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import random
import torch
from torch import nn
from model.stylegan.model import ConvLayer, PixelNorm, EqualLinear, Generator
class AdaptiveInstanceNorm(nn.Module):
def __init__(self, fin, style_dim=512):
super().__init__()
self.norm = nn.InstanceNorm2d(fin, affine=False)
self.style = nn.Linear(style_dim, fin * 2)
self.style.bias.data[:fin] = 1
self.style.bias.data[fin:] = 0
def forward(self, input, style):
style = self.style(style).unsqueeze(2).unsqueeze(3)
gamma, beta = style.chunk(2, 1)
out = self.norm(input)
out = gamma * out + beta
return out
# modulative residual blocks (ModRes)
class AdaResBlock(nn.Module):
def __init__(self, fin, style_dim=512, dilation=1): # modified
super().__init__()
self.conv = ConvLayer(fin, fin, 3, dilation=dilation) # modified
self.conv2 = ConvLayer(fin, fin, 3, dilation=dilation) # modified
self.norm = AdaptiveInstanceNorm(fin, style_dim)
self.norm2 = AdaptiveInstanceNorm(fin, style_dim)
# model initialization
# the convolution filters are set to values close to 0 to produce negligible residual features
self.conv[0].weight.data *= 0.01
self.conv2[0].weight.data *= 0.01
def forward(self, x, s, w=1):
skip = x
if w == 0:
return skip
out = self.conv(self.norm(x, s))
out = self.conv2(self.norm2(out, s))
out = out * w + skip
return out
class DualStyleGAN(nn.Module):
def __init__(self, size, style_dim, n_mlp, channel_multiplier=2, twoRes=True, res_index=6):
super().__init__()
layers = [PixelNorm()]
for i in range(n_mlp-6):
layers.append(EqualLinear(512, 512, lr_mul=0.01, activation="fused_lrelu"))
# color transform blocks T_c
self.style = nn.Sequential(*layers)
# StyleGAN2
self.generator = Generator(size, style_dim, n_mlp, channel_multiplier)
# The extrinsic style path
self.res = nn.ModuleList()
self.res_index = res_index//2 * 2
self.res.append(AdaResBlock(self.generator.channels[2 ** 2])) # for conv1
for i in range(3, self.generator.log_size + 1):
out_channel = self.generator.channels[2 ** i]
if i < 3 + self.res_index//2:
# ModRes
self.res.append(AdaResBlock(out_channel))
self.res.append(AdaResBlock(out_channel))
else:
# structure transform block T_s
self.res.append(EqualLinear(512, 512))
# FC layer is initialized with identity matrices, meaning no changes to the input latent code
self.res[-1].weight.data = torch.eye(512) * 512.0**0.5 + torch.randn(512, 512) * 0.01
self.res.append(EqualLinear(512, 512))
self.res[-1].weight.data = torch.eye(512) * 512.0**0.5 + torch.randn(512, 512) * 0.01
self.res.append(EqualLinear(512, 512)) # for to_rgb7
self.res[-1].weight.data = torch.eye(512) * 512.0**0.5 + torch.randn(512, 512) * 0.01
self.size = self.generator.size
self.style_dim = self.generator.style_dim
self.log_size = self.generator.log_size
self.num_layers = self.generator.num_layers
self.n_latent = self.generator.n_latent
self.channels = self.generator.channels
def forward(
self,
styles, # intrinsic style code
exstyles, # extrinsic style code
return_latents=False,
return_feat=False,
inject_index=None,
truncation=1,
truncation_latent=None,
input_is_latent=False,
noise=None,
randomize_noise=True,
z_plus_latent=False, # intrinsic style code is z+ or z
use_res=True, # whether to use the extrinsic style path
fuse_index=18, # layers > fuse_index do not use the extrinsic style path
interp_weights=[1]*18, # weight vector for style combination of two paths
):
if not input_is_latent:
if not z_plus_latent:
styles = [self.generator.style(s) for s in styles]
else:
styles = [self.generator.style(s.reshape(s.shape[0]*s.shape[1], s.shape[2])).reshape(s.shape) for s in styles]
if noise is None:
if randomize_noise:
noise = [None] * self.generator.num_layers
else:
noise = [
getattr(self.generator.noises, f"noise_{i}") for i in range(self.generator.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.generator.n_latent
if styles[0].ndim < 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.generator.n_latent - 1)
if styles[0].ndim < 3:
latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
latent2 = styles[1].unsqueeze(1).repeat(1, self.generator.n_latent - inject_index, 1)
latent = torch.cat([latent, latent2], 1)
else:
latent = torch.cat([styles[0][:,0:inject_index], styles[1][:,inject_index:]], 1)
if use_res:
if exstyles.ndim < 3:
resstyles = self.style(exstyles).unsqueeze(1).repeat(1, self.generator.n_latent, 1)
adastyles = exstyles.unsqueeze(1).repeat(1, self.generator.n_latent, 1)
else:
nB, nL, nD = exstyles.shape
resstyles = self.style(exstyles.reshape(nB*nL, nD)).reshape(nB, nL, nD)
adastyles = exstyles
out = self.generator.input(latent)
out = self.generator.conv1(out, latent[:, 0], noise=noise[0])
if use_res and fuse_index > 0:
out = self.res[0](out, resstyles[:, 0], interp_weights[0])
skip = self.generator.to_rgb1(out, latent[:, 1])
i = 1
for conv1, conv2, noise1, noise2, to_rgb in zip(
self.generator.convs[::2], self.generator.convs[1::2], noise[1::2], noise[2::2], self.generator.to_rgbs):
if use_res and fuse_index >= i and i > self.res_index:
out = conv1(out, interp_weights[i] * self.res[i](adastyles[:, i]) +
(1-interp_weights[i]) * latent[:, i], noise=noise1)
else:
out = conv1(out, latent[:, i], noise=noise1)
if use_res and fuse_index >= i and i <= self.res_index:
out = self.res[i](out, resstyles[:, i], interp_weights[i])
if use_res and fuse_index >= (i+1) and i > self.res_index:
out = conv2(out, interp_weights[i+1] * self.res[i+1](adastyles[:, i+1]) +
(1-interp_weights[i+1]) * latent[:, i+1], noise=noise2)
else:
out = conv2(out, latent[:, i + 1], noise=noise2)
if use_res and fuse_index >= (i+1) and i <= self.res_index:
out = self.res[i+1](out, resstyles[:, i+1], interp_weights[i+1])
if use_res and fuse_index >= (i+2) and i >= self.res_index-1:
skip = to_rgb(out, interp_weights[i+2] * self.res[i+2](adastyles[:, i+2]) +
(1-interp_weights[i+2]) * latent[:, i + 2], skip)
else:
skip = to_rgb(out, latent[:, i + 2], skip)
i += 2
if i > self.res_index and return_feat:
return out, skip
image = skip
if return_latents:
return image, latent
else:
return image, None
def make_noise(self):
return self.generator.make_noise()
def mean_latent(self, n_latent):
return self.generator.mean_latent(n_latent)
def get_latent(self, input):
return self.generator.style(input) |