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 # Ops -> The rosinality repo uses native cuda kernels for fused LeakyReLUs and upsamplers. This version extracts the # "cpu" alternative code and uses that instead for compatibility reasons. from trainer.networks import register_model from utils.util import opt_get class FusedLeakyReLU(nn.Module): def __init__(self, channel, bias=True, negative_slope=0.2, scale=2 ** 0.5): super().__init__() if bias: self.bias = nn.Parameter(torch.zeros(channel)) else: self.bias = None self.negative_slope = negative_slope self.scale = scale def forward(self, input): return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5): if bias is not None: rest_dim = [1] * (input.ndim - bias.ndim - 1) return ( F.leaky_relu( input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope ) * scale ) else: return F.leaky_relu(input, negative_slope=0.2) * scale def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): out = upfirdn2d_native( input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1] ) return out def upfirdn2d_native( input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 ): _, channel, in_h, in_w = input.shape input = input.reshape(-1, in_h, in_w, 1) _, in_h, in_w, minor = input.shape kernel_h, kernel_w = kernel.shape out = input.view(-1, in_h, 1, in_w, 1, minor) out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) out = out.view(-1, in_h * up_y, in_w * up_x, minor) out = F.pad( out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] ) out = out[ :, max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), :, ] out = out.permute(0, 3, 1, 2) out = out.reshape( [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1] ) w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) out = F.conv2d(out, w) out = out.reshape( -1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, ) out = out.permute(0, 2, 3, 1) out = out[:, ::down_y, ::down_x, :] out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 return out.view(-1, channel, out_h, out_w) # /end Ops 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): 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], ): 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 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 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 = 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, 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.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, noise=None): out = self.conv(input, style) out = self.noise(out, noise=noise) # out = out + self.bias 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 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 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 not input_is_latent: styles = [self.style(s) for s in styles] 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 truncation < 1: style_t = [] for style in styles: style_t.append( truncation_latent + truncation * (style - truncation_latent) ) styles = style_t if len(styles) < 2: inject_index = self.n_latent if styles[0].ndim < 3: latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) else: latent = styles[0] else: if inject_index is None: inject_index = random.randint(1, self.n_latent - 1) latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1) latent = torch.cat([latent, latent2], 1) 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, latent 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 @register_model def register_stylegan2_rosinality_gen(opt_net, opt): kw = opt_get(opt_net, ['kwargs'], {}) return Generator(**kw) @register_model def register_stylegan2_rosinality_disc(opt_net, opt): kw = opt_get(opt_net, ['kwargs'], {}) return Discriminator(**kw)