diff options
Diffstat (limited to 'r_chainner/archs/face/stylegan2_clean_arch.py')
| -rw-r--r-- | r_chainner/archs/face/stylegan2_clean_arch.py | 453 |
1 files changed, 453 insertions, 0 deletions
diff --git a/r_chainner/archs/face/stylegan2_clean_arch.py b/r_chainner/archs/face/stylegan2_clean_arch.py new file mode 100644 index 0000000..a68655a --- /dev/null +++ b/r_chainner/archs/face/stylegan2_clean_arch.py @@ -0,0 +1,453 @@ +# pylint: skip-file
+# type: ignore
+import math
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.nn import init
+from torch.nn.modules.batchnorm import _BatchNorm
+
+
+@torch.no_grad()
+def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
+ """Initialize network weights.
+ Args:
+ module_list (list[nn.Module] | nn.Module): Modules to be initialized.
+ scale (float): Scale initialized weights, especially for residual
+ blocks. Default: 1.
+ bias_fill (float): The value to fill bias. Default: 0
+ kwargs (dict): Other arguments for initialization function.
+ """
+ if not isinstance(module_list, list):
+ module_list = [module_list]
+ for module in module_list:
+ for m in module.modules():
+ if isinstance(m, nn.Conv2d):
+ init.kaiming_normal_(m.weight, **kwargs)
+ m.weight.data *= scale
+ if m.bias is not None:
+ m.bias.data.fill_(bias_fill)
+ elif isinstance(m, nn.Linear):
+ init.kaiming_normal_(m.weight, **kwargs)
+ m.weight.data *= scale
+ if m.bias is not None:
+ m.bias.data.fill_(bias_fill)
+ elif isinstance(m, _BatchNorm):
+ init.constant_(m.weight, 1)
+ if m.bias is not None:
+ m.bias.data.fill_(bias_fill)
+
+
+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)
+
+
+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.
+ 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,
+ 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
+
+ # modulation inside each modulated conv
+ self.modulation = nn.Linear(num_style_feat, in_channels, bias=True)
+ # initialization
+ default_init_weights(
+ self.modulation,
+ scale=1,
+ bias_fill=1,
+ a=0,
+ mode="fan_in",
+ nonlinearity="linear",
+ )
+
+ self.weight = nn.Parameter(
+ torch.randn(1, out_channels, in_channels, kernel_size, kernel_size)
+ / math.sqrt(in_channels * kernel_size**2)
+ )
+ 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.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
+ )
+
+ # upsample or downsample if necessary
+ if self.sample_mode == "upsample":
+ x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=False)
+ elif self.sample_mode == "downsample":
+ x = F.interpolate(x, scale_factor=0.5, mode="bilinear", align_corners=False)
+
+ b, c, h, w = x.shape
+ 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}, out_channels={self.out_channels}, "
+ f"kernel_size={self.kernel_size}, demodulate={self.demodulate}, sample_mode={self.sample_mode})"
+ )
+
+
+class StyleConv(nn.Module):
+ """Style conv used in 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.
+ 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.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ kernel_size,
+ num_style_feat,
+ demodulate=True,
+ sample_mode=None,
+ ):
+ super(StyleConv, self).__init__()
+ self.modulated_conv = ModulatedConv2d(
+ in_channels,
+ out_channels,
+ kernel_size,
+ num_style_feat,
+ demodulate=demodulate,
+ sample_mode=sample_mode,
+ )
+ self.weight = nn.Parameter(torch.zeros(1)) # for noise injection
+ self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1))
+ self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+ def forward(self, x, style, noise=None):
+ # modulate
+ out = self.modulated_conv(x, style) * 2**0.5 # for conversion
+ # 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
+ # add bias
+ out = out + self.bias
+ # activation
+ out = self.activate(out)
+ return out
+
+
+class ToRGB(nn.Module):
+ """To RGB (image space) 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.
+ """
+
+ def __init__(self, in_channels, num_style_feat, upsample=True):
+ super(ToRGB, self).__init__()
+ self.upsample = upsample
+ 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 = F.interpolate(
+ skip, scale_factor=2, mode="bilinear", align_corners=False
+ )
+ 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 StyleGAN2GeneratorClean(nn.Module):
+ """Clean version of 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.
+ narrow (float): Narrow ratio for channels. Default: 1.0.
+ """
+
+ def __init__(
+ self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1
+ ):
+ super(StyleGAN2GeneratorClean, 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.extend(
+ [
+ nn.Linear(num_style_feat, num_style_feat, bias=True),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ ]
+ )
+ self.style_mlp = nn.Sequential(*style_mlp_layers)
+ # initialization
+ default_init_weights(
+ self.style_mlp,
+ scale=1,
+ bias_fill=0,
+ a=0.2,
+ mode="fan_in",
+ nonlinearity="leaky_relu",
+ )
+
+ # channel list
+ 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,
+ )
+ self.to_rgb1 = ToRGB(channels["4"], num_style_feat, upsample=False)
+
+ 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",
+ )
+ )
+ self.style_convs.append(
+ StyleConv(
+ out_channels,
+ out_channels,
+ kernel_size=3,
+ num_style_feat=num_style_feat,
+ demodulate=True,
+ sample_mode=None,
+ )
+ )
+ self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True))
+ 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 StyleGAN2GeneratorClean.
+ 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): The truncation ratio. Default: 1.
+ truncation_latent (Tensor | None): The truncation latent tensor. 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 latents 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) # feature back to the rgb space
+ i += 2
+
+ image = skip
+
+ if return_latents:
+ return image, latent
+ else:
+ return image, None
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