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import math |
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import random |
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import torch |
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from torch import nn |
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from torch.nn import functional as F |
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from . import FusedLeakyReLU, fused_leaky_relu, upfirdn2d |
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class StyleBlock(nn.Module): |
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def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]): |
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super().__init__() |
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self.conv1 = ConvLayer(in_channel, in_channel, 3) |
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self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True) |
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self.skip = ConvLayer( |
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in_channel, out_channel, 1, downsample=True, activate=False, bias=False |
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) |
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def forward(self, input): |
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out = self.conv1(input) |
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out = self.conv2(out) |
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skip = self.skip(input) |
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out = (out + skip) / math.sqrt(2) |
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return out |
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class ConvLayer(nn.Sequential): |
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def __init__( |
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self, |
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in_channel, |
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out_channel, |
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kernel_size, |
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downsample=False, |
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blur_kernel=[1, 3, 3, 1], |
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bias=True, |
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activate=True, |
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): |
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layers = [] |
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if downsample: |
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factor = 2 |
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p = (len(blur_kernel) - factor) + (kernel_size - 1) |
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pad0 = (p + 1) // 2 |
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pad1 = p // 2 |
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layers.append(Blur(blur_kernel, pad=(pad0, pad1))) |
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stride = 2 |
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self.padding = 0 |
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else: |
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stride = 1 |
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self.padding = kernel_size // 2 |
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layers.append( |
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EqualConv2d( |
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in_channel, |
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out_channel, |
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kernel_size, |
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padding=self.padding, |
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stride=stride, |
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bias=bias and not activate, |
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) |
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) |
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if activate: |
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if bias: |
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layers.append(FusedLeakyReLU(out_channel)) |
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else: |
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layers.append(ScaledLeakyReLU(0.2)) |
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super().__init__(*layers) |
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class EqualConv2d(nn.Module): |
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def __init__( |
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self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True |
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): |
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super().__init__() |
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self.weight = nn.Parameter( |
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torch.randn(out_channel, in_channel, kernel_size, kernel_size) |
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) |
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self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2) |
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self.stride = stride |
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self.padding = padding |
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if bias: |
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self.bias = nn.Parameter(torch.zeros(out_channel)) |
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else: |
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self.bias = None |
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def forward(self, input): |
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out = F.conv2d( |
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input, |
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self.weight * self.scale, |
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bias=self.bias, |
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stride=self.stride, |
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padding=self.padding, |
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) |
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return out |
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def __repr__(self): |
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return ( |
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f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},' |
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f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})' |
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) |
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class EqualLinear(nn.Module): |
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def __init__( |
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self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None |
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): |
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super().__init__() |
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self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul)) |
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if bias: |
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self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init)) |
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else: |
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self.bias = None |
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self.activation = activation |
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self.scale = (1 / math.sqrt(in_dim)) * lr_mul |
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self.lr_mul = lr_mul |
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def forward(self, input): |
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if self.activation: |
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out = F.linear(input, self.weight * self.scale) |
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out = fused_leaky_relu(out, self.bias * self.lr_mul) |
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else: |
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out = F.linear( |
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input, self.weight * self.scale, bias=self.bias * self.lr_mul |
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) |
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return out |
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def __repr__(self): |
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return ( |
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f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})' |
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) |
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class ScaledLeakyReLU(nn.Module): |
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def __init__(self, negative_slope=0.2): |
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super().__init__() |
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self.negative_slope = negative_slope |
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def forward(self, input): |
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out = F.leaky_relu(input, negative_slope=self.negative_slope) |
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return out * math.sqrt(2) |
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class Blur(nn.Module): |
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def __init__(self, kernel, pad, upsample_factor=1): |
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super().__init__() |
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kernel = make_kernel(kernel) |
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if upsample_factor > 1: |
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kernel = kernel * (upsample_factor ** 2) |
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self.register_buffer('kernel', kernel) |
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self.pad = pad |
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def forward(self, input): |
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out = upfirdn2d(input, self.kernel, pad=self.pad) |
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return out |
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def make_kernel(k): |
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k = torch.tensor(k, dtype=torch.float32) |
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if k.ndim == 1: |
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k = k[None, :] * k[:, None] |
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k /= k.sum() |
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return k |
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class Upsample(nn.Module): |
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def __init__(self, kernel, factor=2): |
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super().__init__() |
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self.factor = factor |
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kernel = make_kernel(kernel) * (factor ** 2) |
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self.register_buffer('kernel', kernel) |
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p = kernel.shape[0] - factor |
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pad0 = (p + 1) // 2 + factor - 1 |
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pad1 = p // 2 |
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self.pad = (pad0, pad1) |
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def forward(self, input): |
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out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad) |
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return out |
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class Downsample(nn.Module): |
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def __init__(self, kernel, factor=2): |
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super().__init__() |
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self.factor = factor |
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kernel = make_kernel(kernel) |
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self.register_buffer('kernel', kernel) |
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p = kernel.shape[0] - factor |
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pad0 = (p + 1) // 2 |
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pad1 = p // 2 |
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self.pad = (pad0, pad1) |
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def forward(self, input): |
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out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad) |
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return out |