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""" | |
The network architectures is based on PyTorch implemenation of StyleGAN2Encoder. | |
Original PyTorch repo: https://github.com/rosinality/style-based-gan-pytorch | |
Origianl StyelGAN2 paper: https://github.com/NVlabs/stylegan2 | |
Weγuse the network architeture for our single-image traning setting. | |
""" | |
import math | |
import numpy as np | |
import random | |
import torch | |
from torch import nn | |
from torch.nn import functional as F | |
def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5): | |
return F.leaky_relu(input + bias, negative_slope) * scale | |
class FusedLeakyReLU(nn.Module): | |
def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5): | |
super().__init__() | |
self.bias = nn.Parameter(torch.zeros(1, channel, 1, 1)) | |
self.negative_slope = negative_slope | |
self.scale = scale | |
def forward(self, input): | |
# print("FusedLeakyReLU: ", input.abs().mean()) | |
out = fused_leaky_relu(input, self.bias, | |
self.negative_slope, | |
self.scale) | |
# print("FusedLeakyReLU: ", out.abs().mean()) | |
return out | |
def upfirdn2d_native( | |
input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 | |
): | |
_, minor, in_h, in_w = input.shape | |
kernel_h, kernel_w = kernel.shape | |
out = input.view(-1, minor, in_h, 1, in_w, 1) | |
out = F.pad(out, [0, up_x - 1, 0, 0, 0, up_y - 1, 0, 0]) | |
out = out.view(-1, minor, in_h * up_y, in_w * up_x) | |
out = F.pad( | |
out, [max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] | |
) | |
out = out[ | |
:, | |
:, | |
max(-pad_y0, 0): out.shape[2] - max(-pad_y1, 0), | |
max(-pad_x0, 0): out.shape[3] - max(-pad_x1, 0), | |
] | |
# out = out.permute(0, 3, 1, 2) | |
out = out.reshape( | |
[-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1] | |
) | |
w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) | |
out = F.conv2d(out, w) | |
out = out.reshape( | |
-1, | |
minor, | |
in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, | |
in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, | |
) | |
# out = out.permute(0, 2, 3, 1) | |
return out[:, :, ::down_y, ::down_x] | |
def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): | |
return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]) | |
class PixelNorm(nn.Module): | |
def __init__(self): | |
super().__init__() | |
def forward(self, input): | |
return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8) | |
def make_kernel(k): | |
k = torch.tensor(k, dtype=torch.float32) | |
if len(k.shape) == 1: | |
k = k[None, :] * k[:, None] | |
k /= k.sum() | |
return k | |
class Upsample(nn.Module): | |
def __init__(self, kernel, factor=2): | |
super().__init__() | |
self.factor = factor | |
kernel = make_kernel(kernel) * (factor ** 2) | |
self.register_buffer('kernel', kernel) | |
p = kernel.shape[0] - factor | |
pad0 = (p + 1) // 2 + factor - 1 | |
pad1 = p // 2 | |
self.pad = (pad0, pad1) | |
def forward(self, input): | |
out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad) | |
return out | |
class Downsample(nn.Module): | |
def __init__(self, kernel, factor=2): | |
super().__init__() | |
self.factor = factor | |
kernel = make_kernel(kernel) | |
self.register_buffer('kernel', kernel) | |
p = kernel.shape[0] - factor | |
pad0 = (p + 1) // 2 | |
pad1 = p // 2 | |
self.pad = (pad0, pad1) | |
def forward(self, input): | |
out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad) | |
return out | |
class Blur(nn.Module): | |
def __init__(self, kernel, pad, upsample_factor=1): | |
super().__init__() | |
kernel = make_kernel(kernel) | |
if upsample_factor > 1: | |
kernel = kernel * (upsample_factor ** 2) | |
self.register_buffer('kernel', kernel) | |
self.pad = pad | |
def forward(self, input): | |
out = upfirdn2d(input, self.kernel, pad=self.pad) | |
return out | |
class EqualConv2d(nn.Module): | |
def __init__( | |
self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True | |
): | |
super().__init__() | |
self.weight = nn.Parameter( | |
torch.randn(out_channel, in_channel, kernel_size, kernel_size) | |
) | |
self.scale = math.sqrt(1) / math.sqrt(in_channel * (kernel_size ** 2)) | |
self.stride = stride | |
self.padding = padding | |
if bias: | |
self.bias = nn.Parameter(torch.zeros(out_channel)) | |
else: | |
self.bias = None | |
def forward(self, input): | |
# print("Before EqualConv2d: ", input.abs().mean()) | |
out = F.conv2d( | |
input, | |
self.weight * self.scale, | |
bias=self.bias, | |
stride=self.stride, | |
padding=self.padding, | |
) | |
# print("After EqualConv2d: ", out.abs().mean(), (self.weight * self.scale).abs().mean()) | |
return out | |
def __repr__(self): | |
return ( | |
f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},' | |
f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})' | |
) | |
class EqualLinear(nn.Module): | |
def __init__( | |
self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None | |
): | |
super().__init__() | |
self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul)) | |
if bias: | |
self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init)) | |
else: | |
self.bias = None | |
self.activation = activation | |
self.scale = (math.sqrt(1) / math.sqrt(in_dim)) * lr_mul | |
self.lr_mul = lr_mul | |
def forward(self, input): | |
if self.activation: | |
out = F.linear(input, self.weight * self.scale) | |
out = fused_leaky_relu(out, self.bias * self.lr_mul) | |
else: | |
out = F.linear( | |
input, self.weight * self.scale, bias=self.bias * self.lr_mul | |
) | |
return out | |
def __repr__(self): | |
return ( | |
f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})' | |
) | |
class ScaledLeakyReLU(nn.Module): | |
def __init__(self, negative_slope=0.2): | |
super().__init__() | |
self.negative_slope = negative_slope | |
def forward(self, input): | |
out = F.leaky_relu(input, negative_slope=self.negative_slope) | |
return out * math.sqrt(2) | |
class ModulatedConv2d(nn.Module): | |
def __init__( | |
self, | |
in_channel, | |
out_channel, | |
kernel_size, | |
style_dim, | |
demodulate=True, | |
upsample=False, | |
downsample=False, | |
blur_kernel=[1, 3, 3, 1], | |
): | |
super().__init__() | |
self.eps = 1e-8 | |
self.kernel_size = kernel_size | |
self.in_channel = in_channel | |
self.out_channel = out_channel | |
self.upsample = upsample | |
self.downsample = downsample | |
if upsample: | |
factor = 2 | |
p = (len(blur_kernel) - factor) - (kernel_size - 1) | |
pad0 = (p + 1) // 2 + factor - 1 | |
pad1 = p // 2 + 1 | |
self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor) | |
if downsample: | |
factor = 2 | |
p = (len(blur_kernel) - factor) + (kernel_size - 1) | |
pad0 = (p + 1) // 2 | |
pad1 = p // 2 | |
self.blur = Blur(blur_kernel, pad=(pad0, pad1)) | |
fan_in = in_channel * kernel_size ** 2 | |
self.scale = math.sqrt(1) / math.sqrt(fan_in) | |
self.padding = kernel_size // 2 | |
self.weight = nn.Parameter( | |
torch.randn(1, out_channel, in_channel, kernel_size, kernel_size) | |
) | |
if style_dim is not None and style_dim > 0: | |
self.modulation = EqualLinear(style_dim, in_channel, bias_init=1) | |
self.demodulate = demodulate | |
def __repr__(self): | |
return ( | |
f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, ' | |
f'upsample={self.upsample}, downsample={self.downsample})' | |
) | |
def forward(self, input, style): | |
batch, in_channel, height, width = input.shape | |
if style is not None: | |
style = self.modulation(style).view(batch, 1, in_channel, 1, 1) | |
else: | |
style = torch.ones(batch, 1, in_channel, 1, 1).cuda() | |
weight = self.scale * self.weight * style | |
if self.demodulate: | |
demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8) | |
weight = weight * demod.view(batch, self.out_channel, 1, 1, 1) | |
weight = weight.view( | |
batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size | |
) | |
if self.upsample: | |
input = input.view(1, batch * in_channel, height, width) | |
weight = weight.view( | |
batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size | |
) | |
weight = weight.transpose(1, 2).reshape( | |
batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size | |
) | |
out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch) | |
_, _, height, width = out.shape | |
out = out.view(batch, self.out_channel, height, width) | |
out = self.blur(out) | |
elif self.downsample: | |
input = self.blur(input) | |
_, _, height, width = input.shape | |
input = input.view(1, batch * in_channel, height, width) | |
out = F.conv2d(input, weight, padding=0, stride=2, groups=batch) | |
_, _, height, width = out.shape | |
out = out.view(batch, self.out_channel, height, width) | |
else: | |
input = input.view(1, batch * in_channel, height, width) | |
out = F.conv2d(input, weight, padding=self.padding, groups=batch) | |
_, _, height, width = out.shape | |
out = out.view(batch, self.out_channel, height, width) | |
return out | |
class NoiseInjection(nn.Module): | |
def __init__(self): | |
super().__init__() | |
self.weight = nn.Parameter(torch.zeros(1)) | |
def forward(self, image, noise=None): | |
if noise is None: | |
batch, _, height, width = image.shape | |
noise = image.new_empty(batch, 1, height, width).normal_() | |
return image + self.weight * noise | |
class ConstantInput(nn.Module): | |
def __init__(self, channel, size=4): | |
super().__init__() | |
self.input = nn.Parameter(torch.randn(1, channel, size, size)) | |
def forward(self, input): | |
batch = input.shape[0] | |
out = self.input.repeat(batch, 1, 1, 1) | |
return out | |
class StyledConv(nn.Module): | |
def __init__( | |
self, | |
in_channel, | |
out_channel, | |
kernel_size, | |
style_dim=None, | |
upsample=False, | |
blur_kernel=[1, 3, 3, 1], | |
demodulate=True, | |
inject_noise=True, | |
): | |
super().__init__() | |
self.inject_noise = inject_noise | |
self.conv = ModulatedConv2d( | |
in_channel, | |
out_channel, | |
kernel_size, | |
style_dim, | |
upsample=upsample, | |
blur_kernel=blur_kernel, | |
demodulate=demodulate, | |
) | |
self.noise = NoiseInjection() | |
# self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1)) | |
# self.activate = ScaledLeakyReLU(0.2) | |
self.activate = FusedLeakyReLU(out_channel) | |
def forward(self, input, style=None, noise=None): | |
out = self.conv(input, style) | |
if self.inject_noise: | |
out = self.noise(out, noise=noise) | |
# out = out + self.bias | |
out = self.activate(out) | |
return out | |
class ToRGB(nn.Module): | |
def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]): | |
super().__init__() | |
if upsample: | |
self.upsample = Upsample(blur_kernel) | |
self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False) | |
self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) | |
def forward(self, input, style, skip=None): | |
out = self.conv(input, style) | |
out = out + self.bias | |
if skip is not None: | |
skip = self.upsample(skip) | |
out = out + skip | |
return out | |
class Generator(nn.Module): | |
def __init__( | |
self, | |
size, | |
style_dim, | |
n_mlp, | |
channel_multiplier=2, | |
blur_kernel=[1, 3, 3, 1], | |
lr_mlp=0.01, | |
): | |
super().__init__() | |
self.size = size | |
self.style_dim = style_dim | |
layers = [PixelNorm()] | |
for i in range(n_mlp): | |
layers.append( | |
EqualLinear( | |
style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu' | |
) | |
) | |
self.style = nn.Sequential(*layers) | |
self.channels = { | |
4: 512, | |
8: 512, | |
16: 512, | |
32: 512, | |
64: 256 * channel_multiplier, | |
128: 128 * channel_multiplier, | |
256: 64 * channel_multiplier, | |
512: 32 * channel_multiplier, | |
1024: 16 * channel_multiplier, | |
} | |
self.input = ConstantInput(self.channels[4]) | |
self.conv1 = StyledConv( | |
self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel | |
) | |
self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False) | |
self.log_size = int(math.log(size, 2)) | |
self.num_layers = (self.log_size - 2) * 2 + 1 | |
self.convs = nn.ModuleList() | |
self.upsamples = nn.ModuleList() | |
self.to_rgbs = nn.ModuleList() | |
self.noises = nn.Module() | |
in_channel = self.channels[4] | |
for layer_idx in range(self.num_layers): | |
res = (layer_idx + 5) // 2 | |
shape = [1, 1, 2 ** res, 2 ** res] | |
self.noises.register_buffer(f'noise_{layer_idx}', torch.randn(*shape)) | |
for i in range(3, self.log_size + 1): | |
out_channel = self.channels[2 ** i] | |
self.convs.append( | |
StyledConv( | |
in_channel, | |
out_channel, | |
3, | |
style_dim, | |
upsample=True, | |
blur_kernel=blur_kernel, | |
) | |
) | |
self.convs.append( | |
StyledConv( | |
out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel | |
) | |
) | |
self.to_rgbs.append(ToRGB(out_channel, style_dim)) | |
in_channel = out_channel | |
self.n_latent = self.log_size * 2 - 2 | |
def make_noise(self): | |
device = self.input.input.device | |
noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)] | |
for i in range(3, self.log_size + 1): | |
for _ in range(2): | |
noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device)) | |
return noises | |
def mean_latent(self, n_latent): | |
latent_in = torch.randn( | |
n_latent, self.style_dim, device=self.input.input.device | |
) | |
latent = self.style(latent_in).mean(0, keepdim=True) | |
return latent | |
def get_latent(self, input): | |
return self.style(input) | |
def forward( | |
self, | |
styles, | |
return_latents=False, | |
inject_index=None, | |
truncation=1, | |
truncation_latent=None, | |
input_is_latent=False, | |
noise=None, | |
randomize_noise=True, | |
): | |
if not input_is_latent: | |
styles = [self.style(s) for s in styles] | |
if noise is None: | |
if randomize_noise: | |
noise = [None] * self.num_layers | |
else: | |
noise = [ | |
getattr(self.noises, f'noise_{i}') for i in range(self.num_layers) | |
] | |
if truncation < 1: | |
style_t = [] | |
for style in styles: | |
style_t.append( | |
truncation_latent + truncation * (style - truncation_latent) | |
) | |
styles = style_t | |
if len(styles) < 2: | |
inject_index = self.n_latent | |
if 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 | |