view(styles.size(0), n_latent, -1) | |
| out = input | |
| i = 0 | |
| for conv in self.adain_bottleneck: | |
| out = conv(out, styles) | |
| i += 1 | |
| out = self.conv1(out, styles, noise=None) | |
| skip = self.to_rgb1(out, styles) | |
| i += 2 | |
| for conv1, conv2, to_rgb in zip( | |
| self.convs[::2], self.convs[1::2], self.to_rgbs | |
| ): | |
| out = conv1(out, styles, noise=None) | |
| out = conv2(out, styles, noise=None) | |
| skip = to_rgb(out, styles, skip) | |
| i += 3 | |
| 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: | |
| if bias: | |
| layers.append(FusedLeakyReLU(out_channel)) | |
| else: | |
| layers.append(ScaledLeakyReLU(0.2)) | |
| super().__init__(*layers) | |
| class InResBlock(nn.Module): | |
| def __init__(self, in_channel, blur_kernel=[1, 3, 3, 1]): | |
| super().__init__() | |
| self.conv1 = StyledConv(in_channel, in_channel, 3, None, blur_kernel=blur_kernel, demodulate=True, use_style=False) | |
| self.conv2 = StyledConv(in_channel, in_channel, 3, None, blur_kernel=blur_kernel, demodulate=True, use_style=False) | |
| def forward(self, input): | |
| out = self.conv1(input, None) | |
| out = self.conv2(out, None) | |
| out = (out + input) / math.sqrt(2) | |
| return out | |
| class ResBlock(nn.Module): | |
| def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1], downsample=True): | |
| super().__init__() | |
| self.conv1 = ConvLayer(in_channel, in_channel, 3) | |
| self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=downsample) | |
| if downsample or in_channel != out_channel: | |
| self.skip = ConvLayer( | |
| in_channel, out_channel, 1, downsample=downsample, activate=False, bias=False | |
| ) | |
| else: | |
| self.skip = None | |
| def forward(self, input): | |
| out = self.conv1(input) | |
| out = self.conv2(out) | |
| if self.skip is None: | |
| skip = input | |
| else: | |
| 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__() | |
| self.size = size | |
| l_branch = self.make_net_(32) | |
| l_branch += [ConvLayer(channels[32], 1, 1, activate=False)] | |
| self.l_branch = nn.Sequential(*l_branch) | |
| g_branch = self.make_net_(8) | |
| self.g_branch = nn.Sequential(*g_branch) | |
| self.g_adv = ConvLayer(channels[8], 1, 1, activate=False) | |
| self.g_std = nn.Sequential(ConvLayer(channels[8], channels[4], 3, downsample=True), | |
| nn.Flatten(), | |
| EqualLinear(channels[4] * 4 * 4, 128, activation='fused_lrelu'), | |
| ) | |
| self.g_final = EqualLinear(128, 1, activation=False) | |
| def make_net_(self, out_size): | |
| size = self.size | |
| convs = [ConvLayer(3, channels[size], 1)] | |
| log_size = int(math.log(size, 2)) | |
| out_log_size = int(math.log(out_size, 2)) | |
| in_channel = channels[size] | |
| for i in range(log_size, out_log_size, -1): | |
| out_channel = channels[2 ** (i - 1)] | |
| convs.append(ResBlock(in_channel, out_channel)) | |
| in_channel = out_channel | |
| return convs | |
| def forward(self, x): | |
| l_adv = self.l_branch(x) | |
| g_act = self.g_branch(x) | |
| g_adv = self.g_adv(g_act) | |
| output = self.g_std(g_act) | |
| g_stddev = torch.sqrt(output.var(0, keepdim=True, unbiased=False) + 1e-8).repeat(x.size(0),1) | |
| g_std = self.g_final(g_stddev) | |
| return [l_adv, g_adv, g_std] | |
| class Encoder(nn.Module): | |
| def __init__(self, size, latent_dim, num_down, n_res, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]): | |
| super().__init__() | |
| stem = [ConvLayer(3, channels[size], 1)] | |
| log_size = int(math.log(size, 2)) | |
| in_channel = channels[size] | |
| for i in range(log_size, log_size-num_down, -1): | |
| out_channel = channels[2 ** (i - 1)] | |
| stem.append(ResBlock(in_channel, out_channel, downsample=True)) | |
| in_channel = out_channel | |
| stem += [ResBlock(in_channel, in_channel, downsample=False) for i in range(n_res)] | |
| self.stem = nn.Sequential(*stem) | |
| self.content = nn.Sequential( | |
| ConvLayer(in_channel, in_channel, 1), | |
| ConvLayer(in_channel, in_channel, 1) | |
| ) | |
| style = [] | |
| for i in range(log_size-num_down, 2, -1): | |
| out_channel = channels[2 ** (i - 1)] | |
| style.append(ConvLayer(in_channel, out_channel, 3, downsample=True)) | |
| in_channel = out_channel | |
| style += [ | |
| nn.Flatten(), | |
| EqualLinear(channels[4] * 4 * 4, channels[4], activation='fused_lrelu'), | |
| EqualLinear(channels[4], latent_dim), | |
| ] | |
| self.style = nn.Sequential(*style) | |
| def forward(self, input): | |
| act = self.stem(input) | |
| content = self.content(act) | |
| style = self.style(act) | |
| return content, style | |
| class StyleEncoder(nn.Module): | |
| def __init__(self, size, style_dim, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]): | |
| super().__init__() | |
| convs = [ConvLayer(3, channels[size], 1)] | |
| log_size = int(math.log(size, 2)) | |
| in_channel = channels[size] | |
| num_down = 6 | |
| for i in range(log_size, log_size-num_down, -1): | |
| w = 2 ** (i - 1) | |
| out_channel = channels[w] | |
| convs.append(ConvLayer(in_channel, out_channel, 3, downsample=True)) | |
| in_channel = out_channel | |
| convs += [ | |
| nn.Flatten(), | |
| EqualLinear(channels[4] * 4 * 4, channels[4], activation='fused_lrelu'), EqualLinear(channels[4], style_dim), | |
| ] | |
| self.convs = nn.Sequential(*convs) | |
| def forward(self, input): | |
| style = self.convs(input) | |
| return style.view(input.size(0), -1) | |
| class LatDiscriminator(nn.Module): | |
| def __init__(self, style_dim): | |
| super().__init__() | |
| fc = [EqualLinear(style_dim, 256, activation='fused_lrelu')] | |
| for i in range(3): | |
| fc += [EqualLinear(256, 256, activation='fused_lrelu')] | |
| fc += [FCMinibatchStd(256, 256)] | |
| fc += [EqualLinear(256, 1)] | |
| self.fc = nn.Sequential(*fc) | |
| def forward(self, input): | |
| return [self.fc(input), ] | |
| class FCMinibatchStd(nn.Module): | |
| def __init__(self, in_channel, out_channel): | |
| super().__init__() | |
| self.fc = EqualLinear(in_channel+1, out_channel, activation='fused_lrelu') | |
| def forward(self, out): | |
| stddev = torch.sqrt(out.var(0, unbiased=False) + 1e-8).mean().view(1,1).repeat(out.size(0), 1) | |
| out = torch.cat([out, stddev], 1) | |
| out = self.fc(out) | |
| return out | |