915 lines
27 KiB
Python
915 lines
27 KiB
Python
"""
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The network architectures is based on PyTorch implemenation of StyleGAN2Encoder.
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Original PyTorch repo: https://github.com/rosinality/style-based-gan-pytorch
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Origianl StyelGAN2 paper: https://github.com/NVlabs/stylegan2
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We use the network architeture for our single-image traning setting.
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"""
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import math
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import numpy as np
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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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def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
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return F.leaky_relu(input + bias, negative_slope) * scale
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class FusedLeakyReLU(nn.Module):
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def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
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super().__init__()
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self.bias = nn.Parameter(torch.zeros(1, channel, 1, 1))
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self.negative_slope = negative_slope
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self.scale = scale
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def forward(self, input):
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# print("FusedLeakyReLU: ", input.abs().mean())
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out = fused_leaky_relu(input, self.bias,
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self.negative_slope,
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self.scale)
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# print("FusedLeakyReLU: ", out.abs().mean())
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return out
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def upfirdn2d_native(
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input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
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):
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_, minor, in_h, in_w = input.shape
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kernel_h, kernel_w = kernel.shape
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out = input.view(-1, minor, in_h, 1, in_w, 1)
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out = F.pad(out, [0, up_x - 1, 0, 0, 0, up_y - 1, 0, 0])
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out = out.view(-1, minor, in_h * up_y, in_w * up_x)
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out = F.pad(
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out, [max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
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)
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out = out[
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:,
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:,
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max(-pad_y0, 0): out.shape[2] - max(-pad_y1, 0),
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max(-pad_x0, 0): out.shape[3] - max(-pad_x1, 0),
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]
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# out = out.permute(0, 3, 1, 2)
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out = out.reshape(
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[-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
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)
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w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
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out = F.conv2d(out, w)
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out = out.reshape(
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-1,
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minor,
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in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
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in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
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)
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# out = out.permute(0, 2, 3, 1)
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return out[:, :, ::down_y, ::down_x]
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def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
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return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
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class PixelNorm(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, input):
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return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
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def make_kernel(k):
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k = torch.tensor(k, dtype=torch.float32)
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if len(k.shape) == 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
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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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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 = math.sqrt(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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# print("Before EqualConv2d: ", input.abs().mean())
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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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# print("After EqualConv2d: ", out.abs().mean(), (self.weight * self.scale).abs().mean())
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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 = (math.sqrt(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 ModulatedConv2d(nn.Module):
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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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style_dim,
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demodulate=True,
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upsample=False,
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downsample=False,
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blur_kernel=[1, 3, 3, 1],
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):
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super().__init__()
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self.eps = 1e-8
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self.kernel_size = kernel_size
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self.in_channel = in_channel
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self.out_channel = out_channel
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self.upsample = upsample
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self.downsample = downsample
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if upsample:
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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 + factor - 1
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pad1 = p // 2 + 1
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self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor)
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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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self.blur = Blur(blur_kernel, pad=(pad0, pad1))
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fan_in = in_channel * kernel_size ** 2
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self.scale = math.sqrt(1) / math.sqrt(fan_in)
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self.padding = kernel_size // 2
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self.weight = nn.Parameter(
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torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
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)
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if style_dim is not None and style_dim > 0:
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self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
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self.demodulate = demodulate
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def __repr__(self):
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return (
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f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, '
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f'upsample={self.upsample}, downsample={self.downsample})'
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)
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def forward(self, input, style):
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batch, in_channel, height, width = input.shape
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if style is not None:
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style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
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else:
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style = torch.ones(batch, 1, in_channel, 1, 1).cuda()
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weight = self.scale * self.weight * style
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if self.demodulate:
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demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
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weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
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weight = weight.view(
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batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
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)
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if self.upsample:
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input = input.view(1, batch * in_channel, height, width)
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weight = weight.view(
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batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size
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)
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weight = weight.transpose(1, 2).reshape(
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batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size
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)
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out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch)
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_, _, height, width = out.shape
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out = out.view(batch, self.out_channel, height, width)
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out = self.blur(out)
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elif self.downsample:
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input = self.blur(input)
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_, _, height, width = input.shape
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input = input.view(1, batch * in_channel, height, width)
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out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
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_, _, height, width = out.shape
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out = out.view(batch, self.out_channel, height, width)
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else:
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input = input.view(1, batch * in_channel, height, width)
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out = F.conv2d(input, weight, padding=self.padding, groups=batch)
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_, _, height, width = out.shape
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out = out.view(batch, self.out_channel, height, width)
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return out
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class NoiseInjection(nn.Module):
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def __init__(self):
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super().__init__()
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self.weight = nn.Parameter(torch.zeros(1))
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def forward(self, image, noise=None):
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if noise is None:
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batch, _, height, width = image.shape
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noise = image.new_empty(batch, 1, height, width).normal_()
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return image + self.weight * noise
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class ConstantInput(nn.Module):
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def __init__(self, channel, size=4):
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super().__init__()
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self.input = nn.Parameter(torch.randn(1, channel, size, size))
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def forward(self, input):
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batch = input.shape[0]
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out = self.input.repeat(batch, 1, 1, 1)
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return out
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class StyledConv(nn.Module):
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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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style_dim=None,
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upsample=False,
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blur_kernel=[1, 3, 3, 1],
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demodulate=True,
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inject_noise=True,
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):
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super().__init__()
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self.inject_noise = inject_noise
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self.conv = ModulatedConv2d(
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in_channel,
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out_channel,
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kernel_size,
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style_dim,
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upsample=upsample,
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blur_kernel=blur_kernel,
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demodulate=demodulate,
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)
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self.noise = NoiseInjection()
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# self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
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# self.activate = ScaledLeakyReLU(0.2)
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self.activate = FusedLeakyReLU(out_channel)
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def forward(self, input, style=None, noise=None):
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out = self.conv(input, style)
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if self.inject_noise:
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out = self.noise(out, noise=noise)
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# out = out + self.bias
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out = self.activate(out)
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return out
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class ToRGB(nn.Module):
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def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]):
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super().__init__()
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if upsample:
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self.upsample = Upsample(blur_kernel)
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self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False)
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self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
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def forward(self, input, style, skip=None):
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out = self.conv(input, style)
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out = out + self.bias
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if skip is not None:
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skip = self.upsample(skip)
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out = out + skip
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return out
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class Generator(nn.Module):
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def __init__(
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self,
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size,
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style_dim,
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n_mlp,
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channel_multiplier=2,
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blur_kernel=[1, 3, 3, 1],
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lr_mlp=0.01,
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):
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super().__init__()
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self.size = size
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self.style_dim = style_dim
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layers = [PixelNorm()]
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for i in range(n_mlp):
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layers.append(
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EqualLinear(
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style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu'
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)
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)
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self.style = nn.Sequential(*layers)
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self.channels = {
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4: 512,
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8: 512,
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16: 512,
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32: 512,
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64: 256 * channel_multiplier,
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128: 128 * channel_multiplier,
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256: 64 * channel_multiplier,
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512: 32 * channel_multiplier,
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1024: 16 * channel_multiplier,
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}
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self.input = ConstantInput(self.channels[4])
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self.conv1 = StyledConv(
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self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel
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)
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self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False)
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self.log_size = int(math.log(size, 2))
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self.num_layers = (self.log_size - 2) * 2 + 1
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self.convs = nn.ModuleList()
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self.upsamples = nn.ModuleList()
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self.to_rgbs = nn.ModuleList()
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self.noises = nn.Module()
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in_channel = self.channels[4]
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for layer_idx in range(self.num_layers):
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res = (layer_idx + 5) // 2
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shape = [1, 1, 2 ** res, 2 ** res]
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self.noises.register_buffer(f'noise_{layer_idx}', torch.randn(*shape))
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for i in range(3, self.log_size + 1):
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out_channel = self.channels[2 ** i]
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self.convs.append(
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StyledConv(
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in_channel,
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out_channel,
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3,
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style_dim,
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upsample=True,
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blur_kernel=blur_kernel,
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)
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)
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self.convs.append(
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StyledConv(
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out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel
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)
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)
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self.to_rgbs.append(ToRGB(out_channel, style_dim))
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in_channel = out_channel
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self.n_latent = self.log_size * 2 - 2
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def make_noise(self):
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device = self.input.input.device
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noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)]
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for i in range(3, self.log_size + 1):
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for _ in range(2):
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noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device))
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return noises
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|
||
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 len(styles[0].shape) < 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
|
||
|
||
if return_latents:
|
||
return image, latent
|
||
|
||
else:
|
||
return image, None
|
||
|
||
|
||
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 ResBlock(nn.Module):
|
||
def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1], downsample=True, skip_gain=1.0):
|
||
super().__init__()
|
||
|
||
self.skip_gain = skip_gain
|
||
self.conv1 = ConvLayer(in_channel, in_channel, 3)
|
||
self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=downsample, blur_kernel=blur_kernel)
|
||
|
||
if in_channel != out_channel or downsample:
|
||
self.skip = ConvLayer(
|
||
in_channel, out_channel, 1, downsample=downsample, activate=False, bias=False
|
||
)
|
||
else:
|
||
self.skip = nn.Identity()
|
||
|
||
def forward(self, input):
|
||
out = self.conv1(input)
|
||
out = self.conv2(out)
|
||
|
||
skip = self.skip(input)
|
||
out = (out * self.skip_gain + skip) / math.sqrt(self.skip_gain ** 2 + 1.0)
|
||
|
||
return out
|
||
|
||
|
||
class StyleGAN2Discriminator(nn.Module):
|
||
def __init__(self, input_nc, ndf=64, n_layers=3, no_antialias=False, size=None, opt=None):
|
||
super().__init__()
|
||
self.opt = opt
|
||
self.stddev_group = 16
|
||
if size is None:
|
||
size = 2 ** int((np.rint(np.log2(min(opt.load_size, opt.crop_size)))))
|
||
if "patch" in self.opt.netD and self.opt.D_patch_size is not None:
|
||
size = 2 ** int(np.log2(self.opt.D_patch_size))
|
||
|
||
blur_kernel = [1, 3, 3, 1]
|
||
channel_multiplier = ndf / 64
|
||
channels = {
|
||
4: min(384, int(4096 * channel_multiplier)),
|
||
8: min(384, int(2048 * channel_multiplier)),
|
||
16: min(384, int(1024 * channel_multiplier)),
|
||
32: min(384, int(512 * channel_multiplier)),
|
||
64: int(256 * channel_multiplier),
|
||
128: int(128 * channel_multiplier),
|
||
256: int(64 * channel_multiplier),
|
||
512: int(32 * channel_multiplier),
|
||
1024: int(16 * channel_multiplier),
|
||
}
|
||
|
||
convs = [ConvLayer(3, channels[size], 1)]
|
||
|
||
log_size = int(math.log(size, 2))
|
||
|
||
in_channel = channels[size]
|
||
|
||
if "smallpatch" in self.opt.netD:
|
||
final_res_log2 = 4
|
||
elif "patch" in self.opt.netD:
|
||
final_res_log2 = 3
|
||
else:
|
||
final_res_log2 = 2
|
||
|
||
for i in range(log_size, final_res_log2, -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)
|
||
|
||
if False and "tile" in self.opt.netD:
|
||
in_channel += 1
|
||
self.final_conv = ConvLayer(in_channel, channels[4], 3)
|
||
if "patch" in self.opt.netD:
|
||
self.final_linear = ConvLayer(channels[4], 1, 3, bias=False, activate=False)
|
||
else:
|
||
self.final_linear = nn.Sequential(
|
||
EqualLinear(channels[4] * 4 * 4, channels[4], activation='fused_lrelu'),
|
||
EqualLinear(channels[4], 1),
|
||
)
|
||
|
||
def forward(self, input, get_minibatch_features=False):
|
||
if "patch" in self.opt.netD and self.opt.D_patch_size is not None:
|
||
h, w = input.size(2), input.size(3)
|
||
y = torch.randint(h - self.opt.D_patch_size, ())
|
||
x = torch.randint(w - self.opt.D_patch_size, ())
|
||
input = input[:, :, y:y + self.opt.D_patch_size, x:x + self.opt.D_patch_size]
|
||
out = input
|
||
for i, conv in enumerate(self.convs):
|
||
out = conv(out)
|
||
# print(i, out.abs().mean())
|
||
# out = self.convs(input)
|
||
|
||
batch, channel, height, width = out.shape
|
||
|
||
if False and "tile" in self.opt.netD:
|
||
group = min(batch, self.stddev_group)
|
||
stddev = out.view(
|
||
group, -1, 1, channel // 1, height, width
|
||
)
|
||
stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
|
||
stddev = stddev.mean([2, 3, 4], keepdim=True).squeeze(2)
|
||
stddev = stddev.repeat(group, 1, height, width)
|
||
out = torch.cat([out, stddev], 1)
|
||
|
||
out = self.final_conv(out)
|
||
# print(out.abs().mean())
|
||
|
||
if "patch" not in self.opt.netD:
|
||
out = out.view(batch, -1)
|
||
out = self.final_linear(out)
|
||
|
||
return out
|
||
|
||
|
||
class TileStyleGAN2Discriminator(StyleGAN2Discriminator):
|
||
def forward(self, input):
|
||
B, C, H, W = input.size(0), input.size(1), input.size(2), input.size(3)
|
||
size = self.opt.D_patch_size
|
||
Y = H // size
|
||
X = W // size
|
||
input = input.view(B, C, Y, size, X, size)
|
||
input = input.permute(0, 2, 4, 1, 3, 5).contiguous().view(B * Y * X, C, size, size)
|
||
return super().forward(input)
|
||
|
||
|
||
class StyleGAN2Encoder(nn.Module):
|
||
def __init__(self, input_nc, output_nc, ngf=64, use_dropout=False, n_blocks=6, padding_type='reflect', no_antialias=False, opt=None):
|
||
super().__init__()
|
||
assert opt is not None
|
||
self.opt = opt
|
||
channel_multiplier = ngf / 32
|
||
channels = {
|
||
4: min(512, int(round(4096 * channel_multiplier))),
|
||
8: min(512, int(round(2048 * channel_multiplier))),
|
||
16: min(512, int(round(1024 * channel_multiplier))),
|
||
32: min(512, int(round(512 * channel_multiplier))),
|
||
64: int(round(256 * channel_multiplier)),
|
||
128: int(round(128 * channel_multiplier)),
|
||
256: int(round(64 * channel_multiplier)),
|
||
512: int(round(32 * channel_multiplier)),
|
||
1024: int(round(16 * channel_multiplier)),
|
||
}
|
||
|
||
blur_kernel = [1, 3, 3, 1]
|
||
|
||
cur_res = 2 ** int((np.rint(np.log2(min(opt.load_size, opt.crop_size)))))
|
||
convs = [nn.Identity(),
|
||
ConvLayer(3, channels[cur_res], 1)]
|
||
|
||
num_downsampling = self.opt.stylegan2_G_num_downsampling
|
||
for i in range(num_downsampling):
|
||
in_channel = channels[cur_res]
|
||
out_channel = channels[cur_res // 2]
|
||
convs.append(ResBlock(in_channel, out_channel, blur_kernel, downsample=True))
|
||
cur_res = cur_res // 2
|
||
|
||
for i in range(n_blocks // 2):
|
||
n_channel = channels[cur_res]
|
||
convs.append(ResBlock(n_channel, n_channel, downsample=False))
|
||
|
||
self.convs = nn.Sequential(*convs)
|
||
|
||
def forward(self, input, layers=[], get_features=False):
|
||
feat = input
|
||
feats = []
|
||
if -1 in layers:
|
||
layers.append(len(self.convs) - 1)
|
||
for layer_id, layer in enumerate(self.convs):
|
||
feat = layer(feat)
|
||
# print(layer_id, " features ", feat.abs().mean())
|
||
if layer_id in layers:
|
||
feats.append(feat)
|
||
|
||
if get_features:
|
||
return feat, feats
|
||
else:
|
||
return feat
|
||
|
||
|
||
class StyleGAN2Decoder(nn.Module):
|
||
def __init__(self, input_nc, output_nc, ngf=64, use_dropout=False, n_blocks=6, padding_type='reflect', no_antialias=False, opt=None):
|
||
super().__init__()
|
||
assert opt is not None
|
||
self.opt = opt
|
||
|
||
blur_kernel = [1, 3, 3, 1]
|
||
|
||
channel_multiplier = ngf / 32
|
||
channels = {
|
||
4: min(512, int(round(4096 * channel_multiplier))),
|
||
8: min(512, int(round(2048 * channel_multiplier))),
|
||
16: min(512, int(round(1024 * channel_multiplier))),
|
||
32: min(512, int(round(512 * channel_multiplier))),
|
||
64: int(round(256 * channel_multiplier)),
|
||
128: int(round(128 * channel_multiplier)),
|
||
256: int(round(64 * channel_multiplier)),
|
||
512: int(round(32 * channel_multiplier)),
|
||
1024: int(round(16 * channel_multiplier)),
|
||
}
|
||
|
||
num_downsampling = self.opt.stylegan2_G_num_downsampling
|
||
cur_res = 2 ** int((np.rint(np.log2(min(opt.load_size, opt.crop_size))))) // (2 ** num_downsampling)
|
||
convs = []
|
||
|
||
for i in range(n_blocks // 2):
|
||
n_channel = channels[cur_res]
|
||
convs.append(ResBlock(n_channel, n_channel, downsample=False))
|
||
|
||
for i in range(num_downsampling):
|
||
in_channel = channels[cur_res]
|
||
out_channel = channels[cur_res * 2]
|
||
inject_noise = "small" not in self.opt.netG
|
||
convs.append(
|
||
StyledConv(in_channel, out_channel, 3, upsample=True, blur_kernel=blur_kernel, inject_noise=inject_noise)
|
||
)
|
||
cur_res = cur_res * 2
|
||
|
||
convs.append(ConvLayer(channels[cur_res], 3, 1))
|
||
|
||
self.convs = nn.Sequential(*convs)
|
||
|
||
def forward(self, input):
|
||
return self.convs(input)
|
||
|
||
|
||
class StyleGAN2Generator(nn.Module):
|
||
def __init__(self, input_nc, output_nc, ngf=64, use_dropout=False, n_blocks=6, padding_type='reflect', no_antialias=False, opt=None):
|
||
super().__init__()
|
||
self.opt = opt
|
||
self.encoder = StyleGAN2Encoder(input_nc, output_nc, ngf, use_dropout, n_blocks, padding_type, no_antialias, opt)
|
||
self.decoder = StyleGAN2Decoder(input_nc, output_nc, ngf, use_dropout, n_blocks, padding_type, no_antialias, opt)
|
||
|
||
def forward(self, input, layers=[], encode_only=False):
|
||
feat, feats = self.encoder(input, layers, True)
|
||
if encode_only:
|
||
return feats
|
||
else:
|
||
fake = self.decoder(feat)
|
||
|
||
if len(layers) > 0:
|
||
return fake, feats
|
||
else:
|
||
return fake
|