from random import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.arch_util import kaiming_init
from models.styled_sr.stylegan2_base import StyleVectorizer, GeneratorBlock
from models.styled_sr.transfer_primitives import TransferConvGnLelu, TransferConv2d, TransferLinear
from trainer.networks import register_model
from utils.util import checkpoint, opt_get
def rrdb_init_weights(module, scale=1):
for m in module.modules():
if isinstance(m, TransferConv2d):
kaiming_init(m, a=0, mode='fan_in', bias=0)
m.weight.data *= scale
elif isinstance(m, TransferLinear):
kaiming_init(m, a=0, mode='fan_in', bias=0)
m.weight.data *= scale
class EncoderRRDB(nn.Module):
def __init__(self, mid_channels=64, output_channels=32, growth_channels=32, init_weight=.1, transfer_mode=False):
super(EncoderRRDB, self).__init__()
for i in range(5):
out_channels = output_channels if i == 4 else growth_channels
self.add_module(
f'conv{i+1}',
TransferConv2d(mid_channels + i * growth_channels, out_channels, 3, 1, 1, transfer_mode=transfer_mode))
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
for i in range(5):
rrdb_init_weights(getattr(self, f'conv{i+1}'), init_weight)
def forward(self, x):
x1 = self.lrelu(self.conv1(x))
x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
return x5
class StyledSrEncoder(nn.Module):
def __init__(self, fea_out=256, initial_stride=1, transfer_mode=False):
super().__init__()
# Current assumes fea_out=256.
self.initial_conv = TransferConvGnLelu(3, 32, kernel_size=7, stride=initial_stride, norm=False, activation=False, bias=True, transfer_mode=transfer_mode)
self.rrdbs = nn.ModuleList([
EncoderRRDB(32, transfer_mode=transfer_mode),
EncoderRRDB(64, transfer_mode=transfer_mode),
EncoderRRDB(96, transfer_mode=transfer_mode),
EncoderRRDB(128, transfer_mode=transfer_mode),
EncoderRRDB(160, transfer_mode=transfer_mode),
EncoderRRDB(192, transfer_mode=transfer_mode),
EncoderRRDB(224, transfer_mode=transfer_mode)])
def forward(self, x):
fea = self.initial_conv(x)
for rrdb in self.rrdbs:
fea = torch.cat([fea, checkpoint(rrdb, fea)], dim=1)
return fea
class Generator(nn.Module):
def __init__(self, image_size, latent_dim, initial_stride=1, start_level=3, upsample_levels=2, transfer_mode=False):
super().__init__()
total_levels = upsample_levels + 1 # The first level handles the raw encoder output and doesn't upsample.
self.image_size = image_size
self.scale = 2 ** upsample_levels
self.latent_dim = latent_dim
self.num_layers = total_levels
self.transfer_mode = transfer_mode
filters = [
512, # 4x4
512, # 8x8
512, # 16x16
256, # 32x32
128, # 64x64
64, # 128x128
32, # 256x256
16, # 512x512
8, # 1024x1024
]
# I'm making a guess here that the encoder does not need transfer learning, hence fixed transfer_mode=False. This should be vetted.
self.encoder = StyledSrEncoder(filters[start_level], initial_stride, transfer_mode=False)
in_out_pairs = list(zip(filters[:-1], filters[1:]))
self.blocks = nn.ModuleList([])
for ind in range(start_level, start_level+total_levels):
in_chan, out_chan = in_out_pairs[ind]
not_first = ind != start_level
not_last = ind != (start_level+total_levels-1)
block = GeneratorBlock(
latent_dim,
in_chan,
out_chan,
upsample=not_first,
upsample_rgb=not_last,
transfer_learning_mode=transfer_mode
)
self.blocks.append(block)
def forward(self, lr, styles):
b, c, h, w = lr.shape
if self.transfer_mode:
with torch.no_grad():
x = self.encoder(lr)
else:
x = self.encoder(lr)
styles = styles.transpose(0, 1)
input_noise = torch.rand(b, h * self.scale, w * self.scale, 1).to(lr.device)
if h != x.shape[-2]:
rgb = F.interpolate(lr, size=x.shape[2:], mode="area")
else:
rgb = lr
for style, block in zip(styles, self.blocks):
x, rgb = checkpoint(block, x, rgb, style, input_noise)
return rgb
class StyledSrGenerator(nn.Module):
def __init__(self, image_size, initial_stride=1, latent_dim=512, style_depth=8, lr_mlp=.1, transfer_mode=False):
super().__init__()
# Assume the vectorizer doesnt need transfer_mode=True. Re-evaluate this later.
self.vectorizer = StyleVectorizer(latent_dim, style_depth, lr_mul=lr_mlp, transfer_mode=False)
self.gen = Generator(image_size=image_size, latent_dim=latent_dim, initial_stride=initial_stride, transfer_mode=transfer_mode)
self.l2 = nn.MSELoss()
self.mixed_prob = .9
self._init_weights()
self.transfer_mode = transfer_mode
self.initial_stride = initial_stride
if transfer_mode:
for p in self.parameters():
if not hasattr(p, 'FOR_TRANSFER_LEARNING'):
p.DO_NOT_TRAIN = True
def _init_weights(self):
for m in self.modules():
if type(m) in {TransferConv2d, TransferLinear} and hasattr(m, 'weight'):
nn.init.kaiming_normal_(m.weight, a=0, mode='fan_in', nonlinearity='leaky_relu')
for block in self.gen.blocks:
nn.init.zeros_(block.to_noise1.weight)
nn.init.zeros_(block.to_noise2.weight)
nn.init.zeros_(block.to_noise1.bias)
nn.init.zeros_(block.to_noise2.bias)
def forward(self, x):
b, f, h, w = x.shape
# Synthesize style latents from noise.
style = torch.randn(b*2, self.gen.latent_dim).to(x.device)
if self.transfer_mode:
with torch.no_grad():
w = self.vectorizer(style)
else:
w = self.vectorizer(style)
# Randomly distribute styles across layers
w_styles = w[:,None,:].expand(-1, self.gen.num_layers, -1).clone()
for j in range(b):
cutoff = int(torch.rand(()).numpy() * self.gen.num_layers)
if cutoff == self.gen.num_layers or random() > self.mixed_prob:
w_styles[j] = w_styles[j*2]
else:
w_styles[j, :cutoff] = w_styles[j*2, :cutoff]
w_styles[j, cutoff:] = w_styles[j*2+1, cutoff:]
w_styles = w_styles[:b]
out = self.gen(x, w_styles)
# Compute an L2 loss on the areal interpolation of the generated image back down to LR * initial_stride; used
# for regularization.
out_down = F.interpolate(out, size=(x.shape[-2] // self.initial_stride, x.shape[-1] // self.initial_stride), mode="area")
if self.initial_stride > 1:
x = F.interpolate(x, scale_factor=1/self.initial_stride, mode="area")
l2_reg = self.l2(x, out_down)
return out, l2_reg, w_styles
if __name__ == '__main__':
gen = StyledSrGenerator(128, 2)
out = gen(torch.rand(1,3,64,64))
print([o.shape for o in out])
@register_model
def register_styled_sr(opt_net, opt):
return StyledSrGenerator(128,
initial_stride=opt_get(opt_net, ['initial_stride'], 1),
transfer_mode=opt_get(opt_net, ['transfer_mode'], False))