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| import math | |
| import random | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| from basicsr.ops.fused_act import FusedLeakyReLU, fused_leaky_relu | |
| from basicsr.ops.upfirdn2d import upfirdn2d | |
| from basicsr.utils.registry import ARCH_REGISTRY | |
| class NormStyleCode(nn.Module): | |
| def forward(self, x): | |
| """Normalize the style codes. | |
| Args: | |
| x (Tensor): Style codes with shape (b, c). | |
| Returns: | |
| Tensor: Normalized tensor. | |
| """ | |
| return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) | |
| def make_resample_kernel(k): | |
| """Make resampling kernel for UpFirDn. | |
| Args: | |
| k (list[int]): A list indicating the 1D resample kernel magnitude. | |
| Returns: | |
| Tensor: 2D resampled kernel. | |
| """ | |
| k = torch.tensor(k, dtype=torch.float32) | |
| if k.ndim == 1: | |
| k = k[None, :] * k[:, None] # to 2D kernel, outer product | |
| # normalize | |
| k /= k.sum() | |
| return k | |
| class UpFirDnUpsample(nn.Module): | |
| """Upsample, FIR filter, and downsample (upsampole version). | |
| References: | |
| 1. https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.upfirdn.html # noqa: E501 | |
| 2. http://www.ece.northwestern.edu/local-apps/matlabhelp/toolbox/signal/upfirdn.html # noqa: E501 | |
| Args: | |
| resample_kernel (list[int]): A list indicating the 1D resample kernel | |
| magnitude. | |
| factor (int): Upsampling scale factor. Default: 2. | |
| """ | |
| def __init__(self, resample_kernel, factor=2): | |
| super(UpFirDnUpsample, self).__init__() | |
| self.kernel = make_resample_kernel(resample_kernel) * (factor**2) | |
| self.factor = factor | |
| pad = self.kernel.shape[0] - factor | |
| self.pad = ((pad + 1) // 2 + factor - 1, pad // 2) | |
| def forward(self, x): | |
| out = upfirdn2d(x, self.kernel.type_as(x), up=self.factor, down=1, pad=self.pad) | |
| return out | |
| def __repr__(self): | |
| return (f'{self.__class__.__name__}(factor={self.factor})') | |
| class UpFirDnDownsample(nn.Module): | |
| """Upsample, FIR filter, and downsample (downsampole version). | |
| Args: | |
| resample_kernel (list[int]): A list indicating the 1D resample kernel | |
| magnitude. | |
| factor (int): Downsampling scale factor. Default: 2. | |
| """ | |
| def __init__(self, resample_kernel, factor=2): | |
| super(UpFirDnDownsample, self).__init__() | |
| self.kernel = make_resample_kernel(resample_kernel) | |
| self.factor = factor | |
| pad = self.kernel.shape[0] - factor | |
| self.pad = ((pad + 1) // 2, pad // 2) | |
| def forward(self, x): | |
| out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=self.factor, pad=self.pad) | |
| return out | |
| def __repr__(self): | |
| return (f'{self.__class__.__name__}(factor={self.factor})') | |
| class UpFirDnSmooth(nn.Module): | |
| """Upsample, FIR filter, and downsample (smooth version). | |
| Args: | |
| resample_kernel (list[int]): A list indicating the 1D resample kernel | |
| magnitude. | |
| upsample_factor (int): Upsampling scale factor. Default: 1. | |
| downsample_factor (int): Downsampling scale factor. Default: 1. | |
| kernel_size (int): Kernel size: Default: 1. | |
| """ | |
| def __init__(self, resample_kernel, upsample_factor=1, downsample_factor=1, kernel_size=1): | |
| super(UpFirDnSmooth, self).__init__() | |
| self.upsample_factor = upsample_factor | |
| self.downsample_factor = downsample_factor | |
| self.kernel = make_resample_kernel(resample_kernel) | |
| if upsample_factor > 1: | |
| self.kernel = self.kernel * (upsample_factor**2) | |
| if upsample_factor > 1: | |
| pad = (self.kernel.shape[0] - upsample_factor) - (kernel_size - 1) | |
| self.pad = ((pad + 1) // 2 + upsample_factor - 1, pad // 2 + 1) | |
| elif downsample_factor > 1: | |
| pad = (self.kernel.shape[0] - downsample_factor) + (kernel_size - 1) | |
| self.pad = ((pad + 1) // 2, pad // 2) | |
| else: | |
| raise NotImplementedError | |
| def forward(self, x): | |
| out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=1, pad=self.pad) | |
| return out | |
| def __repr__(self): | |
| return (f'{self.__class__.__name__}(upsample_factor={self.upsample_factor}' | |
| f', downsample_factor={self.downsample_factor})') | |
| class EqualLinear(nn.Module): | |
| """Equalized Linear as StyleGAN2. | |
| Args: | |
| in_channels (int): Size of each sample. | |
| out_channels (int): Size of each output sample. | |
| bias (bool): If set to ``False``, the layer will not learn an additive | |
| bias. Default: ``True``. | |
| bias_init_val (float): Bias initialized value. Default: 0. | |
| lr_mul (float): Learning rate multiplier. Default: 1. | |
| activation (None | str): The activation after ``linear`` operation. | |
| Supported: 'fused_lrelu', None. Default: None. | |
| """ | |
| def __init__(self, in_channels, out_channels, bias=True, bias_init_val=0, lr_mul=1, activation=None): | |
| super(EqualLinear, self).__init__() | |
| self.in_channels = in_channels | |
| self.out_channels = out_channels | |
| self.lr_mul = lr_mul | |
| self.activation = activation | |
| if self.activation not in ['fused_lrelu', None]: | |
| raise ValueError(f'Wrong activation value in EqualLinear: {activation}' | |
| "Supported ones are: ['fused_lrelu', None].") | |
| self.scale = (1 / math.sqrt(in_channels)) * lr_mul | |
| self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) | |
| if bias: | |
| self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) | |
| else: | |
| self.register_parameter('bias', None) | |
| def forward(self, x): | |
| if self.bias is None: | |
| bias = None | |
| else: | |
| bias = self.bias * self.lr_mul | |
| if self.activation == 'fused_lrelu': | |
| out = F.linear(x, self.weight * self.scale) | |
| out = fused_leaky_relu(out, bias) | |
| else: | |
| out = F.linear(x, self.weight * self.scale, bias=bias) | |
| return out | |
| def __repr__(self): | |
| return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' | |
| f'out_channels={self.out_channels}, bias={self.bias is not None})') | |
| class ModulatedConv2d(nn.Module): | |
| """Modulated Conv2d used in StyleGAN2. | |
| There is no bias in ModulatedConv2d. | |
| Args: | |
| in_channels (int): Channel number of the input. | |
| out_channels (int): Channel number of the output. | |
| kernel_size (int): Size of the convolving kernel. | |
| num_style_feat (int): Channel number of style features. | |
| demodulate (bool): Whether to demodulate in the conv layer. | |
| Default: True. | |
| sample_mode (str | None): Indicating 'upsample', 'downsample' or None. | |
| Default: None. | |
| resample_kernel (list[int]): A list indicating the 1D resample kernel | |
| magnitude. Default: (1, 3, 3, 1). | |
| eps (float): A value added to the denominator for numerical stability. | |
| Default: 1e-8. | |
| """ | |
| def __init__(self, | |
| in_channels, | |
| out_channels, | |
| kernel_size, | |
| num_style_feat, | |
| demodulate=True, | |
| sample_mode=None, | |
| resample_kernel=(1, 3, 3, 1), | |
| eps=1e-8): | |
| super(ModulatedConv2d, self).__init__() | |
| self.in_channels = in_channels | |
| self.out_channels = out_channels | |
| self.kernel_size = kernel_size | |
| self.demodulate = demodulate | |
| self.sample_mode = sample_mode | |
| self.eps = eps | |
| if self.sample_mode == 'upsample': | |
| self.smooth = UpFirDnSmooth( | |
| resample_kernel, upsample_factor=2, downsample_factor=1, kernel_size=kernel_size) | |
| elif self.sample_mode == 'downsample': | |
| self.smooth = UpFirDnSmooth( | |
| resample_kernel, upsample_factor=1, downsample_factor=2, kernel_size=kernel_size) | |
| elif self.sample_mode is None: | |
| pass | |
| else: | |
| raise ValueError(f'Wrong sample mode {self.sample_mode}, ' | |
| "supported ones are ['upsample', 'downsample', None].") | |
| self.scale = 1 / math.sqrt(in_channels * kernel_size**2) | |
| # modulation inside each modulated conv | |
| self.modulation = EqualLinear( | |
| num_style_feat, in_channels, bias=True, bias_init_val=1, lr_mul=1, activation=None) | |
| self.weight = nn.Parameter(torch.randn(1, out_channels, in_channels, kernel_size, kernel_size)) | |
| self.padding = kernel_size // 2 | |
| def forward(self, x, style): | |
| """Forward function. | |
| Args: | |
| x (Tensor): Tensor with shape (b, c, h, w). | |
| style (Tensor): Tensor with shape (b, num_style_feat). | |
| Returns: | |
| Tensor: Modulated tensor after convolution. | |
| """ | |
| b, c, h, w = x.shape # c = c_in | |
| # weight modulation | |
| style = self.modulation(style).view(b, 1, c, 1, 1) | |
| # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) | |
| weight = self.scale * self.weight * style # (b, c_out, c_in, k, k) | |
| if self.demodulate: | |
| demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) | |
| weight = weight * demod.view(b, self.out_channels, 1, 1, 1) | |
| weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size) | |
| if self.sample_mode == 'upsample': | |
| x = x.view(1, b * c, h, w) | |
| weight = weight.view(b, self.out_channels, c, self.kernel_size, self.kernel_size) | |
| weight = weight.transpose(1, 2).reshape(b * c, self.out_channels, self.kernel_size, self.kernel_size) | |
| out = F.conv_transpose2d(x, weight, padding=0, stride=2, groups=b) | |
| out = out.view(b, self.out_channels, *out.shape[2:4]) | |
| out = self.smooth(out) | |
| elif self.sample_mode == 'downsample': | |
| x = self.smooth(x) | |
| x = x.view(1, b * c, *x.shape[2:4]) | |
| out = F.conv2d(x, weight, padding=0, stride=2, groups=b) | |
| out = out.view(b, self.out_channels, *out.shape[2:4]) | |
| else: | |
| x = x.view(1, b * c, h, w) | |
| # weight: (b*c_out, c_in, k, k), groups=b | |
| out = F.conv2d(x, weight, padding=self.padding, groups=b) | |
| out = out.view(b, self.out_channels, *out.shape[2:4]) | |
| return out | |
| def __repr__(self): | |
| return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' | |
| f'out_channels={self.out_channels}, ' | |
| f'kernel_size={self.kernel_size}, ' | |
| f'demodulate={self.demodulate}, sample_mode={self.sample_mode})') | |
| class StyleConv(nn.Module): | |
| """Style conv. | |
| Args: | |
| in_channels (int): Channel number of the input. | |
| out_channels (int): Channel number of the output. | |
| kernel_size (int): Size of the convolving kernel. | |
| num_style_feat (int): Channel number of style features. | |
| demodulate (bool): Whether demodulate in the conv layer. Default: True. | |
| sample_mode (str | None): Indicating 'upsample', 'downsample' or None. | |
| Default: None. | |
| resample_kernel (list[int]): A list indicating the 1D resample kernel | |
| magnitude. Default: (1, 3, 3, 1). | |
| """ | |
| def __init__(self, | |
| in_channels, | |
| out_channels, | |
| kernel_size, | |
| num_style_feat, | |
| demodulate=True, | |
| sample_mode=None, | |
| resample_kernel=(1, 3, 3, 1)): | |
| super(StyleConv, self).__init__() | |
| self.modulated_conv = ModulatedConv2d( | |
| in_channels, | |
| out_channels, | |
| kernel_size, | |
| num_style_feat, | |
| demodulate=demodulate, | |
| sample_mode=sample_mode, | |
| resample_kernel=resample_kernel) | |
| self.weight = nn.Parameter(torch.zeros(1)) # for noise injection | |
| self.activate = FusedLeakyReLU(out_channels) | |
| def forward(self, x, style, noise=None): | |
| # modulate | |
| out = self.modulated_conv(x, style) | |
| # noise injection | |
| if noise is None: | |
| b, _, h, w = out.shape | |
| noise = out.new_empty(b, 1, h, w).normal_() | |
| out = out + self.weight * noise | |
| # activation (with bias) | |
| out = self.activate(out) | |
| return out | |
| class ToRGB(nn.Module): | |
| """To RGB from features. | |
| Args: | |
| in_channels (int): Channel number of input. | |
| num_style_feat (int): Channel number of style features. | |
| upsample (bool): Whether to upsample. Default: True. | |
| resample_kernel (list[int]): A list indicating the 1D resample kernel | |
| magnitude. Default: (1, 3, 3, 1). | |
| """ | |
| def __init__(self, in_channels, num_style_feat, upsample=True, resample_kernel=(1, 3, 3, 1)): | |
| super(ToRGB, self).__init__() | |
| if upsample: | |
| self.upsample = UpFirDnUpsample(resample_kernel, factor=2) | |
| else: | |
| self.upsample = None | |
| self.modulated_conv = ModulatedConv2d( | |
| in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None) | |
| self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) | |
| def forward(self, x, style, skip=None): | |
| """Forward function. | |
| Args: | |
| x (Tensor): Feature tensor with shape (b, c, h, w). | |
| style (Tensor): Tensor with shape (b, num_style_feat). | |
| skip (Tensor): Base/skip tensor. Default: None. | |
| Returns: | |
| Tensor: RGB images. | |
| """ | |
| out = self.modulated_conv(x, style) | |
| out = out + self.bias | |
| if skip is not None: | |
| if self.upsample: | |
| skip = self.upsample(skip) | |
| out = out + skip | |
| return out | |
| class ConstantInput(nn.Module): | |
| """Constant input. | |
| Args: | |
| num_channel (int): Channel number of constant input. | |
| size (int): Spatial size of constant input. | |
| """ | |
| def __init__(self, num_channel, size): | |
| super(ConstantInput, self).__init__() | |
| self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) | |
| def forward(self, batch): | |
| out = self.weight.repeat(batch, 1, 1, 1) | |
| return out | |
| class StyleGAN2Generator(nn.Module): | |
| """StyleGAN2 Generator. | |
| Args: | |
| out_size (int): The spatial size of outputs. | |
| num_style_feat (int): Channel number of style features. Default: 512. | |
| num_mlp (int): Layer number of MLP style layers. Default: 8. | |
| channel_multiplier (int): Channel multiplier for large networks of | |
| StyleGAN2. Default: 2. | |
| resample_kernel (list[int]): A list indicating the 1D resample kernel | |
| magnitude. A cross production will be applied to extent 1D resample | |
| kernel to 2D resample kernel. Default: (1, 3, 3, 1). | |
| lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. | |
| narrow (float): Narrow ratio for channels. Default: 1.0. | |
| """ | |
| def __init__(self, | |
| out_size, | |
| num_style_feat=512, | |
| num_mlp=8, | |
| channel_multiplier=2, | |
| resample_kernel=(1, 3, 3, 1), | |
| lr_mlp=0.01, | |
| narrow=1): | |
| super(StyleGAN2Generator, self).__init__() | |
| # Style MLP layers | |
| self.num_style_feat = num_style_feat | |
| style_mlp_layers = [NormStyleCode()] | |
| for i in range(num_mlp): | |
| style_mlp_layers.append( | |
| EqualLinear( | |
| num_style_feat, num_style_feat, bias=True, bias_init_val=0, lr_mul=lr_mlp, | |
| activation='fused_lrelu')) | |
| self.style_mlp = nn.Sequential(*style_mlp_layers) | |
| channels = { | |
| '4': int(512 * narrow), | |
| '8': int(512 * narrow), | |
| '16': int(512 * narrow), | |
| '32': int(512 * narrow), | |
| '64': int(256 * channel_multiplier * narrow), | |
| '128': int(128 * channel_multiplier * narrow), | |
| '256': int(64 * channel_multiplier * narrow), | |
| '512': int(32 * channel_multiplier * narrow), | |
| '1024': int(16 * channel_multiplier * narrow) | |
| } | |
| self.channels = channels | |
| self.constant_input = ConstantInput(channels['4'], size=4) | |
| self.style_conv1 = StyleConv( | |
| channels['4'], | |
| channels['4'], | |
| kernel_size=3, | |
| num_style_feat=num_style_feat, | |
| demodulate=True, | |
| sample_mode=None, | |
| resample_kernel=resample_kernel) | |
| self.to_rgb1 = ToRGB(channels['4'], num_style_feat, upsample=False, resample_kernel=resample_kernel) | |
| self.log_size = int(math.log(out_size, 2)) | |
| self.num_layers = (self.log_size - 2) * 2 + 1 | |
| self.num_latent = self.log_size * 2 - 2 | |
| self.style_convs = nn.ModuleList() | |
| self.to_rgbs = nn.ModuleList() | |
| self.noises = nn.Module() | |
| in_channels = channels['4'] | |
| # noise | |
| for layer_idx in range(self.num_layers): | |
| resolution = 2**((layer_idx + 5) // 2) | |
| shape = [1, 1, resolution, resolution] | |
| self.noises.register_buffer(f'noise{layer_idx}', torch.randn(*shape)) | |
| # style convs and to_rgbs | |
| for i in range(3, self.log_size + 1): | |
| out_channels = channels[f'{2**i}'] | |
| self.style_convs.append( | |
| StyleConv( | |
| in_channels, | |
| out_channels, | |
| kernel_size=3, | |
| num_style_feat=num_style_feat, | |
| demodulate=True, | |
| sample_mode='upsample', | |
| resample_kernel=resample_kernel, | |
| )) | |
| self.style_convs.append( | |
| StyleConv( | |
| out_channels, | |
| out_channels, | |
| kernel_size=3, | |
| num_style_feat=num_style_feat, | |
| demodulate=True, | |
| sample_mode=None, | |
| resample_kernel=resample_kernel)) | |
| self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True, resample_kernel=resample_kernel)) | |
| in_channels = out_channels | |
| def make_noise(self): | |
| """Make noise for noise injection.""" | |
| device = self.constant_input.weight.device | |
| noises = [torch.randn(1, 1, 4, 4, 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 get_latent(self, x): | |
| return self.style_mlp(x) | |
| def mean_latent(self, num_latent): | |
| latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device) | |
| latent = self.style_mlp(latent_in).mean(0, keepdim=True) | |
| return latent | |
| def forward(self, | |
| styles, | |
| input_is_latent=False, | |
| noise=None, | |
| randomize_noise=True, | |
| truncation=1, | |
| truncation_latent=None, | |
| inject_index=None, | |
| return_latents=False): | |
| """Forward function for StyleGAN2Generator. | |
| Args: | |
| styles (list[Tensor]): Sample codes of styles. | |
| input_is_latent (bool): Whether input is latent style. | |
| Default: False. | |
| noise (Tensor | None): Input noise or None. Default: None. | |
| randomize_noise (bool): Randomize noise, used when 'noise' is | |
| False. Default: True. | |
| truncation (float): TODO. Default: 1. | |
| truncation_latent (Tensor | None): TODO. Default: None. | |
| inject_index (int | None): The injection index for mixing noise. | |
| Default: None. | |
| return_latents (bool): Whether to return style latents. | |
| Default: False. | |
| """ | |
| # style codes -> latents with Style MLP layer | |
| if not input_is_latent: | |
| styles = [self.style_mlp(s) for s in styles] | |
| # noises | |
| if noise is None: | |
| if randomize_noise: | |
| noise = [None] * self.num_layers # for each style conv layer | |
| else: # use the stored noise | |
| noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] | |
| # style truncation | |
| if truncation < 1: | |
| style_truncation = [] | |
| for style in styles: | |
| style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) | |
| styles = style_truncation | |
| # get style latent with injection | |
| if len(styles) == 1: | |
| inject_index = self.num_latent | |
| if styles[0].ndim < 3: | |
| # repeat latent code for all the layers | |
| latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) | |
| else: # used for encoder with different latent code for each layer | |
| latent = styles[0] | |
| elif len(styles) == 2: # mixing noises | |
| if inject_index is None: | |
| inject_index = random.randint(1, self.num_latent - 1) | |
| latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) | |
| latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) | |
| latent = torch.cat([latent1, latent2], 1) | |
| # main generation | |
| out = self.constant_input(latent.shape[0]) | |
| out = self.style_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.style_convs[::2], self.style_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 | |
| if return_latents: | |
| return image, latent | |
| else: | |
| return image, None | |
| class ScaledLeakyReLU(nn.Module): | |
| """Scaled LeakyReLU. | |
| Args: | |
| negative_slope (float): Negative slope. Default: 0.2. | |
| """ | |
| def __init__(self, negative_slope=0.2): | |
| super(ScaledLeakyReLU, self).__init__() | |
| self.negative_slope = negative_slope | |
| def forward(self, x): | |
| out = F.leaky_relu(x, negative_slope=self.negative_slope) | |
| return out * math.sqrt(2) | |
| class EqualConv2d(nn.Module): | |
| """Equalized Linear as StyleGAN2. | |
| Args: | |
| in_channels (int): Channel number of the input. | |
| out_channels (int): Channel number of the output. | |
| kernel_size (int): Size of the convolving kernel. | |
| stride (int): Stride of the convolution. Default: 1 | |
| padding (int): Zero-padding added to both sides of the input. | |
| Default: 0. | |
| bias (bool): If ``True``, adds a learnable bias to the output. | |
| Default: ``True``. | |
| bias_init_val (float): Bias initialized value. Default: 0. | |
| """ | |
| def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True, bias_init_val=0): | |
| super(EqualConv2d, self).__init__() | |
| self.in_channels = in_channels | |
| self.out_channels = out_channels | |
| self.kernel_size = kernel_size | |
| self.stride = stride | |
| self.padding = padding | |
| self.scale = 1 / math.sqrt(in_channels * kernel_size**2) | |
| self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size)) | |
| if bias: | |
| self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) | |
| else: | |
| self.register_parameter('bias', None) | |
| def forward(self, x): | |
| out = F.conv2d( | |
| x, | |
| self.weight * self.scale, | |
| bias=self.bias, | |
| stride=self.stride, | |
| padding=self.padding, | |
| ) | |
| return out | |
| def __repr__(self): | |
| return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' | |
| f'out_channels={self.out_channels}, ' | |
| f'kernel_size={self.kernel_size},' | |
| f' stride={self.stride}, padding={self.padding}, ' | |
| f'bias={self.bias is not None})') | |
| class ConvLayer(nn.Sequential): | |
| """Conv Layer used in StyleGAN2 Discriminator. | |
| Args: | |
| in_channels (int): Channel number of the input. | |
| out_channels (int): Channel number of the output. | |
| kernel_size (int): Kernel size. | |
| downsample (bool): Whether downsample by a factor of 2. | |
| Default: False. | |
| resample_kernel (list[int]): A list indicating the 1D resample | |
| kernel magnitude. A cross production will be applied to | |
| extent 1D resample kernel to 2D resample kernel. | |
| Default: (1, 3, 3, 1). | |
| bias (bool): Whether with bias. Default: True. | |
| activate (bool): Whether use activateion. Default: True. | |
| """ | |
| def __init__(self, | |
| in_channels, | |
| out_channels, | |
| kernel_size, | |
| downsample=False, | |
| resample_kernel=(1, 3, 3, 1), | |
| bias=True, | |
| activate=True): | |
| layers = [] | |
| # downsample | |
| if downsample: | |
| layers.append( | |
| UpFirDnSmooth(resample_kernel, upsample_factor=1, downsample_factor=2, kernel_size=kernel_size)) | |
| stride = 2 | |
| self.padding = 0 | |
| else: | |
| stride = 1 | |
| self.padding = kernel_size // 2 | |
| # conv | |
| layers.append( | |
| EqualConv2d( | |
| in_channels, out_channels, kernel_size, stride=stride, padding=self.padding, bias=bias | |
| and not activate)) | |
| # activation | |
| if activate: | |
| if bias: | |
| layers.append(FusedLeakyReLU(out_channels)) | |
| else: | |
| layers.append(ScaledLeakyReLU(0.2)) | |
| super(ConvLayer, self).__init__(*layers) | |
| class ResBlock(nn.Module): | |
| """Residual block used in StyleGAN2 Discriminator. | |
| Args: | |
| in_channels (int): Channel number of the input. | |
| out_channels (int): Channel number of the output. | |
| resample_kernel (list[int]): A list indicating the 1D resample | |
| kernel magnitude. A cross production will be applied to | |
| extent 1D resample kernel to 2D resample kernel. | |
| Default: (1, 3, 3, 1). | |
| """ | |
| def __init__(self, in_channels, out_channels, resample_kernel=(1, 3, 3, 1)): | |
| super(ResBlock, self).__init__() | |
| self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) | |
| self.conv2 = ConvLayer( | |
| in_channels, out_channels, 3, downsample=True, resample_kernel=resample_kernel, bias=True, activate=True) | |
| self.skip = ConvLayer( | |
| in_channels, out_channels, 1, downsample=True, resample_kernel=resample_kernel, bias=False, activate=False) | |
| def forward(self, x): | |
| out = self.conv1(x) | |
| out = self.conv2(out) | |
| skip = self.skip(x) | |
| out = (out + skip) / math.sqrt(2) | |
| return out | |
| class StyleGAN2Discriminator(nn.Module): | |
| """StyleGAN2 Discriminator. | |
| Args: | |
| out_size (int): The spatial size of outputs. | |
| channel_multiplier (int): Channel multiplier for large networks of | |
| StyleGAN2. Default: 2. | |
| resample_kernel (list[int]): A list indicating the 1D resample kernel | |
| magnitude. A cross production will be applied to extent 1D resample | |
| kernel to 2D resample kernel. Default: (1, 3, 3, 1). | |
| stddev_group (int): For group stddev statistics. Default: 4. | |
| narrow (float): Narrow ratio for channels. Default: 1.0. | |
| """ | |
| def __init__(self, out_size, channel_multiplier=2, resample_kernel=(1, 3, 3, 1), stddev_group=4, narrow=1): | |
| super(StyleGAN2Discriminator, self).__init__() | |
| channels = { | |
| '4': int(512 * narrow), | |
| '8': int(512 * narrow), | |
| '16': int(512 * narrow), | |
| '32': int(512 * narrow), | |
| '64': int(256 * channel_multiplier * narrow), | |
| '128': int(128 * channel_multiplier * narrow), | |
| '256': int(64 * channel_multiplier * narrow), | |
| '512': int(32 * channel_multiplier * narrow), | |
| '1024': int(16 * channel_multiplier * narrow) | |
| } | |
| log_size = int(math.log(out_size, 2)) | |
| conv_body = [ConvLayer(3, channels[f'{out_size}'], 1, bias=True, activate=True)] | |
| in_channels = channels[f'{out_size}'] | |
| for i in range(log_size, 2, -1): | |
| out_channels = channels[f'{2**(i - 1)}'] | |
| conv_body.append(ResBlock(in_channels, out_channels, resample_kernel)) | |
| in_channels = out_channels | |
| self.conv_body = nn.Sequential(*conv_body) | |
| self.final_conv = ConvLayer(in_channels + 1, channels['4'], 3, bias=True, activate=True) | |
| self.final_linear = nn.Sequential( | |
| EqualLinear( | |
| channels['4'] * 4 * 4, channels['4'], bias=True, bias_init_val=0, lr_mul=1, activation='fused_lrelu'), | |
| EqualLinear(channels['4'], 1, bias=True, bias_init_val=0, lr_mul=1, activation=None), | |
| ) | |
| self.stddev_group = stddev_group | |
| self.stddev_feat = 1 | |
| def forward(self, x): | |
| out = self.conv_body(x) | |
| b, c, h, w = out.shape | |
| # concatenate a group stddev statistics to out | |
| group = min(b, self.stddev_group) # Minibatch must be divisible by (or smaller than) group_size | |
| stddev = out.view(group, -1, self.stddev_feat, c // self.stddev_feat, h, w) | |
| 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, h, w) | |
| out = torch.cat([out, stddev], 1) | |
| out = self.final_conv(out) | |
| out = out.view(b, -1) | |
| out = self.final_linear(out) | |
| return out | |